Biomimetic model of corticostriatal micro-assemblies discovers a neural code

Domain: neuroscience
Published: 2025-12-29
Editorial outcome: major_revision

Abstract
Although computational models have deepened our understanding of neuroscience, it is still highly challenging to link actual low-level physiological activity (spiking, field potentials) and biochemistry (transmitters and receptors) directly with high-level cognitive abilities (decision-making, working memory) and associated disorders. Here, we introduce a mechanistically accurate multi-scale model directly generating simulated physiology from which extended neural and cognitive phenomena emerge. The model produces spiking, fields, phase synchronies, and synaptic change, directly generating working memory, decisions, and categorization. These were then validated on extensive experimental macaque data from which the model received no prior training of any kind. Moreover, the simulation uncovered a previously unknown neural code (“incongruent neurons”) that specifically predicts upcoming erroneous behaviors, also subsequently confirmed in empirical data. The biomimetic model thus directly and predictively links decision and reinforcement signals, of computational interest, with spiking and field codes, of neurobiological importance.

Introduction
How do collections of neurons somehow produce cognition? How do patterns of physiological activity in cells arranged into anatomical wiring layouts, somehow generate perception, learning, memory, decisions, and behaviors? Beyond its critical role in eliciting cognition, the corticostriatal circuit is implicated across a wide range of common yet devastating psychiatric and neurological disorders broadly related to dopamine, reinforcement learning, conditioning, and reward1–6. Thus, having a way to simulate the corticostriatal circuit in a biologically adherent manner matters not only because it would answer some of our most fundamental questions in neuroscience, but also because it would permit in silico testing of candidate mechanisms for disorders and candidate pharmacological interventions targeting those mechanisms.

In recent years, there have been extraordinary breakthroughs in computational neuroscience. However, most of these models have been optimized for either mechanistic accuracy or to elicit emergent cognition, but not both. On the one hand, there are powerful models with biologically-based systems that reproduce extensive physiologies, from single-cell spiking through generation of oscillations and other critical emergent dynamics. Yet, while these can accurately simulate brain-wide phenomena7, such as epileptic seizure genesis and propagation8,9, they are not designed to generate cognitive processes, such as learning. On the other hand, some of the most remarkable cognitive models are those grounded in artificial neural networks and related algorithms10–15. Yet, while this is a class of models that effectively “learn,” they do so without reliance on strictly biologically realistic (biomimetic) mechanisms at the cellular scale. Some influential models optimize for both16–26. These examples illustrate how a set of constraints from not only the machinery of anatomy and physiology but also the emergent trial-level behavior can be incorporated and satisfied into a single unified model across multiple scales. However, it has been an ongoing challenge to construct models that combine biochemical and physiological details with the cognition they produce, which are required to uncover underlying mechanisms of potential clinical importance for biological disorders that present mentally and behaviorally.

Of course, in silico “discovery” of new mechanisms and treatments is possible only if models can apply their learned knowledge to novel or unexpected inputs. A computational model’s ability to make predictions or draw conclusions about data that fall outside the distribution of its training set is referred to as out-of-distribution inference. A standard training approach is to train a model from half the data of a particular kind and then test its ability to predict the other half. A far more rigorous standard of generalizability is to ask whether a model built from one kind of data can predict phenomena of a wholly different kind. In the present work, the model is constructed from the broad literature of cell types and physiology, but it is not parameter-fitted on any particular experimental data. The model is then presented with an experimental task, such as visual stimuli and rewards. Separately, animal subjects have also been exposed to this same experimental task. The model has not been presented with any of the animal’s physiological or behavioral data. Instead, the model is presented with the same visual stimuli as the animals, as well as simulated rewards. In response, the model learns the same task, at about the same rate, as the nonhuman primates (NHPs). In doing so, the model produces extended simulated physiological cell firing and synaptic change activity, across multiple separate anatomically modeled brain regions. This activity, which we term “physiological computation,” is then directly compared with corresponding physiological measures from the NHP experiments.

In sum, we built a physiological computation model of the corticostriatal circuit designed to be mechanistically accurate across multiple scales, from spiking neurons to local field potentials, informed solely by the physiological literature. This circuit elicits category learning, a complex cognitive process encompassing sensory encoding, perceptual categorization, reinforcement learning, action selection, and working memory; these all are described below in detail. We then validated the model using macaque (NHP) electrophysiology and behavioral data27,28. Intending to start with a minimal circuit model that would incorporate the above functions, we started by modeling a cortical region, worked our way through to the striatal complex (MSNs, TANs, SNc), and continued further to complete a corticostriatal loop by including globus pallidus (GPi and GPe) and thalamus. Each component consists of a specific assembly of simulated neurons arranged according to anatomical circuit layouts. Each of these micro-assemblies had specific internal structure and anatomical connections, and specific physiological activity characteristics, each as informed by the extensive experimental literature, and each such micro-assembly was found to contribute a specific neural computation (biomimetic computational primitives (BCPs)). Crucially, these neural computations were not assigned top-down or in advance, but rather emerged bottom-up from observation of the individual circuit simulations. We initially expected the corticostriatal circuit to exhibit only a few specific outputs, such as accurate behavior and spectral properties of neural activity, yet we surprisingly found that the model exhibited far more complex neuronal phenomena than initially intended. Notably, in addition to replicating known physiological patterns and behaviors, the model predicted previously unknown and unexpected neural encodings, that were then confirmed in the experimental data.

Results
We developed a generalizable, tractable, biomimetic, multi-scale brain model that categorizes visual stimuli after a delay. The model can be described as carrying out “physiological computation”: bottom-up mechanistic information-processing operations that arise directly from the physiological steps taken by cells within well-defined anatomical circuitry from distinct brain regions. The biomimetic model was not directly designed for any particular task, but responds physiologically to whatever tasks are presented to it. The initial task for this simulation was a well-studied task from the literature, given both to the simulation and to NHPs (macaques)28 (see Experimental Task design in Methods and Fig. S6a, b). Simple visual dot-pattern stimuli (see Methods) were presented to the simulation and to the NHPs, and then, after a delay, the subjects had to decide which of two “categories” the stimulus had been. For NHPs, the decision was signaled by a saccade to the left for one category (A) or to the right for the other (B); the NHPs then either received a reward (for correct category choice) or forced-delay “punishment” (for incorrect choice). The simulation was presented with the same images and delays and produced a corresponding simple simulated left or right motor output, and then received a simulated reward or punishment as in standard reinforcement learning. Over many trials (in which the image is shown, a choice is made, and the corresponding reward is given), well-studied plasticity rules from the literature are used by the simulation to adjust its synaptic weights (see Methods for details). These adjustments lead to improved outcomes over time, and thus learning.

Details of the brain model at multiple biological and computational scales
The model is multi-scale in that it specifies anatomical structure across the levels of neurons, assemblies, circuit structure, and brain regions, and physiological responses are measured from the levels of individual spikes, synaptic plasticity, local field potentials, sychrony and phase relations across structures. The model simulates electrical activity of individual neurons, connections among groups of neurons within the same brain region ("assemblies”), and anatomically-based long-distance electrochemical connections between regions. The combination of these interactions, the “connectome,” intrinsically generates the synaptic changes that lead to learning.

There are many ways of describing such a complex model. We provide extensive details and equations in the Methods; Fig. 1 shows graphically the hierarchical configuration from individual cortical neurons to the whole brain model; and we describe here the main subcircuits that comprise the model. Subcircuits are themselves composed of smaller assemblies of neurons. Many components of the model are contained in more than one subcircuit, so the subcircuits are often defined by their physiology as well as by the emergent input-output effect of these physiological operations, i.e., their physiological computation.

We describe our model computationally by considering the biomimetic computational primitives that cannot be further reduced. In computer programming, a primitive is a fundamental irreducible language element that performs a simple task (such as adding two numbers). In the model, primitives are realized as assemblies of small numbers of neurons based on specific anatomical layouts of cell types and interactions, and includes several well-studied emergent primitives of physiological computation, such as action selection, reward prediction, central pattern generation, long-term potentiation/reversal, and exploration/exploitation (see Table 1). Some of these are shown in Fig. 1a as other biomimetic computational primitives. The composition of primitives in turn produces further novel computational operations, allowing for complex behaviors similar to how computer programming primitives can be combined (e.g., in a set of instructions) to perform challenging computations.

For the learning task described here, a set of information-processing operations is carried out by a simple, but biologically grounded, small assembly of excitatory and inhibitory cortical cells. This assembly of typically 5-6 neurons is irreducible and can thus be considered to be one functionally primitive element of brain circuitry. The arrangement used here is notably distinct from other approaches that have been used to generate lateral inhibition29–36. It also provides a starting point for investigating how biochemical deviations (e.g., pharmacological blocking or modulation of glutamatergic or GABAergic receptors) could affect both physiological and cognitive processes. The anatomical configuration of this computational primitive is an assembly of excitatory and inhibitory cortical neurons shown in Fig. 1b. These local assemblies of excitatory and inhibitory neurons in simulated superficial cortical layers are anatomically connected such that 1) each glutamatergic input axon (from the simulated visual cortex or any cortico-cortical input) makes contact sparsely with a fixed number of excitatory target dendrites and 2) each excitatory dendrite receives contacts from a separately fixed number of incoming axons37. Furthermore, 3) the excitatory cells densely contact local inhibitory cells (in a ratio of about 5:1), and 4) the inhibitory cells reciprocally make dense contacts with local excitatory cells38–40. Within a local assembly, an activated excitatory neuron will activate the inhibitory neuron, which then inhibits all other excitatory neurons (including the one that activated the inhibition). Thus a very limited subset of target excitatory cells can successfully respond to an input; this “Local-Feedback Lateral Inhibition Circuit” (L-FLIC) primitive bears some correspondence with well-studied neuronal activity patterns of specific kinds of lateral inhibition competitive circuitry. As a computational primitive, this feedforward excitation - feedback inhibition circuitry has been described in terms of several nonlinear computational functions, including non-linear selection and “soft winner-take-all”; signal restoration; and bistability and multi-stability41. More specifically, lateral inhibition arising from anatomical relations among excitatory pyramidal and local feedback inhibitory cells is conserved across superficial cortical regions42,43, whereas physiological feedback inhibition from multiple subtypes of interneurons is well also studied44,45. Multiple such assemblies are present in the cortical structures (visual regions and anterior cortex) in the model, and they are sparsely interconnected with each other, as briefly illustrated in Fig. 1c, and discussed in Methods.

Thus, the local-circuit L-FLIC biology for superficial cortex directly follows a number of known anatomical and physiological details from the literature. (i) There are 4–5 times more excitatory cells than inhibitory cells. (ii) The inhibitory cells roughly tile the span of excitatory cells. (iii) The radial axonal arborization of local feedback inhibitory basket cells is relatively short, spanning a small set of closely neighboring excitatory cells. (iv) Within this abbreviated neighborhood, excitatory connections to inhibitory cells and inhibitory connections to excitatory cells both are dense. (v) The spiking threshold of the inhibitory cells is far lower than for excitatory cells (inhibitory cells are far easier to activate). (vi) The activation of any subset of excitatory cells (e.g., 1–2 cells) will therefore rapidly activate the inhibitory cells, and the inhibitory cells will then rapidly thereafter spike and inhibit the local excitatory cells (including those cells that activated the inhibitory cell). Thus, after activating any excitatory cells, the feedback inhibitory cell will respond and inhibit them. So once some excitatory cells within this local region have been activated, they will be rapidly silenced, and those excitatory cells will thereby be the only cells to survive the feedback lateral inhibition. It is this physiological cascade of activity that is simulated via the L-FLIC primitive. In a separate ongoing work we are considering simpler circuits to determine the criticality of this particular primitive.

The evolution of neuronal activity in these assemblies starts with an incoming set of spikes from a simple “visual” cortex region (SVCR). The stimulus (dots on the screen) activates these neurons, with individual connections corresponding to specific pixels on the screen. These neurons induce depolarization via input axons that contact dendritic synapses of neurons in the simulated anterior cortical area (ANTC) as illustrated in Fig. S2. The total voltage over time (illustrated by voltage-time graphs in Fig. 1b) rises at a rate that is dependent on the quantity of input activation at a given target cell (e.g., the brightness of that pixel) and the weights of the receiving synapses. The active synapses of the responding cells will then be increased slightly in weight as determined by the rules from the literature for glutamatergic synaptic plasticity (see Methods). These synaptic increases makes it even more likely that slight variants of an input will activate the same target excitatory cells. As a result, similar inputs will increasingly come to elicit identical, not just similar, cortical responses (Figs. S6–S7). It should be noted that the overall behavioral attainment of category learning uses the L-FLIC circuits and trial-based synaptic changes, in concert with additional physiological elements that include both cyclic striatal-thalamic-cortical acitivity, and synaptic short-term potentiation during a delay period, as well as interaction among striatal components, and reinforcement-related dopamine-modulated striatal synaptic change; these and further components are embedded throughout the corticostriatal circuit system (Fig. 1c).

At a larger scale, the simulation incorporates well-studied physiological properties of neurons into three subcircuits (Fig. 1c). The Cortex Subcircuit, comprising the aforementioned Simple Visual Cortical Region (SVCR) and Anterior Cortex (ANTC), is composed of blocks of lateral inhibition assemblies that receive input from an external visual grid input ("Stimulus” in Fig. 1c) with some connections among assemblies. This subcircuit also receives ascending rhythmic inputs (ASC1 in Fig. 1c) modeled as next-generation neural mass models46,47 as described in Methods. The Striatal Subcircuit (which also could be termed the “Basal Ganglia” subcircuit) consists of matrix (matrisome) assemblies, patch (striosome) assemblies, substantia nigra pars compacta (SNc), and tonically active (TAN) neurons. Matrisome and striosome blocks are composed of medium spiny neurons (MSNs) receiving convergent inputs from the cortex (via glutamate), from the Tonically Active Neuron (TAN) structure (via acetylcholine), and from the SNc (via dopamine) while outputting to the pallidum and thalamus. The TAN cells receive inputs from intralaminar thalamic nuclei48 and GABAergic striosomal MSNs. TAN projection synapses co-localize with corticostriatal synapses. It should be noted that neurons in both the Striatal and TAN Subcircuits are not modeled as spiking neuralelements but uses simplified physiologically-derived “rules” whose equations define their input/output behavior (see Methods). This simplification from assembly of multi-compartmental neuron models to input/output elements is supported by a large body of experimental49–53 and modeling54–56 studies. The Brainstem Subcircuit is a highly simplified ascending system (ASC1 in Fig. 1c). This represents a simulated ascending system (such as locus coeruleus or basal forebrain) that generates oscillatory input, centered around 16 Hz. The resulting rhythmic activity provides input to the ANTC beyond the SVCR input generated by the stimulus. It is modeled using next-generation mean-field neural mass models46,47 and descending signals are not as yet considered. Details on these subcircuits are provided in the Methods and Supplementary Material.

The interconnections among these subcircuits support the process of learning. As the model experiences many trials, there are changes to synaptic strength for cortico-cortical, cortico-striatal, and thalamo-cortical contacts that cause changes in behavior. For example, initial trials have significant cholinergic noise from the TAN Subcircuit that increases variance in target matrix MSN responses, which we hypothesize adds exploratory variability to striatal action selection [see ref. 57]. As learning progresses, corticostriatal synapses to striosomes are strengthened, which increases their ability to inhibit TANs, which decreases variability as associations are effectively learned in the corticostriatal system. Thus, the TAN subcircuit is a crucial part of toggling from “exploration” to “exploitation” (see Fig. S3).

The model succeeded in categorical learning at a rate similar to NHPs while replicating cortico-striatal synchrony effects
The simulation is intended to characterize phenomena that currently cut across multiple levels of description. For example, in real brains, spiking neurons give rise to waves, yet waves in turn strongly modulate spiking; and both spiking and waves appear affected by disorders and by drugs [e.g., refs. 58–61]. We provide initial evidence that the simulation may assist in uncovering the mechanisms of these cross-level relations. Preliminary validation of the model came from the observation that the simulated neuronal activity and interaction patterns were similar to NHP brains as they perform similar tasks (see Supplementary Figs. S4 and S5). In particular, the post-stimulus-onset cortical silence observed in the simulation, was also seen in the empirical data (Supplementary Fig. S5). This exemplifies the detailed physiological features produced by the simulation that correspond directly to those that also occur in real neural activity (despite the lack of any training of the simulation on any such data). Although the simulation carries out solely physiological operations, in doing so, it successfully learns the visual categorization task that it is presented with. That is, via its own synaptic changes, it acquires the category characteristics (i.e., discriminating category A from B) and it learns (via its own simulated striatal activity) to produce simple appropriate behaviors (a simulated “saccade” left or right).

The behavioral responses increase in accuracy across trials (Fig. 2a) both in the simulation (top) and in the experimental animals (bottom), and at comparable rates. The model achieves 80% performance accuracy consistently between 100 to 400 trials across the sessions, and once that 80% level is attained, it is maintained throughout the remainder of the trials in the session. Cortico-cortical synapses exhibit long-term increases in weight (Fig. 2b). By contrast, striatal synapses exhibit increases and decreases as per the combined effect of cortical glutamatergic, SNc dopaminergic, and TAN cholinergic inputs that converge at each spine of striatal medium spiny neurons (see Methods and Fig. 1). Thus, cortical synapses are seen to slowly strengthen, while corticostriatal synapses strengthen and weaken over trials.

The processes of learning and of holding information in “working memory” depends on interconnections among all four primary (cortical, striatal, TAN, and brainstem) subcircuits. Ongoing recurrent activity, via feedback from striatum to thalamus to cortex to striatum, crucially drives the ongoing memory; this is concordant with the recorded data from the NHPs.

Investigating neuronal activity differences between early and late trials (Fig. 2c) shows that spiking is much stronger in late trials for both simulated and empirical systems, in both the cortex and striatum, and both during and after stimulus presentation. Empirical observations of spiking in NHP brains are quantified using the local field potential (LFP)28. For the simulation, we developed a far simpler metric called local spike summation (LSS), defined in Methods. This is not meant to simulate LFPs, but rather to identify first the simple summed activity in local simulated cells (via the LSS measure), and by comparing with empirical LFPs, to thereby isolate possible LFP components beyond the simple LSS summations. Notably, the simulated LSS decreases in spiking substantially after stimulus offset whereas empirical LFP activity does not decrease by a corresponding amount (Fig. 2c). This is somewhat expected since real PFC neurons are subjected to substantial input from multiple cortical and subcortical regions that are not present in the simulation, and thus PFC cells are only partly driven by visual stimulus inputs. By contrast, in the simulation, there are no extraneous inputs and no artificially injected noise, so it is expected that the empirical and model records will substantially differ. These differences may well have importance, and further study of these is in progress. Surprisingly it was found that, despite these differences, there were notable correspondences between the inter-area cortico-striatal synchronies, and changes to these synchronies, that occurred both in the model and the macaques. The synchrony between cortical and striatal activity is calculated using the phase locking value (PLV) from a wavelet analysis of LSS or LFP timeseries (see Methods and Supplementary Material) as was done in 28. PLV is measured in multiple frequency bands including the near 16 Hz (beta-frequency) band driven by the ASC1 in the simulations. Figure 2d shows that both simulated and empirical data showed increased synchrony in the beta-frequency band in late trials versus early trials, though this effect was much stronger in the simulation, in particular during the delay epoch.

The activity during the delay epoch represents a “working memory" or “holding memory", in which there is no visual stimulus and the NHP must wait before saccading to indicate the stimulus category (A or B). This requires that the category information must be maintained by the NHP even though it can no longer be viewed. During this delay epoch, increased corticostriatal synchrony is seen in empirical NHP records (Fig. 2d, bottom right). A markedly similar change to synchronous activity is found in the simulation (Fig. 2d, top right), enabling study of what causes this in the simulation, and thus hypotheses of corresponding causes in the macaque brain. In the simulation, looping spiking activity from cortex to striatum to pallidum to thalamus back to the cortex is strongly present, and this is found to directly cause maintenance of the spatiotemporal firing pattern in the simulation even after the stimulus is off. This is explained in detail in Methods, Detailed model description.

Further opportunities for causal mechanistic understanding are afforded by studying activity during correct versus incorrect trials. In the NHPs it is seen that synchrony in the beta frequency band is substantially increased only in incorrect trials. The difference between correct and incorrect trials is shown in terms of the PLV measure in Fig. 3. Both simulated and empirical results show that slightly higher synchrony during early incorrect trials is reversed with learning, so that later trials show stronger synchrony in the beta frequency band only on correct trials (Fig. 3a). The simulation again provides a candidate explanation: the correct trials cause higher cortico-striatal weights (Fig. 3b); these weights are initially similar for synapses on both “correct” and “incorrect” neurons, until about 450 trials (see Fig. 3b, and compare with Fig. 2b bottom panel). This is the point in the trials that may be said to behaviorally transition from “exploration” to “exploitation”; the synaptic strengths then markedly diverge, with correct targets becoming more strongly weighted. These weights for correct vs incorrect targets are also reflected in the rate of neuron spiking, which is much lower and more localized in both cortex and striatum during incorrect trials. This occurs both in the empirical and simulated findings, as can be seen in Fig. 3c.

To further study the influence of particular individual circuit components in the simulation, we also performed an initial set of simulated lesion experiments. We disabled three key elements one at a time: SNc, striosomal cells, and TANs. All three lesions interfered with the learning behavior, but via clearly separate mechanisms (see Supplementary Figs. S12–S14), giving rise to testable predictions and insights about detailed circuit operation.

In sum, not only does the simulation produce behavioral category learning commensurate with that seen in NHPs, the simulation also exhibits explicit and detailed physiological spatio-temporal spiking and field responses, from both cortical and striatal fields, which can be compared directly against empirical physiological data. The simulation shows a range of complex behaviors seen in the NHPs, including different synchrony and phase locking that is specific to the particular behavior that is being engaged (Figs. 2d and 3a). These same cortico-striatal activity patterns are shown to change over time as a response to synaptic plasticity—two distinct forms of plasticity for cortex vs. striatum (Fig. 2b)—and these changes in the model are compared directly against empirical changes in NHPs (Fig. 2c).

The model has predictive power: the discovery of incongruent neurons
An effective model of the brain will provide significant new insights into neuroscientific questions. Here we show that our model has specific predictive power as illustrated by the discovery of “incongruent neurons” in the simulation. Although we report in parallel the discovery in simulated and empirical data, we emphasize that the simulation results prompted the successful identification of a previously-missed behavior in the long-studied empirical data.

Analysis of the simulation showed surprising predictive behavior in some neurons. After extended learning trials in the category task, the model reached a phase where every simulated excitatory anterior cortical neuron had acquired a characteristic preference to fire either for stimulus category A or B. Unexpectedly, every such neuron also turned out to have a specific response to the outcome of the trial: correct vs. incorrect. These neuronal responses were predictive codes: they fired at the start of a trial, even though the specific outcomes that they predicted would not occur for more than a thousand more milliseconds in the future.

Specifically, all simulated neurons were found to show strongly selective responses to one of four conditions: A-correct, A-incorrect, B-correct, and B-incorrect. Here, the prefix (A or B) refers to the category of stimulus presented in a given trial, and the suffix (correct or incorrect) refers to the outcome of the trial after the subject responds. Thus, for instance, A-incorrect means that an A-type stimulus was presented and the subject responded incorrectly (responded in this case as though a ‘B’ had occurred). These selective responses to one of the four conditions occurred during the initial 200ms of stimulus (see Figs. S4, S5, and S8), long before the erroneous response would be performed. It is worth emphasizing that this is a strictly emergent phenomenon from the simulation’s internal synaptic learning process: these neurons are not (and biologically cannot be) pre-categorized in advance to separately target these four trial types. An initial expectation might be that a system that had successfully learned, over trials, to distinguish between the different types of stimuli, would have optimized its performance by having practically all neurons supporting correct choices, i.e., enabling the organism to produce the correct response leading to a reward. By contrast, any neurons that indicate an upcoming incorrect choice (A/B incorrect neurons) we thus termed these “incongruent neurons” (also corresponding to “incorrect synapses" in Fig. 3b). These incongruent neurons (ICNs) learned to predict an upcoming behavioral error. Comprising about 20% of all neurons, ICNs sufficiently activate target striatal action-selection cells for the other category, thus causing the wrong behavioral response.

To explore these in greater detail and to determine whether or not such incongruent neurons also occur in empirical NHP data, we use a simple method to identify and characterize incongruent neurons. 1) Trials are sorted into the four “input-outcome” classes (A-correct, A-incorrect, B-correct, B-incorrect). 2) Spiking rates of each excitatory anterior cortical neuron during the first 200ms of a given trial were normalized by the average number of spikes across the population for that trial-type. This then allows for 3) assignment of a neuron to one of the four classes based on the highest spiking rate. This simple sorting by their predicted decisions leads to neurons strongly (and often uniquely) correlated with each of the four classes, as shown in Fig. 4a.

Though the implanted electrodes in the macaque NHPs acquire recordings of only very few neurons, Fig. 4a does indicate that the empirically recorded neurons appear also to exhibit predictiveness of specific eventual behavioral outcomes, in direct concordance with the ICN neurons found in the simulation.

We confirmed that ICNs were self-consistent and persistent in both simulated and empirical data with strong statistical significance. To ascertain statistical validity, we divided the late trials in half, and used the first half for determination of purported ICNs, which then were tested by being applied to the separate second half of late trials. Figure 4b shows that ICNs predict behavioral outcomes significantly better than randomly chosen neurons (p < 0.001 for empirical data and p < 10−21 for simulated data).

We then applied an even more stringent threshold for validation of ICNs: could these neurons predict outcomes, in real-time, within individual trials? If a hypothetical device were implanted into the NHPs and trained on initial trials, it could be tested for its ability to improve behavior, by halting any trial in real-time whenever incongruent neurons predicted an upcoming erroneous response. Figure 4d shows a sample trial-by-trial run where such an ICN-based intervention significantly (p < 0.005) increases behavioral accuracy (Fig. 4e) of the simulation.

Finally, we identified specific aspects of the simulation that show how ICNs are mechanistically produced. Supplementary Fig. S10 begins with sparse synaptic cortical connectivity and maps those connections backward, through multiple cortical areas, superimposing them directly back onto the visual input stimuli. The sparse combination of synapses that eventually co-occur at downstream cortical cells, defines circumscribed visual receptive fields that any given cortical neuron can “see.” Analyses (in Supplementary Fig. S10b–d) show that cortical cells are thus anatomically “pre-weighted” to probabilistically prefer either category A or category B stimuli. When these anatomical biases are sufficiently strong, they reliably predict eventual learned anterior cortical category A vs B cells, whereas less-biased, or more-ambiguous cells, become candidate incongruent neurons (ICNs). These detailed analysis of anatomical bias and synaptic modification are further detailed in Supplementary Material section IV. In addition to the anatomical bias there is another mechanistic aspect to the emergence of ICNs. Even though both the congruent (A/B correct) and incongruent (A/B incorrect) neurons are selective to their corresponding stimulus type, the difference lies in their downstream connections to the striatum. Thus, ICNs in the cortex tend to form stronger synapses with the striatal region that map to the motor response for the complementary category. Congruent neurons ‘learn’ stronger synapses with the striatal region corresponding to ‘correct’ motor response. This is the expected outcome of reinforcement learning. On the other hand, incongruent neurons ‘learn’ stronger synapses with the ‘incorrect’ striatal region. This outcome is not expected typically from reinforcement learning. This phenomena needs a deeper study on ICNs.

These results statistically validate that ICNs are a real feature of both simulated and, surprisingly, NHP brains. Thus, the corticostriatal circuit simulation demonstrates its ability to satisfy the strictest bar of any model: predicting a previously unknown phenomena, subsequently empirically confirmed, that are quantitatively and qualitatively distinct from any information used in building the model.

Methods
We describe the corticostriatal circuit from mechanistic to emergent scales. We begin with a detailed model description at the neuronal and assembly levels. We then provide details on how corticostriatal decision-making proceeds from synaptic plasticity, e.g., how the neurons are continually modified to proceed with learning. We then provide details for our computational methods, the experimental task design, the simulation protocol, and how we analyze the model’s physiological response. See the Supplementary Material Methods section for additional information regarding the model’s construction, parameter values, and references.

Detailed model description
Extensive anatomical and physiological features incorporated into the present simulation. A sizeable body of references was used in the conception and construction of the several subcircuits and neural assemblies that constitute the present brain circuit simulation (Table 1, Fig. 1d); these consist of attributes that are highly cited in the literature. For list of key publications for each computational primitive, see Table S1 in Supplementary Material.

Spiking neurons and synaptic connections
Neuronal Dynamics. Each neuron is modeled using ionic conductance based point neuron models following the Hodgkin–Huxley formalism89. For simplicity, in this model we consider only voltage gated Sodium and Potassium channels. The membrane potential is described by:

dV
Cm = −gL(V−VL)−INa−IK+Iin+Isyn (1)
dt

where Iin is the input current, which can either be external stimulus or background activity, and Isyn is the synaptic input. Sodium current INa = gNa m3h(V−VNa) and potassium current IK = gK n4(V−VK) are driven by gating variables {m, h, n} whose dynamics are governed by the general form:

dχ
=ϕχ(αχ(V)(1−χ)+βχ(V)χ) (2)
dt

Here, χ is the gating variable and ϕχ is the temperature related effect on the time scale. See Methods section in Supplementary Material for parameter values.

Synapse. Synaptic current in Eq. (1) for a neuron Pi is the sum of synaptic inputs from all incoming axons {j} viz. Iisyn = Σj Iijsyn. Synaptic current from neuron j to neuron i is given by

Iijsyn = Wij · gjsyn(t) (Vi−Vjsyn) (3)

where Wij is the synaptic weight from neuron j to i, a quantity which estimates density of dendritic spines and is modified by Long Term Potentiation (LTP) and Long Term Depression (LTD, see Eq. (28), (30)). The synaptic conductance gjsyn is a function of spiking activity of the presynaptic neuron, and therefore is one of the state variables for each neuron whose dynamics is described by damped oscillator equation:

dz
= −1z+g1(V) (4)
dt τ1

dgsyn = −1gsyn +z (5)
dt τ2

which is driven by impulse force g∞(V) defined as follows90

g1(V) = syn 1+e−(V−Veq)/Vrange (6)

See Methods section in Supplementary Material for parameter values.

Brain regions modeled as cell assemblies. The schematic of the overall circuit is shown in Fig. 1. Most of the brain areas shown in the circuit have been modeled as cell assemblies of sparsely connected spiking neurons, where each region has its own specific internal structure and neuronal types comprising them. These structures and neuronal types are derived from the well known anatomical and physiological literature. Below are the specific structures these brain areas modeled here.

Cortical areas. Superficial layers of cortical regions in this model (cortical blocks) comprise large number of local feedback lateral-inhibition circuits (L-FLIC). Each L-FLIC circuit (see Fig. 1a), comprises five excitatory neurons sending synapses to one feedback inhibitory neuron while the inhibitory neuron sends synapses back to the 5 excitatory neurons. The L-FLIC circuits are connected to each other through synapses between the excitatory neurons. We have set the connection density such that each excitatory neuron is connected to 1 excitatory neuron from the same cortical block. Within each cortical block, we have 1 feed-forward inhibitory neuron which recieves input from excitatory neurons of other cortical regions and sends inhibitory synapses to excitatory neurons of the same cortical block. These feed-forward neurons also receive modulatory inputs from various Brainstem nuclei forming the Ascending Reticular Activating System91. The rhythmic bursting of feed-forward inhibitory neurons causes the rhythmic bursting of the excitatory neurons and which in turn sets the dominant rhythm in the cortical region.

There are two cortical blocks in this simplified model: the Simple Visual Cortical Region (SVCR) and the Anterior Cortex (AC). The SVCR consists of 45 L-FLIC circuits comprising (45×5=) 225 excitatory ‘target’ neurons which receive the stimulus dot patterns in the form of input current Iin. The visual stimulus consists of (15×15=) 225 pixels (see Fig. S6) which are mapped directly onto the 225 visual area neurons, where the Iin for each neuron is proportional to grayscale value of the corresponding pixel.

The anterior cortex (AC) is comprised of 20 L-FLIC circuits, hence (20×5=) 100 target excitatory cells. The connections between the SVCR and the AC are such that every neuron has equal number of outgoing and incoming synaptic connections; the resulting hypergeometric connectome37 ensure the absence of “hot” or “cold” neurons, i.e., cells differently capable of activity due solely to their afferent wiring. Such connectivity is supported by empirical evidence92–94. Every SVCR neuron makes an outgoing synapse with 8 out of 100 AC neurons, while each AC neuron receives incoming synapses from 18 out of 225 SVCR neurons, thus the connection probability for cortico-cortical connection is 8%95.

It may be useful to also note that the particular cortical inhibitory feedback mechanism (L-FLIC) is inherently capable of learning multiple categories, not just two as done in this task. The number of categories is partially limited by the number of superficial-layer cells within a cortical cell assembly (containing multiple L-FLIC circuit assemblies), which number in the hundreds of thousands. In this lateral inhibition mechanism as described earlier in the text, the subset of cells that are active are the strongest responders to the specific pattern of activity across input axons for a given stimulus. The strongest responders for a given stimulus category will selectively respond to the axon activation patterns that are most similar to each other. Thus we emphasize that the L-FLIC circuitry is not at all limiting the number of categories to which the model can respond and encode.

Striatum. For the striatal-pallidal projection system, we model two blocks (’L’ and ’R’, say) for each of the downstream brain regions in the loop, viz. Striatum, GPi, GPe, STN, and Thalamus. Each block in one region connects with the corresponding block in the other region viz. L(R) block of one region connects to L(R) block of another region, thus representing the topographic connections. These two streams of connection correspond to the two choices of response actions (in this case left or right saccade).

The Striatum has three components: Matrisomes (matrix) and Striosomes (patch) medium spiny neurons (MSNs) that are GABAergic, and the tonically active neurons (TANs) that are cholinergic. These three form a striatal complex57, which is supposed to play a vital role in reward-based action selection96. We modeled these components along with SNc as simplified physiologically-derived “rules” defining their input/output behavior (see following subsection, where Eq. (11), (14), (16), (18)). Modeling MSNs, TANs and SNc as input/output elements is well supported by extensive studies dendritic arbors49, relating synaptic input characteristics and output spiking characteristics56. Multiple physiological mechanisms have been proposed to incorporate active dendrites and ion channel dynamics into input/output computational units50–54. Furthermore, we also modeled two blocks of matrisome as spiking neuronal assemblies (L-STR and R-STR) to capture the temporal and spectral properties of the striatal data from the experiments. We can understand the spiking neuronal assemblies as a subset within the larger population of MSNs which are modeled as input/output blocks. We also model a single TAN spiking neuron which provides modulatory input to MSNs.

Each matrisomal block consists of single layer of 25 inhibitory neurons, where each neuron gets incoming synapses from 4 AC neurons. These neurons also get sub-threshold excitation from a TAN neuron. Since it was experimentally observed that TAN neurons are phase-locked with the striatal LFPs97,98, we propose that the striatal rhythm is modulated by rhythmic bursting of TANs. TAN neurons in turn get the rhythmic input from Intralaminar Thalamic Nuclei.

GPe. Two blocks of single-layer assemblies of inhibitory neurons, 15 neurons in each block. Each neuron is connected to 1 striatal neuron of the corresponding block in striatum (topographic connections). Each GPe neuron is connected to 1 GPe neuron of corresponding block.

STN. Two blocks of single-layer assemblies of excitatory neurons, 15 neurons in each block. Each neuron is connected to 1 GPe neuron of corresponding block in GPe (topographic connections). Each STN neuron is connected to 1 GPe neuron of corresponding block.

GPi. Two blocks of single-layer assemblies of inhibitory neurons, 25 neurons in each block. Each neuron is connected to 1 striatal neuron of corresponding block.

Thalamus. Two blocks of single-layer assemblies of excitatory neurons, 25 neurons in each block. Each neuron is connected to 1 GPi neuron of the corresponding block. Thalamic neurons project back to AC target neurons thus completing the loop (see Fig. 1). Each AC neuron gets input from 16 thalamic neurons i.e., eight neurons from each thalamic block.

Ascending systems and intralaminar thalamic nuclei. The simulated ascending system (ASC1) is an extreme simplification of ascending systems such as the cholinergic basal forebrain and noradrenergic locus coeruleus; it produces rhythmic activity via a neural mass model. That activity becomes simulated modulatory input to cortex, driving a beta rhythm in the cortex99. The ASC1 simulation thus functions as a central pattern generator. It has been modeled using a simple ‘Next Generation’ neural mass model46,47 which is a mean field approximation of a network of rhythmic neurons. A single neural mass representing an ascending system nucleus comprises a synaptically coupled population of excitatory (E) and inhibitory (I) neurons. Their collective activity is defined by the complex valued Kuramoto order parameter Ze and Zi respectively, while their synaptic interaction is measured by synaptic conductivities gee, gei, gie and gii such that gab is the synaptic conductivity from population b to population a, where a, b ∈ {E, I}. The dynamics is described as:

dZa
τa = F(Za; η0a, Δa) + G(Za, gab; vsynb) (7)
dt

dgab
α−1 = κ f(Zb) − gab (8)
dt

F(Z; η0, Δ) = (Z−1)2 − i + 1/2(Δ+iη0) (9)

G(Z, g; vsyn) = i vsyn g − g (10)

and the population firing rate f(Ze) of population E is given by

1 1−Z*e
f(Ze) = Re (11)
πτ 1+Z*e

The firing rate f(Ze) of population E directly affects the modulation of the target neurons, in the form of an input current to the target neurons such that Imod = k f(Ze). In this simulation, those targets are simulated somatostatin feed-forward inhibitory cortical neurons. See Methods section in Supplementary Material for parameter values.

Corticostriatal decision making and synaptic plasticity. The spatio-temporal spiking patterns in the above described cortico-striatal-thalamic-cortico (CSTC) circuit have been simulated by modeling the interconnected cortical and subcortical regions as synaptic networks of neuronal assemblies, each comprising spiking neurons of Hodgkin–Huxley type (as described above). Now we describe the set of mechanisms through which these spatio-temporal firing patterns generate the learning driven behavior (response choice) and feedback driven synaptic plasticity (and thus category learning). Although these equations describe our proposed mechanisms for corticostriatal computation at a much coarser scale of length and time compared to the physiological scales of spiking neurons and neuroreceptors, they are nevertheless based on well-known physiological and anatomical principles found across the literature.

In principle, all of the following mechanisms can be entirely modeled using only the physiological phenomena at the neuronal length and time scales. However, in this initial rendition of the model we have implemented reasonably simplified population level abstractions of these well defined physiological mechanisms and anatomical constraints. The model is entirely summarized in Table 2. Following is the detailed description of the model components.

Population activity in STR and SNc. There are four key sub-populations of subcortical regions: matrisome (matrix), striosome (patch) and TANs in the striatum and dopaminergic neurons of substantia nigra pars compacta (SNc). We consider one sub-population each for TANs and SNc and two competing sub-populations each for matrisome and striosome, respectively part of the two corticostriatal loops described in the circuit model. Since matrix and patch neurons are GABAergic neurons with relatively longer time scales (~80ms), their firings have a cumulative effect over a given time interval. Hence for the striosome and matrisome we consider their cumulative spiking activities ρstr and ρmat, respectively. For dopaminergic SNc neurons and cholinergic TANs on the other hand, we consider their instantaneous firing rates RTAN and RDA respectively.

Note that substantia nigra pars reticulata (SNr) is intentionally absent for this initial model. The SNr is predominantly an inhibitory output stage of the striatal complex, as are the pallidal structures (GPe, GPi), and it is possible that there may be some relationships among their functions, as well as some possible non-overlapping functions100. In both cases, lesions to SNr and to pallidal outputs tend to lead to change in spontaneous locomotion, abnormal limb movements, and difficulties with skilled motor tasks101. These lesion-based changes tend to be in opposite directions (SNr lesions tend to increase motor activity; GP lesions tend to impair activity), as would be expected by the anatomical circuitry: GPi inhibits SNr, which in turn is an inhibitory output stage projecting to thalamus and to brainstem102.

Striosome activity. Striosome ("patch”) sub-population is driven by spatio-temporal spiking input from cortex, AC in this case, and is laterally inhibited by matrisomes ("matrix”) of the competing sub-population. The cumulative spiking activity ρpat in the striosome sub-population x at time t over interval Δt is given by

ρpat(t, Δt) = PAT Σi∈Ctx Wi(t) · ⟨Si⟩t−Δt − LI Σy≠x ρmat(t, Δt) (12)

where, PAT is a generic function with cortical neuronal activity as input and striosomal ("patch") activity as output. Here cortical input is represented by ⟨Si⟩t−Δt that is the number of spikes occurring in AC neuron i during interval [t−Δt, t] and Wi(t) is synaptic weight for the synapse(s) from AC neuron i to striatal sub-population x (including both patch and matrix). The lateral inhibition function LI represents the inhibitory inputs coming from all other areas of the striatum.

For the sake of simplicity, in this paper we used discrete time maps for population activity instead of above described continuous dynamics: t ⇒ tn where tn = t0 + n·Δt. Here t0 is the time of stimulus onset and interval Δt is the time duration of GABAergic inhibition. The discretized form for cumulative striosomal activity (Eq. (11)) is given by

ρpatx(tn) = Hx(tn) Σi∈AC tn−1 Ai x(tn) · Si tn jx ∈ {1, 2}, n ∈ {1, 2, 3...} (13)

and the simplified form for used for lateral inhibition between two striatal regions modeled here is given by

Hx(tn) = Θ(ρmatx(tn−1) − ρmaty(tn−1)) j n > 2 (14a)
1 (x, y) ∈ {(1, 2), (2, 1)} n ≤ 2 (14b)

where Θ is the Heaviside function and ρmatx is the cumulative spiking activity of matriosome ("matrix") subpopulation x (see below).

TAN activity. TANs are driven by intralaminar regions of the thalamus, and are inhibited by all striosomal sub-populations. The firing rate RTAN can be generally described by:

RTAN(t) = Inhibition Σx ρpatx(t, Δt) | κTAN (15)

where, κTAN is the baseline activity of TAN neurons and Inhibiton(ρ|κ) is a monotonically decreasing function of the cumulative spiking activity ρ of the source population, given the baseline spiking activity of the target population is κ. The specific discrete form for TAN activity (Eq. (14)) we used here is given by

RTAN(tn) = κTAN, n = 1 (16a)
Σx κTAN / ρpatx(tn−1), n > 1 (16b)

Matrisome activity. Matrisome sub-populations receive similar inputs as corresponding striosome sub-populations, with an additional modulatory input from TANs. Hence the cumulative spiking activity in the matrisome (matrix) sub-population x over interval Δt at time t is given by

ρmatx(t, Δt) = MAT Σi∈AC Ai x(t) · ⟨Si⟩t−Δt + TAN(RTAN) − LI Σy≠x ρmaty(t, Δt) (17)

where, MAT is a generic function like PAT but for the matrisome, with cortical neuronal activity as input and matrisomal (patch) activity as output and TAN is the contribution to matrisomal activity from ACh modulatory inputs from the TAN neurons. The discrete form of Eq. (16) used in this work is as follows

ρmatx(tn) = Hx(tn) Σi∈AC tn−1 Ai x(tn) · Si tn + Pois(RTAN(tn)) jx ∈ {1, 2} (18)

where Pois(λ) generates a random value from Poisson distribution with mean λ.

Note that we have not yet included many of the forms of inhibition that occur in these structures, including presynaptic inhibition among striatal neurons103. The existing lateral inhibition between segments of the striatal complex (Eq. (11), (16)) may be producing some of the effects that otherwise would arise from presynaptic inhibition; this is not known and will be studied as these features are incorporated in the new versions of the model.

Dopaminergic SNc activity. DA neurons in the SNc, like TANs, are inhibited by GABAergic input from striosomal sub-populations. Their activity can be generally described by

RDA(t) = Inhibition Σx ρpatx(t, Δt) | κDA (19)

where, κDA is the baseline activity of DA neurons in SNc.

The specific discretized form for Eq. (18) used in this work is given by

RDA(tn) = κDA, n = 1 (20a)
Σx κDA / ρpatx(tn−1), n > 1 (20b)

Action selection and reward feedback. Due to lateral inhibition between striatal sub-populations, one sub-population tends to dominate while others become suppressed. Thus, during a single trial with a given stimulus, only one of the striatal sub-populations consistently remains active, which then activates specific downstream circuits corresponding to a specific response. Thus the response action A is chosen at the response time tr by

A = action x | ρmatx(tr) = max{ρmat1(tr), ρmat2(tr)...} (21)

The specific form of selected action is

A = left saccade j ρmat1(tr) > 0 (22a)
right saccade j ρmat2(tr) > 0 (22b)

After the response, a simplified feedback F is provided in the form of reward or punishment depending on the correctness of choice

F(A) = reward j A: correct (23a)
punishment = no reward j A: incorrect (23b)

and for this specific case, reward is determined by

F(A) = 0 1 j A: correct (24a)
0 j A: incorrect (24b)

where H determines the state of activity in striatal block x (see Eq. 13b) and Si determines the activity in cortical neuron i. In this simplified form, the activity of the postsynaptic striatal neurons is summarized in the activity state H, since the striatal MSN populations are modeled as input/output blocks (Eq. (11), (16)).

DA release at corticostriatal synapses. The cumulative amount of DA released at the corticostriatal synapses by SNc neurons over the course of a single trial, i.e. from stimulus onset until feedback reward, is given by

DA_SNc = ∫tf t0 RDA(t)dt + DAfb(F(A)) (25)

where, t0 is stimulus onset time, tf is the time of reward feedback and DAfb(F) is the amount of DA released due to reward feedback F which is a function of response action A.

The specific discretized form of Eq. (24) used for this work is as follows

DA_SNc = Σn RDA(tn) + DAfb(F) (26)

where, DA + RDA(tn) − κDA + 1/2 DAfb(F) is the baseline DA release in the absence of any striosomal inhibition of SNc.

The strongest suppression of DA neurons from baseline activity as a result of striosomal inhibition occurs at time t2, since for t > t2 the lateral inhibition within STR sub-populations suppresses most of the striosomal activity, bringing SNc activity back to baseline. Thus the approximation in Eq. (25) is justified. In this specific case, DA released as a result of feedback is given by:

DAfb(F) = θrew j F(A) = 1 (27a)
0 j F(A) = 0 (27b)

Since the total amount of DA released during a trial directly affects the corticostriatal synaptic plasticity, and thus affects learning, it can also be expressed in a traditional reinforcement learning framework in terms of value prediction error:

DA_SNc = DA + ϵ (28)

where, error prediction ϵ = R − Q such that the reward is R = DAfb(F) and the expected reward is Q = κDA − RDA(t2).

Corticostriatal synaptic plasticity. Synaptic plasticity on striatal medium spiny neurons (MSNs) follows a set of rules corresponding to the literature on plasticity in basal ganglia104. A large body of experimental literature indicate three factors involved in the LTP/LTD of corticostriatal synapses105: phasic increase in DA release; presynaptic activity; and postsynaptic depolarization. Here in this model we also consider the synaptic decay of corticostriatal synapses. The general form of corticostriatal change is described as:

dWij / dt = Fion(Si, Sj) · FDA(DA_SNc) − Γdec(Wij) (29)

where, Fion is the synaptic weight change as a function of pre- and postsynaptic activity of cortical and striatal neurons Sj and Si respectively, while FDA is the D1/D2 receptor factor as a function of the DA from SNc and Γdec is the synaptic decay.

The simplified specific form of Eq. (28) implemented here is given by

ΔWix = Hx · Si · FDA(DA_SNc − DA) − γWix j x ∈ {striatal blocks} (30)

Fion: ionotropic receptor factor, Si: activity of neuron i
FDA: DA receptor factor y = DA_SNc − DA = ϵ, σ0(x) = 1 / 1+e−x
Γdec: synaptic decay

KDA = 0.04, α = 2.5, γ = 0.01, θM = 1.0

Cortico-cortical and thalamo-cortical synaptic plasticity. In the simulation, specific rules of synaptic potentiation, and reversal of that potentiation, are adopted for synapses in superficial-layer anterior cortical cells, corresponding to consistently replicated studies from the plasticity literature108–110. Changes in synaptic weights W from cortical cell i to j are described first in general form, illustrating the proposed set of mechanisms to be studied, and then in highly specific form showing a very limited special case of the general form, currently implemented in simulations.

The general form of synaptic change modeled herein is:

dWij / dt = Fion(Si, Sj) − Frev(DA_VTA) (31)

where Fion and Frev are increments and decrements to synaptic weights; Si and Sj are the number of spikes from cortical source cells j and targets i, and D_VTA is the quantity of DA released from the Ventral Tegmental Area (VTA) onto the cortical targets. Frev is a function erasing the increment of all recently incremented synapses.

Having identified physiological-level systems mechanisms for potentiation and reversal, the effects of the general form can be substantially simplified for initial incorporation into a simulation. In the currently implemented model, these general forms are implemented as special cases, such that the change to a cortical weight from cell j to cell i is:

ΔWij = Kion · Si · Sj · (Wmax − Wij) · F(A) (32)

where Kion is constant and Wmax is the maximum value that a weight can attain, while F(A) is a feedback function of the response such that

F(A) = 1 j A: correct (33a)
0 j A: incorrect (33b)

thus capturing the effects of potentiation on correct trials and lack of potentiation on incorrect trials. We apply the same plasticity model for thalamo-cortical synapses as well.

Short-term augmentation of thalamo-cortical synapses: mechanism for sustained activity. In addition of long term plasticity in the thalamo-cortical synapses explained above, there is an additional short term synaptic augmentation that has been studied in the literature111,112. This short-term augmentation of timescales ranging from few milliseconds to few seconds is caused by several factors, e.g., the stimulus frequencies and behavioral states of cortical neurons. This short-term synaptic facilitation/augmentation has also been suggested as a mechanism for working memory through persistent reverberation of spiking activity113. The various molecular mechanisms underlying various types of short-term synaptic augmentation include presynaptic residual calcium114 and perturbation in postsynaptic firing115. In our model this short term thalamo-cortical augmentation serves two purposes: (1) to sustain the spiking activity in the circuit even after offset of stimulus and (2) to hold the pre-offset spatial firing pattern in the anterior cortex ("AC”) intact even during the delay epoch after the offset: the “holding” memory116. Thus, post stimulus offset, instead of only the spiking activity within the circuit, is sustained but also the “memory” of the stimulus encoded in the spatial firing pattern in the AC is sustained. This requires a mechanism which augments the thalamo-cortical synapses based on therecent firing of post-synaptic cortical neurons such that the neurons firing during stimulus would be more susceptible to the thalamic excitation even after stimulus offset.

We implement this by introducing an extra factor in thalamo-cortical synaptic conductance: gistp, for a synapse from thalamic neuron j onto the cortical neuron i. The synaptic Equation (3) gets modified for thalamo-cortical connections as:

Iijsyn = gistp(t) · Wij · gjsyn(t) (Vi−Vjsyn) (34)

where gistp, as in the case of synaptic conductance gsyn (see Equations (4)), increases due to spiking of neuron i:

dz / dt = −1/τ1 z + g1(V) (35)

dgistp / dt = −1/τ3 gistp + z · kstp (Gmaxstp − gistp) (36)

where τ3 is the decay constant and Gmaxstp is the asymptotic value (upper bound) of gistp. The intermediate variable z is the same in equations (4) and (33).

The equations governing the activity in different kinds of neurons is resummarized in Table 2.

Computational methods
One of the practical difficulties in multi-scale computational neuroscience has been the limitation due to the tension between model flexibility and simulation speed.

Other multi-scale models have been developed based on extensive knowledge of basic physiological and anatomical characteristics of the brain. Existing models can be broadly understood as primarily optimized for cellular-scale mechanisms, such as NEURON117, NEST118, Brian119, Emergent62, and SPAUN26; or optimized for multi-scale emergent dynamics like The Virtual Brain120. These platforms present challenges when attempting to produce multi-scale models that span from individual neurons to the whole-brain; with the exception of SPAUN and Emergent, they are also not typically constructed to generate extended cognitive operations such as the behavioral perception, learning, working-memory, and decision task presented herein.

To bridge this gap, we developed Neuroblox, a computational neuroscience platform optimized for data-driven multi-scale brain modeling (see Methods section in Supplementary Material for further details). Previous modeling efforts to create more realistic and detailed multi-scale biomimetic models have been hindered by the computational intractability of solving massive sets of differential equations. Neuroblox uses the Julia programming language and a variety of computational and mathematical algorithms that lead to orders of magnitude speedups, allowing for hundreds of complex neurons to be simulated in almost real-time. Our computational models are openly available, as stand-alone implementation (github.com/Neuroblox/corticostriatal-circuit-notebook) and also as a model designed in Neuroblox.jl (see https://www.neuroblox.ai/). Neuroblox can be used interactively via a graphical user interface.

Experimental task design
The category learning task and the corresponding neurophysiological recording with adult rhesus macaques (non-human primates, NHPs), on which we specifically model our simulations, is described in detail in refs. 27, 28 which we summarize here. The animals were taken care of in accordance with the National Institutes of Health guidelines and the policies of the Massachusetts Institute of Technology Committee for Animal Care. A single category learning session consists of 600–800 trials where, in each trial, initially the animal has to fixate on a central target on screen. While fixating, a randomly chosen category exemplar appears on the screen for 600ms. Each exemplar is a constellation of seven dots derived from either of the two category prototypes (called “A” and “B”) with small random variations. The dots are presented for 0.6s, removed for 1s (requiring a form of working memory), and then a decision is made. For the NHPs, the decision was in the form of a saccade to the left (for A) or right (for B) and correct decisions were rewarded with juice. This is summarized in Fig. S6a, b. The exemplar epoch (stimulus on) is followed by 1000ms of delay epoch (stimulus off) in which the animal again fixates on the center and waits for the response cue, after which two saccade targets appear on the left and right of center, each corresponding to one of the categories. The animal responds with a direct saccade to one of the targets indicating the correct category of the exemplar, thus receives a reward (juice drops) on correct response and no reward for incorrect response. The exemplar set for each category is gradually increased starting from 1 for each category, and is doubled in size after each bock of trials. Each block of trials is over once the accuracy of the animal reaches at least 80% over last 20 trials of that block, after which th next block begins with doubled set of exemplars. Based on this demarcation, first two blocks (100–150 trials) while trials from block 6 onward are considered late learning stage. For every session, a new set of category prototype patterns are used.

Simulation protocol
We simulated the category learning trial sessions as performed in the NHPs on our corticostriatal model. The set of random dot pattern stimuli as used in the experiments were also was used in the simulation as the input. Since the visual area in this model does not represent the primary visual area, but some higher area in the hierarchy of visual processing, we added a spatial filter to the dot patterns that approximate the gaussian smoothing as follows. The images are generated in gray-scale with total (15x15=) 225 pixels, where each pixel has brightness value within [0, 1] (0: black, 1: white). For each pixel (i, j) corresponding to the dot location, the brightness value is 1. For the four adjacent pixels (i−1, j), (i+1, j), (i, j−1), (i, j+1), brightness value is 0.6. For all other pixels, brightness is 0. As mentioned in the model description, the pixels map one-to-one onto the simulated visual area cortical units, where each unit (neuron) receives a steady input current proportional to the brightness value of corresponding pixel.

In accordance with the experiments, each trial was simulated for 1600ms, in which the stimulus onset is between 0–600ms, while during 600–1600ms there is no visual input. At the end of 1600ms the circuit model output determines the action choice, and based on the outcome a reward feedback is instantaneously fed into the model (see Model Description above). The synaptic weights are discretely updated after the reward feedback depending on the spatio-temporal spiking patterns within the circuit and the level of dopamine release from SNc and VTA respectively during the trial interval. The next trial cycle begins with the onset of the next visual stimulus. Right from the first trial onwards the stimuli are chosen from either of the two categories randomly out of the entire set, as opposed to gradual doubling of the stimulus set in the experiments (see ref. 28). For simulations we ran each session for 700 trials, matching the average number of trials in the experiments. We simulated 32 learning sessions to obtain the main results in this paper, wherein each session we used one out of eight pairs of prototypes to generate the exemplar dot patterns. Each prototype had about 1000 exemplars.

It should be noted that the training regimens are not quite the same: the animal training incorporated i) extensive initial shaping and pilot training, absent from the simulation; and ii) graded introduction of training instances such that initially only single category deformations were shown, and others added subsequently. Since we do not have the trials grouped into blocks based on the size of stimulus samples, we define early and late trials based on their learning behavior. For simplicity, we kept the group size of early and late trials equal for all simulated sessions viz. 1–150 trials were grouped as early trials and 500–700 trials were grouped as late trials, such that for all the sessions the early trials showed average accuracy below 60% and late trials showed average accuracy above 80%.

Analysis of model physiological response
Local Spike Summation (LSS). From the simulated spiking models, we wanted a quicker and computationally less complex method to approximate the LFPs to capture the qualitative behavior of the observed LFPs, particularly the synchrony effects. We are primarily interested in i) relating individual cell responses to LFPs, and ii) measuring the synchrony and phase relations among fields, across areas. We obtain a very simple neural mass activity from the spiking activities of various blocks in the model simulating specific brain regions, viz. Local Spike Summation (LSS), which is simply the spike histogram.

LSS(t, Δt) = 1/Δt Σj=1N Σm∈Zj δ(t − Tm) dt (37)

where, Tm is the mth spike-time for jth neuron while neuron j belongs to a specific block (brain region such as AC, STR etc.). For all PLV analyses of LSS, we used time bin Δt = 1ms. For rastor plot representations in Figs. 2 and 3 we used Δt = 20ms. We also calculate Average Local Membrane Potential (ALMP) as an alternate surrogate for local field: ALMP(t) = 1/N Σ Vi(t) where N is the neuronal population size and Vi(t) is membrane potential of neuron i. We devised the LSS as a surrogate, since the input (EPSPs) of the cells directly correspond to the output of the neighboring population.

For calculation of Phase Locking Values used to determine synchrony, see the Supplementary Materials.

Acquisition of incongruent neurons (ICNs). Incongruent neurons, in both simulations as well as empirical data, were identified during late learning trials as those units (neurons) that were selectively most responsive in the 0–200ms window of stimulus onset during the trials with resultant incorrect outcomes in either of the stimulus categories (A-incorrect and B-incorrect). For testing the predictive capability of incongruent neurons to anticipate incorrect outcomes, we used the first half of late trials for the acquisition of incongruent neurons (acquisition trials) and the second half of late trials for measuring the predictive effect (application trials). Acquisition was done as follows.

1. In order to determine the condition specific preference of single units (neurons) based on their average spiking behavior (as shown in Fig. 4a), for each unit (represented by a single row in Fig. 4a), we first determine the spiking activity Si for each unit i in response to the respective stimulus of each trial t. Si is the number of spikes occurring in unit i during a specific time interval (0–200ms of stimulus onset) for trial t. (A notable distinction is the smaller number of average spikes produced in the NHPs compared to the simulation: 1.33±0.05 spikes in 200ms empirically vs. 9.52±0.18 spikes per 200ms in the simulation.)
2. We then calculate normalized spiking activity Ni of each unit at a given trial t as: Ni = Si / Σi Si, which normalizes the global effects that are unrelated to stimulus but cause trial to trial variation of spiking activity.
3. We sort the acquisition learning trials (first half of late trials) into four conditions based on stimulus category (A vs B) and outcome (correct vs incorrect) viz. A-correct (C1), A-incorrect (C2), B-correct (C3) and B-incorrect (C4). We then calculate the average normalized activity NiC for each unit i over group of trials corresponding to each condition C where C ∈ {C1, C2, C3, C4}, using NiC = ⟨Nit⟩t∈C.
4. In order to sort the neurons based on their preferred condition, we determine the maximum values of NiC for each unit i and we do a second normalization of average normalized activity NiC by its maximum.

N_iC = max(NiC1, NiC2, NiC3, NiC4) (38)

If NC is the set of neurons that prefer condition C, we will have NiC = 1 ∀ i ∈ NC.
5. Incongruent neurons (ICNs) are the units that prefer conditions C2 and C4 (A-incorrect and B-incorrect), viz.: ICN ⊂ {NC2, NC4}
6. For empirical data we used an extra clean up step before the identification of incongruent neurons. We had only two criteria: (1) Reject sessions that have insufficient sample size of incorrect trials during the acquisition (≥4 A and B incorrect trials), and (2) Reject the neurons which have too sparse spiking activity throughout acquisition (neurons with Ni < 0.05 for more than 75% of trials)

Testing of incongruent neurons (ICNs). We established the predictive significance of empirical as well as simulated incongruent neurons as follows. After identifying ICNs in the acquisition trials, it was essential to test the statistical significance of predictive accuracy of incorrect choices in the subsequent set of trials, which we called application trials. We hypothesized that the incongruent neurons identified in acquisition trials would continue to be selectively more responsive during A (or B)-incorrect trials during subsequent application trials as well. For a given trial t, we use the total normalized activity of condition specific neurons Σi∈NC Ni as a decision variable to predict whether the trial is going to be of condition C. Thus, given a decision threshold θd the trial will be predicted to be A-incorrect if Σi∈NC2 Ni > θd, and B-incorrect if Σi∈NC4 Ni > θd. For a fixed θd, we determine the true positive trials and false positive trials based on the prediction success. If true positive PT is defined as number of C trials accurately predicted to be C, true negative GT is number of non-C trials accurately predicted to be non-C, false positive PF is number of non-C trials wrongly predicted to be C and false negative GF is number of C trials predicted to be non-C, then true positive rates RT and false positive rates RF are calculated as : RT = PT / (PT + GF) and RF = PF / (PF + GT). We then used these true positive and false positive rates for conditions C ∈ {C2, C4} to analyze the predictive significance of incongruent neurons. We performed three separate tests:

1. Group Statistics of ICN prediction. We determined the receiver operating characteristic (ROC) curves (true positive rate vs false positive rate) for each learning session and for each condition type (A-incorrect and B-incorrect) by sweeping across a range of decision threshold values θd. Predictive performance is estimated using the area under the curve (AUC) which is calculated as area between the ROC curve and the diagonal identity line. It ranges between 0.5 and -0.5. We compared the entire sample of AUC values A obtained over all sessions, to the randomized control set of AUCs obtained from using randomly sampled neuron set as predictors of incorr trials A0. We calculated the one-sample z-score as Z = (A − ⟨A0⟩) / sqrt(Var(A0)/N), where N is the sample size of all ROCs calculated over all sessions. The results are illustrated in Fig. 4b.
2. Sample predictive ICN effect. As a specific example to illustrate a simple ICN driven predictive criteria uniformly applicable for all learning sessions, we fix the value of θd instead of sweeping it over a range of values as done in the first test described above. For each learning session we determine the value of θd for predicting a trial condition C (C ∈ {C2, C4}), from the acquisition trials, using the criteria : Σi∈NC Ni < θd during 75% of the non-C trials during acquisition period. Using these acquired values of decision thresholds, we determine the true positive and false positive rates for each condition (A-incorr and B-incorr) across all the sessions. Here predictive performance is measured as difference between true positive and false positive rates: dTF = RT − RF. We compare the sample of dTF obtained from ICNs to the randomized control of predictive performances obtained by randomly sampled neurons used for prediction d0TF. Significance is measured using one sample z-score: Z = (dTF − ⟨d0TF⟩) / sqrt(Var(d0TF)/N), where N is the sample size. The results are illustrated in Fig. 4c.
3. Predictive real-time statistics of ICN intervention Based on the predictive criteria suggested in the second test described above, we design a prediction based hypothetical intervention protocol (imagined as a “neuroprosthetic”) during the application trials in real time, so as to test whether there is a significant improvement in the accuracy of the model as well as the animal. The intervention protocol simply is: if the trial is predicted to be incorrect by either A-incorrect (NC2) or B-incorrect neurons (NC4), stop the trial/disqualify the response. We calculate the difference of accuracy between intervened behavior and the original behavior Δ. We compare the sample of observed Δ to that obtained from randomized control set of randomly intervened behavior Δ0 i.e., randomly selected trials are disqualified instead of trials predicted as incorrect. The significance is measured as one sample z-score: ZΔ = (Δ − ⟨Δ0⟩) / sqrt(Var(Δ0)/N), where N is the sample size. The results are illustrated in Fig. 4d, e

All the above mentioned statistical analysis of the simulated physiological responses, such as power spectral density, phase lock value, etc. were written in MATLAB R2020.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have dealt with one of the prominent problems in neuroscience, namely, hierarchical modeling of cellular-to-cognitive functioning of the nervous system. They have attempted to reach this goal using a detailed cellular and connectivity model of the cortico-striato-thalamic system, for which there is a veritable mountain of neurobiological evidence. They have verified this model to account for cellular and behavioral evidence in the nonhuman primate. Most important for the present submission, the authors claim to have utilized the model to uncover a novel “neural code”. The highlighted features of the authors’ model is that it can reveal model outcomes that are, to use the authors’ terminology, “zero-trained” or “out-of-distribution inferences”. These are certainly marked strengths of the current paper, and in this sense, are certainly deserving of publication in Nature.\n\nDespite strong support for the current submission, this reviewer finds several weaknesses that require revision. First is the authors’ justification of “lateral inhibition assemblies” as evidence for a “computational primitive” of the cortical-striatal circuit. Although this claim is certainly reasonable, lateral inhibition by itself is not terribly convincing as an emerging computational property. In addition, “winner-take-all” functions are hardly biologically reasonable. Aren’t there more complex/surprising and thus more interesting spatio-temporal patterns of activity emerging from the interconnected circuitry of neurons having the biophysical properties known for the modeled neurons? Given the known richness of voltage-dependent channels having differential dendritic and cellular distributions in cortical, striatal, nigral, and thalamic populations, is the proposed model really limited to age-old lateral inhibition? Although this question is open-ended and thus somewhat unfair to the authors, this reviewer finds that other spatio-temporal patterns must be evident and would represent a far stronger argument for the model.\n\nThe fact that the higher-level output of the model represents category learning is exciting, but it should be noted that the number of categories is small indeed, i.e., two. This is consistent with the emphasis on lateral inhibition: on or off. Perhaps this reviewer has missed something important about the category learning accomplished by the model, in which case whatever that is should be re-emphasized.\n\nA second concern which is related to the first is the small number of neurons included in the model. The proposed model is composed of only hundreds of neurons (without compartments? see below). In these days of widespread computational power, an increased number of neurons per region in the model would seem more than reasonable. In addition, it would provide the basis for detection of more complex spatio-temporal patterns involved in coding schemes.\n\nThird, an important issue is that the authors point out that both the Striatal and TAN subcircuits are not represented by sets of compartmental neurons, but instead as input/output elements. There is a rich literature documenting the strength of input/output models and their comparison to compartmental models, yet this literature is not cited or used to justify the authors’ approach.\n\nThis reviewer has several minor comments as follows: (1) is “lateral inhabitation” a useful, widely used term? vs lateral inhibition? (2) was the pars reticulata purposely left out of the model? (3) presynaptic inhibition also has been found among striatal neurons; was this form of inhibition also deliberately omitted? The authors cannot be expected to include every known function or feature in their model; simple justifications to these questions should suffice.", "reviewer_2": "In this paper, the authors constructed a biologically detailed model of the cortico-basal ganglia-thalamic networks of spiking neurons. Through numerical simulations of the model, the authors validated the model's neural activities using the data recorded from macaque monkeys. The authors showed that neuronal functional subtypes emerge to predict a failure behavioral choice (incongruent neurons) and claimed that the model is \"zero trained\". The two results sound interesting and constitute the major finding of this study. However, the present manuscript is hard to follow: it is written in small fonts and densely packed with many lines. It was also a labor to find the information necessary to clarify the details of the study. These style issues have nothing to do with the quality of the science reported, but they disturb the readers' understanding of the results. Partly due to these difficulties, I could not be fully convinced of the validity of the major claims. I feel that these claims are somewhat overstated.\n\nMajor comments:\n1. The categorization task used should be explained more clearly with illustration (fig. s6). In the current manuscript, fig. s6 is referenced much later than where the task is first explained in ll. 53-55 at the beginning of the Result section. The reader can easily miss the figure and just find a short description of the task in the Methods section (without a reference to Fig. s6).\n2. In ll. 34-36, the authors explained the \"zero trained\" condition as: \" A far more rigorous standard of generalizability is to ask whether a model built from one kind of data can predict phenomena of a wholly different kind. This explanation confused me because the model does not seem to undergo any training on a different type of task before it is trained on the categorization task. The authors might have meant to say that they built the model solely based on anatomy and physiology data and tested its performance on learning behavioral data from the categorization task. If this is the case, however, I do not find a big surprise in that the model could learn the categorization task using its entire dataset. The authors should explain why this is non-trivial before they claim that the \"zero trained\" feature is an unexpected outcome of the model. The training conditions should also be explained more clearly.\n3. Potentially related to the above comment, I am not convinced of the importance of the \"primitive\", which is a modular block of small numbers of excitatory and inhibitory neurons (fig. 1b). The computational benefit of this modular structure was not explored sufficiently in detail. The authors hypothesized that these primitives exist in the cortical layer 2/3 and they perform WTA competition via lateral inhibition. Are these hypotheses supported by experimental evidence or just theoretical assumptions?\n4. In ll. 277-281, the authors claimed \"Finally, we emphasize the broader point that it was the zero-trained nature of simulations based on biomimetic computation that enabled the emergence of these broad and unexpected findings. Whereas much computational work focuses on the statistical learning of specific data, the present work instead attempts to uncover how low-level physiological spiking, embedded in specific delimited sets of anatomical wiring organizations, can itself give rise to the forms of neural and behavioral results that brains produce.\" However, I doubt whether this finding is as surprising as the authors claim. As mentioned in my comment 3, the authors hypothesized WTA competition for the primitives, a basic computation essential for categorization tasks. Their large-scale model, therefore, involves a minimal circuit motif necessary to solve the task under consideration. When a large system contains a small system that can solve a task, I find no surprise that the large system can also solve the same task.\n5. The A-correct, A-incorrect, B-correct, and B-incorrect neuronal responses should be defined explicitly. What exactly can A-incorrect and B-incorrect neurons tell about the categorization of input? Is it the probability that an input is unlikely to belong to A or B (i.e., the volume ratio between the pink shaded area vs its outside in Fig. S6)? Logically, X = not-A does not mean X=B, and similarly, X=not-B does not mean X=A. However, in the alternative choice task (e.g., succade to A or B), not-A may behaviorally imply B as the animal can choose one of the two options. How are the activities of the four functional subtypes combined to generate the model's decision? The computational implications and importance of incongruent activities depend on how the task was designed and how the neural responses were used to solve the task. I might find these details somewhere in the manuscript, but they should be explained more carefully.\n6. What conditions ensure the emergence of incongruent responses in the model? Do incongruent neurons play any role in switching between exploitation and exploration? How crucial is having such a functional subtype for task performance?\n7. I wonder whether references are cited correctly (for example, refs 16-18). Please check the references.\nMinor comments:\n8. How is the speed of learning compared with the experimental observation?\n9. Are the responses of TANs crucial for switching exploitation and exploration?\n10. In Fig. 2b, what <W> means? Does it refer to averaging over neurons? I guess some connections are strengthened, as shown in the figure, but the other connections are weakened even if their overall average increases during learning.\n11. The limitations of the model should also be discussed briefly.", "reviewer_3": "This study proposes a computational model that can simulates multiple brain regions (cortex, striatum and thalamus) associated with perceptual decision-making and replicates both spikes and LFPs, which have been experimentally observed. As most brain areas do not work alone, it is imperative that we simulate interareal interactions. In this study, the authors propose to combine population models and spiking neural networks to reduce the complexity of multi-areal models, which is an interesting approach. As they constrain the model’s structure using experimental data, their study may provide a good reference model for those who develop multi-areal models.\n\nHowever, I would like to point out several concerns.\n\nThis model aims to replicate the recorded neural responses, while NHPs are performing a simple visual categorization task. Since the task is quite simple, no significant learning is necessary, but the authors implied that their model can provide insights into the learning process in the brain. In my opinion, the value of this model is that we could probe functional roles of brain areas on visual categorization using their model. Thus, I would like to ask the authors if they tried to quantitatively evaluate the influence of individual brain areas. If one of the areas is disabled, neural and behavioral responses may fail or differ from the observed responses. It may be possible to gain new insights into perceptual decision-making from these failure modes.\n\nThe authors claimed that their model can predict the existence of incongruent neurons, which are experimentally confirmed. However, I am concerned that this incongruent neuron could be just a byproduct of winner-take-all mechanisms. Let’s consider three sets of neurons. Stimulus A, Stimulus B and none-of-the-above (NOA). Due to lateral inhibition, we expect only one of the three sets to be active at a time. In addition, we can further assume that Stimulus A or B is trained to become quiescent when an incongruent stimulus is presented. That is, when stimulus A is presented, Stimulus B neurons become silent, and vice versa. Then, if Stimulus A fails to activate Stimulus A neurons, NOA neurons will be active due to the lack of lateral inhibition, which will be consistent with incongruent neuron behaviors. Based on this line of reasoning, I am not sure what we can learn from their model’s predictions.\n\nIn the Results section, the main cortical area of interest is \"anterior cortex\", but Methods section describes \"prefrontal cortex\", not anterior cortex. As the cortex model relies on a generic structure, such mix-up may not matter too much, but to maintain clarity of this study and not to confuse the readers, this mix-up should be fixed properly.\n\nIn lines 18-25, the author stated: “However, most of these models have been optimized for either mechanistic accuracy or to elicit emergent cognition, but not both. On the one hand, there are powerful models with biologically-based systems that reproduce extensive physiologies, from single-cell spiking through generation of oscillations and other critical emergent dynamics. Yet, while these can accurately simulate brain-wide phenomena (7) such as epileptic seizure genesis and propagation (8, 9), they are not designed to generate cognitive processes, such as learning. On the other hand, some of the most remarkable cognitive models are those grounded in artificial neural networks (10–15). Yet, while this is a class of models that effectively “learn,” they do so without reliance on biologically realistic (biomimetic) mechanisms at the cellular scale.”\n\nI believe these statements are not accurate. A line of study uses biologically-realistic circuits to probe neural correlates of high-level cognitive functions such as decision-making, working memory and integration of sensory evidence. Also, all major simulation platforms, such as NEST and NEURON, support long-term synaptic plasticity and can be used to study ‘learning’ in the brain. The general learning algorithm for spiking neural networks, which corresponds to 'backpropagation' in deep learning, remains missing, but the lack of learning algorithm must not be confused with the lack of study of biological learning.\n\nSelected References\nGoldman, M. S., Compte, A., & Wang, X. J. (2009). Neural Integrator Models. In Encyclopedia of Neuroscience (pp. 165-178). Elsevier Ltd. https://doi.org/10.1016/B978-008045046-9.01434-0\nWang XJ. Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition. Annu Rev Neurosci. 2022 Jul 8;45:533-560. doi: 10.1146/annurev-neuro-110920-035434. PMID: 35803587.\nWang XJ. Decision making in recurrent neuronal circuits. Neuron. 2008 Oct 23;60(2):215-34. doi: 10.1016/j.neuron.2008.09.034. PMID: 18957215; PMCID: PMC2710297.\nHill S, Tononi G. Modeling sleep and wakefulness in the thalamocortical system. J Neurophysiol. 2005 Mar;93(3):1671-98. doi: 10.1152/jn.00915.2004. Epub 2004 Nov 10. PMID: 15537811.\nWagatsuma N, Potjans TC, Diesmann M, Fukai T. Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model. Front Comput Neurosci. 2011 Jul 8;5:31. doi: 10.3389/fncom.2011.00031. PMID: 21779240; PMCID: PMC3134838.\n\nIn lines 31-39, the authors stated: “Of course, in silico “discovery” of new mechanisms and treatments is possible only if models can apply their learned knowledge to novel or unexpected inputs. A computational model’s ability to make predictions or draw conclusions about data that fall outside the distribution of its training set is referred to as out-of-distribution inference. A standard training approach is to train a model from half the data of a particular kind and then test its ability to predict the other half. A far more rigorous standard of generalizability is to ask whether a model built from one kind of data can predict phenomena of a wholly different kind. These latter types of predictions are said to be zero-trained. Here, we address these challenges by building a zero-trained computational model of the corticostriatal circuit designed to be mechanistically accurate across multiple scales, from spiking neurons to local field potentials.”\n\nI strongly recommend the authors to remove the statements regarding ‘zero-trained’ and clarify what their proposed model actually achieves. The authors are implying that their proposed model can perform zero-shot learning (zero-trained) or make predictions on novel inputs (out-of-distribution inference). While zero-shot learning and out-of-distribution are important topics in the field of deep learning, the authors do not provide any results relevant to zero-shot learning and out-of-distribution inference in their study.\n\nAlso, the authors need to clarify what ‘multi-scale’ means in their manuscript. Based on the phrase, “from spiking neurons to local field potentials”, they appear to be referring to both structure (e.g., neurons or brain areas) and neural responses (e.g., spikes and LFPs), but to avoid confusion, they should clarify what they mean.\n\nIn line 106, the authors stated: “The arrangement used here is very distinct from ANN approaches”\nANN has not been defined. If ANN means artificial neural networks, the authors should define what kind of ANN approaches they are referring to.\n\nIn lines 126-128, the authors stated: “The interconnections between these subcircuits support the process of learning. As the model experiences many trials, there are changes to synaptic strength for cortico-cortical, cortico-striatal, and thalamo-cortical contacts that cause changes in behavior”\nI think the authors should show how these interareal connections evolve over time. As the model depends on various cell assemblies, they should show the evolution of weights between cell assemblies.\n\nIn line 156, the authors stated: “For the simulation, we developed a similar metric called Local Spike Summation (LSS), a simple measure of simulated neuronal activity defined in Method”\nEarlier works simulated LFPs in multiple ways, but LFPs are generally thought to reflect synaptic inputs, whereas LSS reflect outputs of neurons. The authors should, at least, justify why they used LSS to approximate LFPs. As their proposed model aims to produce multi-scale responses, it is important to simulate LFPs as accurately as possible.\n\nIn line 158, the authors stated: “one noticable difference is that NHP brains do not show as marked a decrease in spiking after stimulus offset as the model (Figure 2C)”\nCan the authors provide any insights into this discrepancy? Alternatively, can they explain why it is not important?", "reviewer_4": "This is an exciting paper, mainly because of the discovery in the model of a type of neural coding that was unknown before and is found in their data after its discovery in the model. This population of neurons codes for when the choice of response is going to be incorrect.\n\nSince I took the theory track at Cornell, I never got diffEQ, so I did not check the equations carefully, but I will note that they switch in the article between W^{ij} meaning the weight from unit j to unit i (e.g., line 319) to vice versa (e.g., line 520) and back (line 99, suppl. mat.). Be consistent.\n\nMy main problem with the paper is that this type of model has been done before, perhaps not with such exacting precision with respect to the neurophysiological data, but the spirit is the same, by Randy O’Reilly and Yuko Munakata (2000 and subsequent versions), and there is no reference to this work. In that book, they simulate learning a go-no go task with a model basal ganglia using the Emergent simulator. Similar to the current model, the system is wired up in the same manner as the basal ganglia, and it is trained by the reinforcement learning system intrinsic to the model. Similarly, there’s no reference to Chris Eliasmith’s work with SPAUN.\n\nIn addition, you say \"those models were designed to elicit a circumscribed family of behaviors centered on reinforcement learning and were not constructed in a manner that allows them to be easily extended beyond their original scope.\" This is certainly not true of Eliasmith's model, which shows the ability to learn seven different tasks. And while I haven't read all of them, O'Reilly, especially with Michael Frank, has published numerous papers on his model's explanation of working memory, etc.\n\nI'm sure there are many important differences (for example, your model includes oscillatory behavior, crucial to some aspects of the model, which differs from O'Reilly's model if I recall correctly). However, it is still necessary to distinguish this model from those earlier works.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that emerged from the simulations. However, I would like to see how it relates to earlier models in the same vein. If that issue were addressed, I think this rates as publishable in Nature Communications.\n\nWhat would really nail the data would be if you can use your idea of promoting learning by stopping trials that the incongruent neurons predict will be an error in an actual monkey. I don't expect that, given the difficulty, expense, and time necessary, but wow, if you did that, it would be a paper for the ages.\n\nMore specific comments/wording suggestions/typos/notes:\nIntroduction: there is no reference to any of Randy O’Reilly’s work??\n“These latter types of predictions are said to be zero-trained.”\nI’ve never heard the concept of “zero-trained” - I’ve heard of zero-shot. If you are going to introduce new terminology, say instead “we call these models “zero-trained””.\nHowever, that seems like an anomaly, since you do train it - by reinforcement learning, which is built into the system. I think a better description would be to call it “intrinsically-trained”. But I will leave that up to the authors.\n\nline 91: lateral inhibitation -> lateral inhibition\nFig1 caption: Detailed discussion in provided in the Methods\n-> Detailed discussion is provided in the Methods\nline 155: no need for scare quotes around local field potentials\nline 160: no need for scare quotes around phase locking value.\nThere may be other examples of this.\n\nline 161: reference 20: EG Antzoulatos, EK Miller (2014). Glancing at this paper, I take it that the strength of connections between the two areas (striatum and PFC) increase overall - i.e., both ways. In Figure 3b, you also show this connectivity increasing, but it isn't clear whether this is from stratum to your simulated frontal lobes or vice-versa. Can you be more specific than \"portico-striatal\"? Does this graph work both ways?\nLine 186: Commprising -> Comprising\nFigure 3A caption: highlighted in gray, not yellow. The colors of the lines are incorrectly specified (they are blue and green, not red and light red).\nFigure 3B Figure: Correct me if I'm wrong, but you seem to have incorrect and correct flipped here.\nLast line of Figure 4 caption: The sentence says “would improve” - so this is a prediction. If you were able to do this experiment, it would be especially confirmatory, although I know experiments with NHPs are expensive and time consuming.\nLines 251-252 - please remove the scare quotes.\nline 269: NHPs are not “constructed”! Try “evolved”.\nLine 285 and others: You describe this as “working memory”? Can you elaborate on why you call it that?\n\nIn equations 30 and 31, I believe you have switched from W^{ij} meaning the weight from j to i (as in equation 3) to the reverse. Hopefully this is correct in the code!\nIt is unclear what is going on in equation 29, as there is only one index on the weight, that is, “i”, which suggests this is a solely pre-synaptic learning rule, unlike the rule in equation 28. Explain this, or fix it.\nEquation 33 reverts to the equation 3 definition of which way the weight W^{ij} goes.\nLine 501: you need a reference for the BCM rule. Bienenstock, Cooper, and Munro 1982.\nLine 502: typo: “formas” either this is supposed to be “form as” or “formulas”\nIn the Computational Methods section (starting line 547), you don’t mention Chris Eliasmith’s SPAUM model using the Semantic Pointer Architecture. How does NeuroBlox differ from his simulator, which seems to be addressing very similar concerns and uses actual spiking neurons. Or alternatively, how does it differ from Emergent (O’Reilly)?\n\nSupplementary Material\nLine 8: (NG-NMM) model, -> mass model (NG-NMM),\nLine 24 Supplimental -> Supplemental\nFigure S5 caption: \"Striatal spiking shows a post-stimulus-onset silence that was subsequently found in empirical data;\"\nThis seems like a second discovery. Why don't you also emphasize this in the main text? Here, in terms of \"silence\", are you referring to the pause in spiking around 40-60ms? This appears to be borne out in the local spike summation, although the timing is not quite matched by the empirical data. The match between D and E in this figure is pretty cool; again, it seems like a second finding that you might push in the main text.\n\nThe mechanical explanation that you later derive in the supplemental material should be better reflected in the main text.\n\nReferences\nEliasmith et al. (2012) A Large-Scale Model of the Functioning Brain. Science.\nO'Reilly & Frank (2006) Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation. [And many other papers with Michael Frank]\nO'Reilly & Munakata (2000) Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge: MIT Press\nSigned,\nGary Cottrell"}, "author_response": "Reviewer 1:\nThe authors have dealt with one of the prominent problems in neuroscience, namely,\nhierarchical modeling of cellular-to-cognitive functioning of the nervous system. They have\nattempted to reach this goal using a detailed cellular and connectivity model of the cortico-\nstriato-thalamic system, for which there is a veritable mountain of neurobiological evidence.\nThey have verified this model to account for cellular and behavioral evidence in the nonhuman\nprimate. Most important for the present submission, the authors claim to have utilized the model\nto uncover a novel “neural code”. The highlighted features of the authors’ model is that it can\nreveal model outcomes that are, to use the authors’ terminology, “zero-trained” or “out-of-\ndistribution inferences”.\nThese are certainly marked strengths of the current paper, and in this sense, are certainly\ndeserving of publication in Nature.\n\nOur reply:\nWe greatly appreciate these comments from the Reviewer. We have now responded to\nall reviewer comments, as well as substantial updates to the manuscript, all of which are\nnoted and pointed to within our responses below.\n\nReviewer 1:\nFirst is the authors’ justification of “lateral inhibition assemblies” as evidence for a\n“computational primitive” of the cortical-striatal circuit. Although this claim is certainly\nreasonable, lateral inhibition by itself is not terribly convincing as an emerging computational\nproperty. In addition, “winner-take-all” functions are hardly biologically reasonable.\n\nOur reply:\nThis is a very helpful point. These terms “winner take all” and “lateral inhibition\nassemblies” have been used in the literature to refer to multiple very different proposed\nmechanisms. The reviewer is correct, some of them are not biologically reasonable. We\nmeant to refer to the set of specific mechanisms that are biologically plausible. We used\n“winner-take-all” because it is easy-to-understand concept that is familiar to many.\nWe actually used a set of mechanisms based on well-established anatomical and\nphysiological relations among excitatory pyramidal and local feedback inhibitory cells.\nThe time course of their activity (rapid excitation followed by slower and longer-duration\nfeedback inhibition) and nonlinear computational functions are based on empirical\nobservations (e.g., Rudy et al., 2011; Tremblay et al., 2016). Further, studies show that\nthis anatomical arrangement is conserved across cortical regions (e.g., Pfeffer et al.,\n2013; Campagnola et al., 2022).\nThe revised manuscript now describes these mechanisms and their empirical support in\ngreater detail (lines 93-114, Fig 1b). We now refer to these mechanisms as local-\nfeedback lateral inhibition circuits (L-FLIC).\nREFs\nCarsten K Pfeffer, Mingshan Xue, Miao He, Z Josh Huang, and Massimo Scanziani. Inhibition of inhibition\nin visual cortex: the logic of connections between molecularly distinct interneurons. Nature neuroscience,\n16(8):1068–1076, 2013.\nLuke Campagnola, Stephanie C Seeman, Thomas Chartrand, Lisa Kim, Alex Hoggarth, Clare Gam- lin,\nShinya Ito, Jessica Trinh, Pasha Davoudian, Cristina Radaelli, et al. Local connectivity and synaptic\ndynamics in mouse and human neocortex. Science, 375(6585):eabj5861, 2022.\nRudy B, Fishell G, Lee S, Hjerling-Leffler J. Three groups of interneurons account for nearly 100% of\nneocortical gabaergic neurons. Developmental neurobiology, 71(1):45–61, 2011.\nTremblay R, Lee S, Rudy B. Gabaergic interneurons in the neocortex: from\ncellular properties to circuits. Neuron, 91(2):260– 292, 2016.\nDouglas R, Martin K. Neuronal circuits of the neocortex. Ann. Rev. Neurosci. 2004. 27:419–51.\n\nReviewer 1:\nAren’t there more complex/surprising and thus more interesting spatio-temporal patterns of\nactivity emerging from the interconnected circuitry of neurons having the biophysical properties\nknown for the modeled neurons?\nGiven the known richness of voltage-dependent channels having differential dendritic and\ncellular distributions in cortical, striatal, nigral, and thalamic populations, is the proposed model\nreally limited to age-old lateral inhibition? Although this question is open-ended and thus\nsomewhat unfair to the authors, this reviewer finds that other spatio-temporal patterns must be\nevident and would represent a far stronger argument for the model.\n\nOur reply:\nThe reviewer's instincts are correct—it is not merely \"age-old\" lateral inhibition. The\nmodel generates spatiotemporal patterns, and we have revised the manuscript to make\nthis explicit.\nThe model exhibits distinct cortico-striatal behaviors over time, including varying\npatterns of synchrony and phase locking directly linked to the behavior of the model (or\nanimal) [see Figure 2d, 3a; revised manuscript lines 189-208].\nCortico-striatal activity patterns evolve due to distinct mechanisms in the cortex and\nstriatum, as supported by nonhuman primate (NHP) data [see Figure 2b, 3b,\nSupplementary Figure S11; revised manuscript lines 170-173, 208-212, 647-705].\nCrucially, the model was not trained on NHP neurophysiological data, yet it\nindependently produced spatiotemporal patterns that closely align with NHP data across\nmultiple levels (cells, assemblies, local fields, inter-region synchrony, and phase).\nWe agree that this strongly supports the model. These points should have been clearer\nin the original draft, and we have now addressed them.\n\nReviewer 1:\nThe fact that the higher-level output of the model represents category learning is exciting, but it\nshould be noted that the number of categories is small indeed, i.e., two. This is consistent with\nthe emphasis on lateral inhibition: on or off. Perhaps this reviewer has missed something\nimportant about the category learning accomplished by the model, in which case whatever that\nis should be re-emphasized.\n\nOur reply:\nWe chose a two-alternative category decision task because it connects a large body of\nprior work that has used two-alternative forced choice paradigms. Future work will\nincrease the model’s abilities.\nImportantly, this was not a simple two-alternative task. The nature of the category\nlearning made it complex. The number of distinct exemplars that the model (and the\nNHPs) learn is substantial. There were 8 separate prototypes, each with ~1000\nexemplars. We have revised the manuscript to make this explicit (see revised manuscript\nlines 756-758).\nIt may be useful to also note that the cortical inhibitory feedback mechanism (L-FLIC)\nthat we employ is inherently capable of learning multiple categories, not two. The\nsuperficial-layer cell population within a cortical column contains multiple L-FLIC circuit\nassemblies and comprises hundreds of thousands of cells. Each dendrite in a cortical\nregion has a unique distribution of synapses and strengths, leading to differential\nactivation speeds (faster or slower voltage rise) in response to diverse input patterns,\nsuch as dot stimuli.\nIn short, the same mechanism applied to this architecture can learn many categories, not\njust two. This is now further explained in lines 491-497 in the revised manuscript.\n\nReviewer 1:\nA second concern which is related to the first is the small number of neurons included in the\nmodel. The proposed model is composed of only hundreds of neurons (without compartments?\nsee below). In these days of widespread computational power, an increased number of neurons\nper region in the model would seem more than reasonable. In addition, it would provide the\nbasis for detection of more complex spatio-temporal patterns involved in coding schemes.\n\nOur reply:\nWe again very much agree. Work is under way to run substantially larger numbers of\nneurons as well as multi-compartment neural renderings. We anticipate that, as the\nreviewer notes, these will add to the model’s abilities to produce more complex spatio-\ntemporal patterns, and potential further neural codes.\nWe chose the initial size of the model to allow the model’s operation to be sufficiently\nstraightforward, transparent, and to explain the desired phenomena in a fully\nunderstandable and analyzable way, with no “black boxes” of training to intervene. In\nother words, we aimed to make the model as simple as possible, but no simpler.\nWe continue to be excited and surprised at the initial success of even this modest sized\nmodel to account for substantial complex physiological and behavioral phenomena, and\nas the reviewer appropriately points out, we are actively pursuing ongoing study of\nmodels that are larger and more complex.\nWe revised the manuscript to make these points (see manuscript lines 387-403).\n\nReviewer 1:\nThird, an important issue is that the authors point out that both the Striatal and TAN subcircuits\nare not represented by sets of compartmental neurons, but instead as input/output elements.\nThere is a rich literature documenting the strength of input/output models and their comparison\nto compartmental models, yet this literature is not cited or used to justify the authors’ approach.\n\nOur reply:\nWe agree. Current work is in process to expand the striatal, pallidal, and TAN regions to\nsupplant simplified input/output entities with physiologically based medium spiny\nneurons (MSNs) and detailed tonically active neurons (TANs).\nAs the reviewer points out, modeling MSNs, TANs, and SNc as input/output elements is\nwell supported by extensive studies of dendritic arbors (Carandini 1996) relating synaptic\ninput characteristics and output spiking characteristics (Bernander 1994). Multiple\nphysiological mechanisms have been proposed to incorporate active dendrites and ion\nchannel dynamics into input/output computational units (Silver 2010; Cash 1998; 1999;\nTamas 2002; Gasparini 2006).\nThis has been added to the revised manuscript (lines 140-142, 508-510).\nReferences:\nM Carandini, F Mechler, CS Leonard, JA Movshon, Spike train encoding by regular-spiking cells of\nthe visual cortex. J. Neurophysiol.974\n(1996).975\nS Cash, R Yuste, Input Summation by Cultured Pyramidal Neurons Is Linear and Position-\nIndependent. J. Neurosci. 18, 10–15976\n(1998).977\nS Cash, R Yuste, Linear Summation of Excitatory Inputs by CA1 Pyramidal Neurons. Neuron 22,\n383–394 (1999).978\nG Tamás, J Szabadics, P Somogyi, Cell Type- and Subcellular Position-Dependent Summation of\nUnitary Postsynaptic Potentials979\nin Neocortical Neurons. J. Neurosci. 22, 740–747 (2002).980\nS Gasparini, JC Magee, State-Dependent Dendritic Computation in Hippocampal CA1 Pyramidal\nNeurons. J. Neurosci. 26,981\n2088–2100 (2006).982\nRA Silver, Neuronal arithmetic. Nat. Rev. Neurosci. 11, 474–489 (2010).983\nO Bernander, C Koch, RJ Douglas, Amplification and linearization of distal synaptic input to\ncortical pyramidal cells. J. Neurophysiol.985\n72, 2743–2753 (1994)\n\nMinor comments\nReviewer 1:\n(1) is “lateral inhabitation” a useful, widely used term? vs lateral inhibition?\n\nOur reply:\nWe thank the reviewer for catching this typographical error. Lateral inhibition has now\nbeen replaced by L-FLIC (revised manuscript lines 106-107)\n\nReviewer 1:\nwas the pars reticulata purposely left out of the model?\n\nOur reply:\nYes, SNr is intentionally absent only for this initial model for the sake of simplicity. But\nPer the reviewer’s comments, we now added a discussion of the SNr to the revised\nmanuscript (manuscript lines 579-585) with the following citations.\nREFS\nJ.S. Schneider, U.E. Olazabal,\nBehaviorally specific limb use deficits following globus pallidus lesions in rats,\nBrain Research,\nVolume 308, Issue 2, 1984, Pages 341-346,\nW Hauber, S Lutz, M Münkle,\nThe effects of globus pallidus lesions on dopamine-dependent motor behaviour in rats,\nNeuroscience,\nVolume 86, Issue 1, 1998, Pages 147-157\nLai YY, Kodama T, Hsieh KC, Nguyen D, Siegel JM. Substantia nigra pars reticulata-mediated\nsleep and motor activity regulation. Sleep. 2021 Jan 21;44(1):zsaa151. doi: 10.1093/sleep/zsaa151.\nPMID: 32808987; PMCID: PMC7819837.,\n\nReviewer 1:\npresynaptic inhibition also has been found among striatal neurons; was this form of inhibition\nalso deliberately omitted? The authors cannot be expected to include every known function or\nfeature in their model; simple justifications to these questions should suffice.\n\nOur reply:\nWe are incorporating this characteristic into new versions of the model in ongoing work.\nThe existing lateral inhibition between segments of the striatal complex may be\nproducing some of the effects that otherwise would arise from presynaptic inhibition.\nWe now discuss this in the revised manuscript (lines 620-623).\n\nReviewer #2 (Remarks to the Author):\nIn this paper, the authors constructed a biologically detailed model of the cortico-basal ganglia-\nthalamic networks of spiking neurons. Through numerical simulations of the model, the authors\nvalidated the model's neural activities using the data recorded from macaque monkeys. The\nauthors showed that neuronal functional subtypes emerge to predict a failure behavioral choice\n(incongruent neurons) and claimed that the model is \"zero trained\". The two results sound\ninteresting and constitute the major finding of this study.\nHowever, the present manuscript is hard to follow: it is written in small fonts and densely packed\nwith many lines. It was also a labor to find the information necessary to clarify the details of the\nstudy.\nThese style issues have nothing to do with the quality of the science reported, but they disturb\nthe readers' understanding of the results. Partly due to these difficulties, I could not be fully\nconvinced of the validity of the major claims. I feel that these claims are somewhat overstated.\n\nOur reply\nWe apologize for our lack of clarity. We have revised the manuscript accordingly\nMajor comments:\n1. The categorization task used should be explained more clearly with illustration (fig. s6). In the\ncurrent manuscript, fig. s6 is referenced much later than where the task is first explained in ll.\n53-55 at the beginning of the Result section. The reader can easily miss the figure and just find\na short description of the task in the Methods section (without a reference to Fig. s6).\n\nOur reply\nThese are very helpful comments and suggestions.\nPer the reviewer’s suggestion, the references to Figure S6 now appear very early, in\nrevised manuscript line 65, as well as in the Methods section (revised manuscript line\n730)\n2. In ll. 34-36, the authors explained the \"zero trained\" condition as: \" A far more rigorous\nstandard of generalizability is to ask whether a model built from one kind of data can predict\nphenomena of a wholly different kind. This explanation confused me because the model does\nnot seem to undergo any training on a different type of task before it is trained on the\ncategorization task.\n\nOur reply:\nWe have added explanatory text in the revised manuscript lines 36-42 and 405-415 to\nclarify the concepts and the terminology of “zero-trained”, and lines 363-369 and 387-403\nto elucidate the non-trivial accomplishments of the model.\nThe reviewer is correct that a common test of generalizability is training a model on one\nset of data and then tested on another dataset. Our test was far more rigorous. Our\nmodel was not trained on any data. Instead, the model was trained on the same\nbehavioral task that the NHPs learned. Thus, the model’s properties closely match real\nneurophysiological data even though no data was ever presented to the model.\nWe should have made this clearer. This is an important point because it shows that our\nmodel is biologically plausible. It can replicate observed neurophysiological data\nwithout being trained or tuned by data. We have revised the manuscript accordingly.\n\nReviewer 2:\nThe authors might have meant to say that they built the model solely based on anatomy and\nphysiology data and tested its performance on learning behavioral data from the categorization\ntask. If this is the case, however, I do not find a big surprise in that the model could learn the\ncategorization task using its entire dataset. The authors should explain why this is non-trivial\nbefore they claim that the \"zero trained\" feature is an unexpected outcome of the model. The\ntraining conditions should also be explained more clearly.\n\nOur reply:\nWe apologize for not being clear. Our model was not trained on any data. Nonetheless,\nit matched real neurophysiological data. It even predicted a previously unknown neural\nproperty that we subsequently confirm in the data (see revised manuscript lines 370-\n386). It made a new discovery. We hope the reviewer agrees that this is non-trivial.\n\nReviewer 2:\n3. Potentially related to the above comment, I am not convinced of the importance of the\n\"primitive\", which is a modular block of small numbers of excitatory and inhibitory neurons (fig.\n1b). The computational benefit of this modular structure was not explored sufficiently in detail.\nThe authors hypothesized that these primitives exist in the cortical layer 2/3 and they perform\nWTA competition via lateral inhibition. Are these hypotheses supported by experimental\nevidence or just theoretical assumptions?\n\nOur reply:\nWe have clarified this and added details in the revised version of the manuscript. Our use\nof block of excitatory and inhibitory neurons is indeed supported by empirical evidence.\nWe used a set of mechanisms based on well-established anatomical and physiological\nrelations among excitatory pyramidal and local feedback inhibitory cells. The time\ncourse of their activity (rapid excitation followed by slower and longer-duration\nfeedback inhibition) and nonlinear computational functions are based on empirical\nobservations (e.g., Rudy et al., 2011; Tremblay et al., 2016). Further, studies show that\nthis anatomical arrangement is conserved across cortical regions (e.g., Pfeffer et al.,\n2013; Campagnola et al., 2022). The revised manuscript now describes these mechanisms and their\nempirical support in greater detail (revised manuscript lines 93-114 and 491-497, Fig 1b)\n\nReviewer 2:\n4. In ll. 277-281, the authors claimed \"Finally, we emphasize the broader point that it was the\nzero-trained nature of simulations based on biomimetic computation that enabled the\nemergence of these broad and unexpected findings. Whereas much computational work\nfocuses on the statistical learning of specific data, the present work instead attempts to uncover\nhow low-level physiological spiking, embedded in specific delimited sets of anatomical wiring\norganizations, can itself give rise to the forms of neural and behavioral results that brains\nproduce.\" However, I doubt whether this finding is as surprising as the authors claim. As\nmentioned in my comment 3, the authors hypothesized WTA competition for the primitives, a\nbasic computation essential for categorization tasks. Their large-scale model, therefore,\ninvolves a minimal circuit motif necessary to solve the task under consideration. When a large\nsystem contains a small system that can solve a task, I find no surprise that the large system\ncan also solve the same task.\n\nOur reply:\nWe appreciate the opportunity to highlight what makes our model unique. We have\nclarified these key points in the revised manuscript.\nFirst, our results extend beyond behavior. The model didn’t just solve the task—it did so\nin a brain-like manner. Despite not being trained on neurophysiological data, it replicated\nsuch data and even led to a novel discovery (revised manuscript lines 370-386).\nNotably, the model exhibits neural spiking patterns, local field potentials, inter-region\nsynchrony, and phase relations—all closely matching monkey physiology—none of\nwhich were used to construct or train the model (revised manuscript lines 363–369).\nThe local-feedback lateral inhibition circuit (L-FLIC) is not a simple winner-take-all\nnetwork. It is grounded in well-established neuroscience literature (revised manuscript\nlines 93-114).\nWe did not intend to suggest that the L-FLIC alone drives category learning. Other\nphysiological and computational components contribute, including working memory\nmechanisms, local field potential effects, and cross-area synchrony (revised manuscript\nlines 123-128, 219–225).\n\nReviewer 2:\n5. The A-correct, A-incorrect, B-correct, and B-incorrect neuronal responses should be defined\nexplicitly. What exactly can A-incorrect and B-incorrect neurons tell about the categorization of\ninput? Is it the probability that an input is unlikely to belong to A or B (i.e., the volume ratio\nbetween the pink shaded area vs its outside in Fig. S6)? Logically, X = not-A does not mean\nX=B, and similarly, X=not-B does not mean X=A. However, in the alternative choice task (e.g.,\nsuccade to A or B), not-A may behaviorally imply B as the animal can choose one of the two\noptions. How are the activities of the four functional subtypes combined to generate the model's\ndecision? The computational implications and importance of incongruent activities depend on\nhow the task was designed and how the neural responses were used to solve the task. I might\nfind these details somewhere in the manuscript, but they should be explained more carefully.\n\nOur reply:\nWe appreciate the reviewer’s insightful comments and have revised the manuscript\naccordingly. We now clarify the definitions of A-correct, A-incorrect, B-correct, and B-\nincorrect neurons, as well as the data analysis (revised manuscript lines 238–244, 278–\n285).\nThe prefix A or B denotes the stimulus category presented in a trial, while the suffix\ncorrect or incorrect indicates the trial outcome based on the subject’s response. For\nexample, A-incorrect means an A-type stimulus was presented, but the subject\nmistakenly responded with B instead of A.\nAnalysis of neural responses across these four trial types revealed distinct neurons\nselectively responding to one type but not the others—an emergent property of synaptic\nlearning. These neurons are not biologically pre-categorized but develop their selectivity\nthrough experience.\nNotably, incongruent neurons (ICNs), such as A-incorrect and B-incorrect, do not merely\nencode stimulus type but also reflect decision-making processes that unfold after\nstimulus presentation. Strikingly, ICNs spike selectively within the first 200 ms of a trial,\neffectively \"predicting\" an incorrect response before the decision is executed.\nFurther analysis confirms that ICN activity is not solely stimulus-driven; instead, these\nneurons are also tuned to the upcoming response outcome. While both congruent (A-\ncorrect, B-correct) and incongruent (A-incorrect, B-incorrect) neurons encode stimulus\ncategory (A vs. B), only ICNs exhibit selectivity for incorrect action selection in the\nstriatum. These findings are detailed in Supplementary Figure S10.\n\nReviewer 2:\n6. What conditions ensure the emergence of incongruent responses in the model? Do incongruent\nneurons play any role in switching between exploitation and exploration? How crucial is having\nsuch a functional subtype for task performance?\n- What conditions ensure the emergence of incongruent responses in the model?\n\nOur reply:\nIncongruent neurons (ICNs) emerge under specific conditions in the model, responding\nwhen a stimulus from one category is about to be incorrectly classified as the other. In\nother words, they predict an upcoming incorrect choice long before it occurs within the\ntrial.\n\nReviewer 2:\nDo incongruent neurons play any role in switching between exploitation and exploration?\n\nOur reply:\nThe reviewer’s insights are correct.\nICNs predict an impending incorrect action, suggesting the animal is \"choosing\" a\nresponse that may not be in its best interest. However, these neurons could become\nvaluable if environmental changes require a shift to exploration. In essence, they may\nserve as a reserve mechanism, allowing the network to escape local exploitation minima\nwhen exploration becomes advantageous.\nWe have added a brief discussion on this point in the revised manuscript and will explore\nit further in future work. (lines 379-386)\n\nReviewer 2:\nHow crucial is having such a functional subtype for task performance?\n\nOur reply:\nICN activity corresponds with pronounced local field potential (LFP) effects, prominently\nobserved in nonhuman primate (NHP) cortico-striatal synchrony (see Figure 3a). This\nsuggests ICNs play a key role in large-scale cortical-striatal signaling.\nWe have included this in the revised manuscript (lines 372-386). As the reviewer noted,\nICNs may also influence exploration vs. exploitation, a topic we will address in future\nwork.\n\nReviewer 2:\n7. I wonder whether references are cited correctly (for example, refs 16-18). Please check the\nreferences.\n\nOur reply:\nThanks you for noting this. This has been corrected in the revised manuscript lines 326-\n332.\n\nReviewer 2 - Minor comments:\n8. How is the speed of learning compared with the experimental observation?\n\nOur reply:\nAs the reviewer suggests, the model’s learning rate aligns with behavioral learning in\nmacaques. It reaches 80% accuracy within 100–400 trials and maintains this level for the\nremainder of the session (add to manuscript lines 167–170).\n\n9. Are the responses of TANs crucial for switching exploitation and exploration?\n\nOur reply:\nA very useful observation. TANs play a direct role in the model’s shift from exploration to\nexploitation. Early in learning, TAN activity introduces stochasticity to medium spiny\nneurons (MSNs) in the striatum, promoting exploration. As learning progresses,\nstriosomal (patch) MSNs increasingly inhibit TANs, reducing stochastic influence and\nmaking MSN activity more reliant on cortical input. This marks the transition to\nexploitation, where the model reliably associates input features with specific behaviors.\nThis progression is now detailed in Supplementary Figure S3 and manuscript lines 147–\n153.\n\n10. In Fig. 2b, what <W> means? Does it refer to averaging over neurons? I guess some\nconnections are strengthened, as shown in the figure, but the other connections are weakened\neven if their overall average increases during learning.\n\nOur reply:\nThe <W> in Figure 2b represents the average synaptic weight across all connections\nbetween specified brain regions: cortico-cortical (top panel), cortico-striatal (second\npanel), and thalamo-cortical (third panel). This clarification has been added to the Figure\n2b caption. The analysis functions as the reviewer suggested.\n\n11. The limitations of the model should also be discussed briefly.\n\nOur reply:\nWe now do. Our model is meant to be first steps in using biomimetic model by\nelimination artificial neural network data-learning, focusing instead on emergent\nphysiological properties.\nBut as a first step, it needs further development in the future. This includes incorporating\nadditional key structures, such as the amygdala, hippocampal circuits, and specific\nthalamic details. This discussion has been added in lines 387–403 of the revised\nmanuscript.\n\nReviewer #3 (Remarks to the Author):\nThis study proposes a computational model that can simulate multiple brain regions (cortex,\nstriatum and thalamus) associated with perceptual decision-making and replicates both spikes\nand LFPs, which have been experimentally observed. As most brain areas do not work alone, it\nis imperative that we simulate interareal interactions. In this study, the authors propose to\ncombine population models and spiking neural networks to reduce the complexity of multi-areal\nmodels, which is an interesting approach. As they constrain the model’s structure using\nexperimental data, their study may provide a good reference model for those who develop multi-\nareal models.\nThis model aims to replicate the recorded neural responses, while NHPs are performing a\nsimple visual categorization task. Since the task is quite simple, no significant learning is\nnecessary, but the authors implied that their model can provide insights into the learning\nprocess in the brain.\nIn my opinion, the value of this model is that we could probe functional roles of brain areas on\nvisual categorization using their model. Thus, I would like to ask the authors if they tried to\nquantitatively evaluate the influence of individual brain areas. If one of the areas is disabled,\nneural and behavioral responses may fail or differ from the observed responses. It may be\npossible to gain new insights into perceptual decision-making from these failure modes.\n\nOur Reply:\nWe strongly agree with these comments. Adding experiments to test simulated lesions\nwould significantly enhance the existing work, and we are already planning future\nstudies to explore this in more detail.\nFor now, we have incorporated several new observations on model lesions based on the\nreviewer’s suggestions (revised manuscript lines 215-218).\nA new section (Section VI) has been added to the Supplemental Material, systematically\nanalyzing the effects of disabling three key circuit elements: the SNc, striosomal\ncomponents of the striatal complex, and TAN neurons. Their functional roles are\ncomplex, and our new analysis provides further insight. We have expanded\nSupplementary Figure S3, and added three new figures (S12, S13, and S14), to illustrate\nthese findings.\nKey Findings:\nSNc Disablement: Corticostriatal synapses gradually weakened, leading MSNs to rely\nmore on inputs from TANs rather than cortical inputs (Figure S12).\nStriosomal Disablement: Learning no longer influenced TAN activity, causing\nperseverative behavior, where the model produced the same response regardless of\ninput (Figure S12). Striosomal neurons project to both TANs and SNc via inhibition. As\npreviously shown, striosomal suppression of TANs shifts behavior from exploration to\nexploitation (Supplementary Figure S3, revised manuscript lines 149–153).\nTAN Disablement: Effects varied based on synaptic learning parameters. When learning\nparameters was reward-biased, TAN disablement led to perseverative behavior. However,\ndisrupting TANs had no effect when learning parameters was punishment-biased. This\nsuggests possible redundancy between the TAN and SNc systems, where SNc may\ncompensate for TAN disruption to maintain learning (Supplementary Figure S13, 14 and\nSupplementary Materials lines 118–126).\nThese findings suggest new experimental tests, which we plan to explore in future work.\n\nReviewer 3:\nThe authors claimed that their model can predict the existence of incongruent neurons, which\nare experimentally confirmed. However, I am concerned that this incongruent neuron could be\njust a byproduct of winner-take-all mechanisms. Let’s consider three sets of neurons. Stimulus\nA, Stimulus B and none-of-the-above (NOA). Due to lateral inhibition, we expect only one of the\nthree sets to be active at a time. In addition, we can further assume that Stimulus A or B is\ntrained to become quiescent when an incongruent stimulus is presented. That is, when stimulus\nA is presented, Stimulus B neurons become silent, and vice versa. Then, if Stimulus A fails to\nactivate Stimulus A neurons, NOA neurons will be active due to the lack of lateral inhibition,\nwhich will be consistent with incongruent neuron behaviors. Based on this line of reasoning, I\nam not sure what we can learn from their model’s predictions.\n\nOur reply:\nThe reviewer raises an important thought experiment regarding incongruent neurons\n(ICNs). While ICNs could have arisen from lateral inhibition, their behavior extends\nbeyond simply signaling the opposing category. They selectively activate when an\nincorrect behavioral response is about to occur.\nIn other words, ICNs do not merely represent a “wrong” or “leftover” category due to\nlateral inhibition—they actively predict an impending incorrect choice. This is a novel\ndiscovery, confirmed by neurophysiological recordings in nonhuman primates (NHPs).\nFurther analysis of model ICNs revealed that synaptic receptive fields in cortical cells\nundergo differential training via striatal-thalamo-cortical feedback, aligning correct\ncortical activity with incorrect striatal action-selection (MSN) responses.\nThis is now discussed in detail in Figures 4, Supplementary Figures S8–S10, and revised\nmanuscript lines 226–289, 370–378. Additionally, as suggested by Reviewer 2, we\npropose that ICNs may also play a role in exploration vs. exploitation (manuscript lines\n379–386).\n\nReviewer 3:\nIn the Results section, the main cortical area of interest is \"anterior cortex\", but Methods section\ndescribes \"prefrontal cortex\", not anterior cortex. As the cortex model relies on a generic\nstructure, such mix-up may not matter too much, but to maintain clarity of this study and not to\nconfuse the readers, this mix-up should be fixed properly.\n\nOur reply:\nThe reviewer is providing a very useful comment, and we have carefully changed the\nrevised manuscript to solely refer to ‘anterior cortex’ in the model.\n\nReviewer 3:\nIn lines 18-25, the author stated: “However, most of these models have been optimized for\neither mechanistic accuracy or to elicit emergent cognition, but not both. On the one hand, there\nare powerful models with biologically-based systems that reproduce extensive physiologies,\nfrom single-cell spiking through generation of oscillations and other critical emergent dynamics.\nYet, while these can accurately simulate brain-wide phenomena (7) such as epileptic seizure\ngenesis and propagation (8, 9), they are not designed to generate cognitive processes, such as\nlearning. On the other hand, some of the most remarkable cognitive models are those grounded\nin artificial neural networks (10–15). Yet, while this is a class of models that effectively “learn,”\nthey do so without reliance on biologically realistic (biomimetic) mechanisms at the cellular\nscale.”\n\nI believe these statements are not accurate. A line of study uses biologically-realistic circuits to\nprobe neural correlates of high-level cognitive functions such as decision-making, working\nmemory and integration of sensory evidence. Also, all major simulation platforms, such as\nNEST and NEURON, support long-term synaptic plasticity and can be used to study ‘learning’ in\nthe brain. The general learning algorithm for spiking neural networks, which corresponds to\n'backpropagation' in deep learning, remains missing, but the lack of learning algorithm must not\nbe confused with the lack of study of biological learning.\n\nSelected References\nGoldman, M. S., Compte, A., & Wang, X. J. (2009). Neural Integrator Models. In Encyclopedia\nof Neurosci (pp. 165-178). Elsevier Ltd. https://doi.org/10.1016/B978-008045046-9.01434-0\nWang XJ. Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed\nCognition. Annu Rev Neurosci. 2022 Jul 8;45:533-560. doi: 10.1146/annurev-neuro-110920-\n035434. PMID: 35803587.\nWang XJ. Decision making in recurrent neuronal circuits. Neuron. 2008 Oct 23;60(2):215-34.\ndoi: 10.1016/j.neuron.2008.09.034. PMID: 18957215; PMCID: PMC2710297.\nHill S, Tononi G. Modeling sleep and wakefulness in the thalamocortical system. J\nNeurophysiol. 2005 Mar;93(3):1671-98. doi: 10.1152/jn.00915.2004. Epub 2004 Nov 10. PMID:\n15537811.\nWagatsuma N, Potjans TC, Diesmann M, Fukai T. Layer-Dependent Attentional Processing by\nTop-down Signals in a Visual Cortical Microcircuit Model. Front Comput Neurosci. 2011 Jul\n8;5:31. doi: 10.3389/fncom.2011.00031. PMID: 21779240; PMCID: PMC3134838.\n\nOur reply:\nThe reviewer’s comments are highly valuable. We fully agree that various simulation\nplatforms and studies have been effectively used to investigate synaptic plasticity,\nlearning, and network dynamics.\nWe have revised the manuscript to acknowledge these and other earlier works, including\nall of these cited by the reviewer, which emphasize balancing mechanistic accuracy with\nemergent cognition (manuscript lines 25–28). Additionally, we have expanded the\ndiscussion on this topic (lines 326–340).\nOur work specifically highlights the complete absence of artificial neural network\nlearning (e.g., backpropagation), replacing it with a more biologically grounded\napproach, while acknowledging extensive work in the existing literature on biological\nlearning. We now make this clear.\n\nReviewer 3:\nI strongly recommend the authors to remove the statements regarding ‘zero-trained’ and clarify\nwhat their proposed model actually achieves. The authors are implying that their proposed\nmodel can perform zero-shot learning (zero-trained) or make predictions on novel inputs (out-of-\ndistribution inference). While zero-shot learning and out-of-distribution are important topics in\nthe field of deep learning, the authors do not provide any results relevant to zero-shot learning\nand out-of-distribution inference in their study.\n\nOur reply:\nWe have revised the manuscript per the reviewer’s suggestions.\nTo clarify, the manuscript demonstrates that the model can make predictions and\nperform “out-of-distribution” tasks despite having no training on animal data—literally\nzero training. The reviewer correctly notes that \"zero-shot\" and \"zero-training\" learning\ntypically refer to deep learning. However, our model exhibits this behavior, as it can\nperform tasks without any prior training. We should have made this clearer in the\noriginal manuscript. Now, we do. We now state this explicitly (see revised manuscript\nlines 36–42 and 404–415).\n\nReviewer 3:\nAlso, the authors need to clarify what ‘multi-scale’ means in their manuscript. Based on the\nphrase, “from spiking neurons to local field potentials”, they appear to be referring to both\nstructure (e.g., neurons or brain areas) and neural responses (e.g., spikes and LFPs), but to\navoid confusion, they should clarify what they mean.\n\nOur reply:\nWe appreciate the opportunity to clarify these statements. We refer to both structures\nand activities. The work spans multiple structural scales by anatomically incorporating\ndifferent neuron types, wired into assemblies with distinct wiring diagrams. This, in turn,\ngives rise to spikes, local field potentials (measured across cell neighborhoods), regional\nsynchrony, and phase relationships among regions—phenomena that can only be\nmeasured across multiple areas rather than locally. We have revised the manuscript\n(lines 74–79) to state this more clearly.\n\nReviewer 3:\nIn line 106, the authors stated: “The arrangement used here is very distinct from ANN\napproaches”\nANN has not been defined. If ANN means artificial neural networks, the authors should define\nwhat kind of ANN approaches they are referring to.\n\nOur reply:\nThank you for noticing this. To avoid the unclear reference, the phrase that the reviewer\ncites has been changed to eliminate “ANN” ; it now reads: “The arrangement used here\nis notably distinct from many other approaches that have been used to generate lateral\ninhibition.” (revised manuscript line 96).\n\nReviewer 3:\nIn lines 126-128, the authors stated: “The interconnections between these subcircuits support\nthe process of learning. As the model experiences many trials, there are changes to synaptic\nstrength for cortico-cortical, cortico-striatal, and thalamo-cortical contacts that cause changes in\nbehavior”\nI think the authors should show how these interareal connections evolve over time. As the\nmodel depends on various cell assemblies, they should show the evolution of weights between\ncell assemblies.\n\nOur reply:\nWe very much agree with this suggestion. We have now added a new supplemental\nfigure S11, which gives extensive descriptions and illustration of the changing evolution\nof weights between cell assemblies.\n\nReviewer 3:\nIn line 156, the authors stated: “For the simulation, we developed a similar metric called Local\nSpike Summation (LSS), a simple measure of simulated neuronal activity defined in Method”\nEarlier works simulated LFPs in multiple ways, but LFPs are generally thought to reflect\nsynaptic inputs, whereas LSS reflect outputs of neurons. The authors should, at least, justify\nwhy they used LSS to approximate LFPs. As their proposed model aims to produce multi-scale\nresponses, it is important to simulate LFPs as accurately as possible.\n\nOur reply:\nWe agree and have clarified this further (manuscript lines 769–781) per the reviewer’s\nsuggestion.\nMany LFP approximation methods (e.g., Mazzoni 2015) convolve EPSPs over spatial\nfilters. Our goal was to relate individual cell responses to LFPs and measure synchrony\nand phase relationships among fields. To achieve this, we used LSSs, which combine\nindividual cell responses. To confirm their validity, we demonstrated similarities between\nmodel LSSs and empirical LFPs.\n\nReviewer 3:\nIn line 158, the authors stated: “one noticable difference is that NHP brains do not show as\nmarked a decrease in spiking after stimulus offset as the model (Figure 2C)”\nCan the authors provide any insights into this discrepancy? Alternatively, can they explain why it\nis not important?\n\nOur reply:\nThis is a fair point. Models inevitably differ from the actual brain, and we emphasize\nthese differences to highlight features that future work may need to address.\nIn this case, real PFC neurons receive substantial input from multiple cortical and\nsubcortical regions, which are not modeled here. The model has no extraneous inputs or\nartificially injected noise, so some discrepancies between empirical and model data are\nexpected. These differences may be meaningful, and further studies are underway.\nThat said, we observed notable correspondences in inter-area cortico-striatal\nsynchronies and their changes in both the model and macaques. These points are\ndiscussed in manuscript lines 185–189.\n\nReviewer #4 (Remarks to the Author):\nThis is an exciting paper, mainly because of the discovery in the model of a type of neural\ncoding that was unknown before and is found in their data after its discovery in the model. This\npopulation of neurons codes for when the choice of response is going to be incorrect.\n\nOur reply:\nWe very much appreciate these positive statements from the reviewer.\nSince I took the theory track at Cornell, I never got diffEQ, so I did not check the equations\ncarefully, but I will note that they switch in the article between W^{ij} meaning the weight from\nunit j to unit i (e.g., line 319) to vice versa (e.g., line 520) and back (line 99, suppl. mat.). Be\nconsistent.\n\nOur reply:\nWe appreciate the reviewer catching this typo. It has now been fixed (see revised\nmanuscript lines 675 and 679)\n\nReviewer 4:\nMy main problem with the paper is that this type of model has been done before, perhaps not\nwith such exacting precision with respect to the neurophysiological data, but the spirit is the\nsame, by Randy O’Reilly and Yuko Munakata (2000 and subsequent versions), and there is no\nreference to this work. In that book, they simulate learning a go-no go task with a model basal\nganglia using the Emergent simulator. Similar to the current model, the system is wired up in the\nsame manner as the basal ganglia, and it is trained by the reinforcement learning system\nintrinsic to the model. Similarly, there’s no reference to Chris Eliasmith’s work with SPAUN.\n\nIn addition, you say \"those models were designed to elicit a circumscribed family of behaviors\ncentered on reinforcement learning and were not constructed in a manner that allows them to\nbe easily extended beyond their original scope.\" This is certainly not true of Eliasmith's model,\nwhich shows the ability to learn seven different tasks. And while I haven't read all of them,\nO'Reilly, especially with Michael Frank, has published numerous papers on his model's\nexplanation of working memory, etc.\n\nI'm sure there are many important differences (for example, your model includes oscillatory\nbehavior, crucial to some aspects of the model, which differs from O'Reilly's model if I recall\ncorrectly). However, it is still necessary to distinguish this model from those earlier works.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that\nemerged from the simulations. However, I would like to see how it relates to earlier models in\nthe same vein. If that issue were addressed, I think this rates as publishable in Nature\nCommunications.\n\nOur reply:\nWe fully agree. Incorporating O’Reilly’s and Eliasmith’s work is valuable, as our model\nbuilds on their contributions. Both Emergent and Spaun were groundbreaking in\nadvancing computational principles in neural systems. The revised manuscript now\nreferences and discusses Emergent and Spaun and discusses how our model serves a\nbridging role (revised manuscript lines 25-26, 301–325).\n\nReviewer 4:\nWhat would really nail the data would be if you can use your idea of promoting learning by\nstopping trials that the incongruent neurons predict will be an error in an actual monkey. I don't\nexpect that, given the difficulty, expense, and time necessary, but wow, if you did that, it would\nbe a paper for the ages.\n\nOur reply:\nWe (of course) love this idea. In fact, we aim to do just that in future experiments.\nMinor comments.\nReviewer 4:\nMore specific comments/wording suggestions/typos/notes:\nIntroduction: there is no reference to any of Randy O’Reilly’s work??\n\nOur reply:\nIndeed, our mistake. O’Reilly’s work is highly relevant. We have added a substantial\ndiscussion of O’Reilly’s, as detailed above.\n\nReviewer 4:\n“These latter types of predictions are said to be zero-trained.”\nI’ve never heard the concept of “zero-trained” - I’ve heard of zero-shot. If you are going to\nintroduce new terminology, say instead “we call these models “zero-trained””.\nHowever, that seems like an anomaly, since you do train it - by reinforcement learning, which is\nbuilt into the system. I think a better description would be to call it “intrinsically-trained”. But I will\nleave that up to the authors.\n\nOur reply:\nWe agree with the reviewer and have added the phrase “we term these models ‘zero-\ntrained’ ” (e.g., revised manuscript line 37).\nZero-trained emphasizes a crucial point: the model was not trained on any\nneurophysiological data (i.e., zero), yet its activity closely matches neurophysiological\nrecordings from NHPs. This contrasts with models trained on such data and tested on\nheld-out samples. Our model goes beyond data-fitting approaches, by generating\nneurophysiological-like activity de novo.\nWe did not make this explicit in the original manuscript but have now done so (revised\nmanuscript lines 36–42 and 404–415).\n\nReviewer 4:\nline 91: lateral inhibitation -> lateral inhibition\nFig1 caption: Detailed discussion in provided in the Methods\n-> Detailed discussion is provided in the Methods\n\nOur reply:\nThank you: typo has been corrected.\n\nReviewer 4:\nline 155: no need for scare quotes around local field potentials\nline 160: no need for scare quotes around phase locking value.\nThere may be other examples of this.\n\nOur reply:\nWe agree. Done.\n\nReviewer 4:\nline 161: reference 20: EG Antzoulatos, EK Miller (2014). Glancing at this paper, I take it that\nthe strength of connections between the two areas (striatum and PFC) increase overall - i.e.,\nboth ways. In Figure 3b, you also show this connectivity increasing, but it isn't clear whether this\nis from stratum to your simulated frontal lobes or vice-versa. Can you be more specific than\n\"portico-striatal\"? Does this graph work both ways?\n\nOur reply:\nThis is a helpful clarifying question. In Figure 3b, we show the increase in synaptic\nweights from anterior cortical to striatal cells, separately displayed because they are\nmodulated by reward-driven dopamine. Other connections also change, as the reviewer\nnotes. Both cortico-cortical and thalamo-cortical synapses adapt via their internal\nplasticity rules, as shown in Figure 2b. Figure 3b specifically illustrates cortex-to-\nstriatum connections—this is now clarified in the caption.\n\nReviewer 4:\nLine 186: Commprising -> Comprising\nFigure 3A caption: highlighted in gray, not yellow. The colors of the lines are incorrectly\nspecified (they are blue and green, not red and light red).\nFigure 3B Figure: Correct me if I'm wrong, but you seem to have incorrect and correct flipped\nhere.\n\nOur reply:\nThank you. The typos have been corrected.\n\nReviewer 4:\nLast line of Figure 4 caption: The sentence says “would improve” - so this is a prediction. If you\nwere able to do this experiment, it would be especially confirmatory, although I know\nexperiments with NHPs are expensive and time consuming.\n\nOur reply:\nIndeed. That would be great. It is in our future plans after we secure funding for further\nexperiments.\n\nReviewer 4:\nLines 251-252 - please remove the scare quotes.\nline 269: NHPs are not “constructed”! Try “evolved”.\n\nOur reply:\nDone.\n\nReviewer 4:\nLine 285 and others: You describe this as “working memory”? Can you elaborate on why you\ncall it that?\n\nOur reply:\nWe use working memory because the task includes a short (1000 ms) delay, requiring the\nNHP to retain cued information. This aligns with a large body of neurophysiological\nresearch on working memory, which we now clarify in the revised manuscript (see lines\n193-203, 688-689).\nIn equations 30 and 31, I believe you have switched from W^{ij} meaning the weight from j to i\n(as in equation 3) to the reverse. Hopefully this is correct in the code!\n\nOur reply:\nThis was indeed a typo (only in the text, not in the code). It has now been fixed. Thanks.\nIt is unclear what is going on in equation 29, as there is only one index on the weight, that is, “i”,\nwhich suggests this is a solely pre-synaptic learning rule, unlike the rule in equation 28. Explain\nthis, or fix it.\n\nOur reply:\nThis does require further explanation: equation 29 is a specific form of Hebbian learning\n(derived from eq. 28). There are in fact two indices in eq.29; one is superscript “i”\nwhereas the other is subscript “x”. The “i” refers to the presynaptic “i-th” cortical\nneuron, while the “x” refers to the label of the target striatal block (the values of x in the\ncurrent model are simply 1 or 2). We use a different index convention for striatum\nbecause the striatal block in the current simulation is implemented differently from\ncortex, as described in equations 12 and 17. We have added new additional text in the\nrevised ms (lines 656-658) to clarify these points.\nEquation 33 reverts to the equation 3 definition of which way the weight W^{ij} goes.\n\nOur reply:\nYes, this is in fact the correct convention, and we now have corrected the other\ninstances of this to conform, as mentioned in responses to previous comments above.\nLine 501: you need a reference for the BCM rule. Bienenstock, Cooper, and Munro 1982.\n\nOur reply:\nWe have added this reference – thank you (revised manuscript reference 98).\nLine 502: typo: “formas” either this is supposed to be “form as” or “formulas”\n\nOur reply:\nThank you. This typo has now been corrected. It should be “form as”.\n\nReviewer 4:\nIn the Computational Methods section (starting line 547), you don’t mention Chris Eliasmith’s\nSPAUN model using the Semantic Pointer Architecture. How does NeuroBlox differ from his\nsimulator, which seems to be addressing very similar concerns and uses actual spiking\nneurons. Or alternatively, how does it differ from Emergent (O’Reilly)?\n\nOur reply:\nIndeed. We now correct this by adding reference to both SPAUN and Emergent in\nComputational Methods section (revised manuscript line 710). As detailed above, we\nhave also added a discussion of how our model bridges between these approaches and\nneurophysiological experiments (revised manuscript lines 301-325).\n\nReviewer 4\nSupplementary Material\nLine 8: (NG-NMM) model, -> mass model (NG-NMM),\nLine 24 Supplimental -> Supplemental\n\nOur reply:\nFixed. Thanks.\n\nReviewer 4:\nFigure S5 caption: \"Striatal spiking shows a post-stimulus-onset silence that was subsequently\nfound in empirical data;\" This seems like a second discovery. Why don't you also emphasize\nthis in the main text? Here, in terms of \"silence\", are you referring to the pause in spiking around\n40-60ms? This appears to be borne out in the local spike summation, although the timing is not\nquite matched by the empirical data. The match between D and E in this figure is pretty cool;\nagain, it seems like a second finding that you might push in the main text.\n\nThe mechanical explanation that you later derive in the supplemental material should be better\nreflected in the main text.\n\nOur reply:\nWe appreciate this. We have added this observation to the main text. (see manuscript\nlines 160-163).\n\nReviewer 4:\nReferences\nEliasmith et al. (2012) A Large-Scale Model of the Functioning Brain. Science.\nO'Reilly & Frank (2006) Making working memory work: a computational model of learning in\nthe prefrontal cortex and basal ganglia. Neural Computation. [And many other papers with\nMichael Frank]\nO'Reilly & Munakata (2000) Computational Explorations in Cognitive Neuroscience:\nUnderstanding the Mind by Simulating the Brain. Cambridge: MIT Press\n\nOur reply:\nAgreed – description and discussion of this work is included in the revised manuscript,\nand these citations are now included in our references."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I appreciate the authors’ efforts to clarify my concerns, and they successfully addressed most of my concerns. Consequently, the clarity of the manuscript has been much improved, and now I have a much better understanding of the model and find some results, for instance, the finding of incongruent neurons, are interesting. Training a large-scale model of cortico-basal-ganglia-thalamic neural networks on a cognitive task (categorical learning) is a non-trivial problem, and the obtained results are consistent with experimental observations. Therefore, the manuscript may eventually be published. The model (and its future extensions) will also provide a platform to examine biological hypotheses in silico.\n\nHowever, I strongly doubt whether the model achieved “zero-shot learning\". Indeed, I wonder whether the zero-shot learning claimed in the manuscript is a well-defined theoretical concept. I feel the claim only causes unnecessary confusion. The authors said that the model has not been pre-trained on a similar task and hence achieves zero-shot learning. However, cortico-striatal and thalamo-cortical synapses of the model undergo long-term modifications implementing reinforcement learning. I feel that the authors' thought behind the \"zero-shot learning\" is that the model should be able to perform the same cognitive task as the brain can do so, if the model copies the biological circuits accurately enough. This naive view may hold in principle. However, the binary categorization task demonstrated in the paper is just a simple example of various cognitive tasks, and the model contains the crucial component (e.g., lateral inhibition between \"primitives\") for solving the particular task. The authors' claim about \"zero-shot learning\" should be removed from the manuscript before acceptance.\n\nIn addition, whether \"primitives\" (i.e., small-scale cortical functional units) are necessary for solving the task or improving the model's performance has not been addressed. What would happen if the cortical network model did not have the subnetwork structure? Although I do not insist that clarifying this point is necessary for the acceptance of the manuscript, it is regrettable that we cannot see whether the complex model describes the minimal network structure required for solving cognitive tasks (or more specifically, for generating incongruent neurons).", "reviewer_3": "The authors have significantly improved the manuscript, and I believe this model, as it is, can serve as a reference for future studies.\n\nI do have one more suggestion. In the model, there is only one generic type of inhibitory neurons, but recent studies found multiple functional types of inhibitory neurons and cell-type specific connections in cortical areas (see references below, for instance). If the authors could discuss potential links between the model and the observed cell-type specific connectome, I believe it can further strengthen the study.\n\nPfeffer, C., Xue, M., He, M. et al. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci 16, 1068–1076 (2013). https://doi.org/10.1038/nn.3446\nJiang X, Shen S, Cadwell CR, Berens P, Sinz F, Ecker AS, Patel S, Tolias AS. Principles of connectivity among morphologically defined cell types in adult neocortex. Science. 2015 Nov 27;350(6264):aac9462. doi: 10.1126/science.aac9462. PMID: 26612957; PMCID: PMC4809866.\n\nThere is a typo in line 279; “Eventhough”.", "reviewer_4": "This is an exciting paper, mainly because of the discovery in the model of a type of neural coding that was unknown before and is found in their data after its discovery in the model. This population of neurons codes for when the choice of response is going to be incorrect.\n\nThe paper now reviews SPAUN and Emergent, so they responded to that critique.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that emerged from the simulations. I noted in my previous review that of the issue of related models were addressed, then I think this rates as publishable in Nature. So, I recommend its publication.\n\nMore specific comments/wording suggestions/typos/notes:\nThis time around, I found the description of the striatal sub circuit and the TAN sub circuit to be confusing. The TAN is just one blob in the striatal sub circuit - wouldn’t it be better to simply refer to it as part (in the model, if not in reality) of the striatal sub circuit. And why not just call this the Basal Ganglia?\n\nLine 24: “predetermined mathematical systems” What do you mean by “predetermined”? There must be some other word to describe your intent here.\n\nFigure 1. You define all of the different acronyms except MSN (medium spiny neurons) which doesn’t appear until line 135. Define it in the caption.\n\nline 118: You mention something will be illustrated by voltage/time graphs, but don’t give a pointer - I believe you’re referring to Figure S1A?\n\nline 279: Eventhough -> Even though\nLine 310: arecharacterized -> are characterized\nLine 355: backprop is used throughout neural network land, including self-supervised or unsupervised learning; it isn’t only used for supervised learning.\nLine 356: The sentence starting with “Confusingly…” is a non sequitur. Either provide some linking sentence or (better) start a new paragraph.\nLine 388: physiolgy -> physiology\n\nIn the Methods section, you describe some properties of the circuits without giving any references for their origin. Since this is all supposed to be based on evidence, be sure to cite the evidence. This should be true anywhere you describe a gross circuit physiology. For example, at the end of the paragraph from 486-491, you mention that “the connection probability for cortico-cortical connection is 8%.” What evidence is this based on? Other examples include the stipulation that every neuron between the SVCR and the AC has the same number of outgoing as incoming connections, some properties of the striatum, etc.\n\nAgain, I did not check the equations carefully; my previous comments about these were responded to.\nSigned,\nGary Cottrell"}, "author_response": "Reviewer #2 (Remarks to the Author):\nI appreciate the authors’ efforts to clarify my concerns, and they successfully addressed most of my concerns. Consequently, the clarity of the manuscript has been much improved, and now I have a much better understanding of the model and =ind some results, for instance, the =inding of incongruent neurons, are interesting. Training a large-scale model of cortico-basal-ganglia-thalamic neural networks on a cognitive task (categorical learning) is a non-trivial problem, and the obtained results are consistent with experimental observations. Therefore, the manuscript may eventually be published. The model (and its future extensions) will also provide a platform to examine biological hypotheses in silico.\n\nOur reply:\nWe appreciate the positive comments and support.\n\nReviewer 2:\nHowever, I strongly doubt whether the model achieved “zero-shot learning\". Indeed, I wonder whether the zero-shot learning claimed in the manuscript is a well-de=ined theoretical concept. I feel the claim only causes unnecessary confusion. The authors said that the model has not been pre-trained on a similar task and hence achieves zero-shot learning. However, cortico-striatal and thalamo-cortical synapses of the model undergo long-term modi=ications implementing reinforcement learning. I feel that the authors' thought behind the \"zero-shot learning\" is that the model should be able to perform the same cognitive task as the brain can do so, if the model copies the biological circuits accurately enough. This naive view may hold in principle. However, the binary categorization task demonstrated in the paper is just a simple example of various cognitive tasks, and the model contains the crucial component (e.g., lateral inhibition between \"primitives\") for solving the particular task. The authors' claim about \"zero-shot learning\" should be removed from the manuscript before acceptance.\n\nOur reply:\nThese are very helpful points. We have entirely removed any claims about “zero shot learning” from the manuscript.\n\nReviewer 2:\nIn addition, whether \"primitives\" (i.e., small-scale cortical functional units) are necessary for solving the task or improving the model's performance has not been addressed. What would happen if the cortical network model did not have the subnetwork structure? Although I do not insist that clarifying this point is necessary for the acceptance of the manuscript, it is regrettable that we cannot see whether the complex model describes the minimal network structure required for solving cognitive tasks (or more speci=ically, for generating incongruent neurons).\n\nOur reply:\nWe agree with the reviewer that understanding how the subnet structure contributes to model function is an important question. Our ongoing work is addressing this by varying the contribution of different primitives and identifying the minimal required structure. However, the necessary analyses and Iigures are beyond the scope—and likely the word and Iigure limits—of this manuscript. This will be the focus of our next paper.\nReaders may have the same question as the reviewer. Thus, we added text to the revised manuscript (lines 121-131) that raises these questions and how they can be addressed in future work.\n\nReviewer #3 (Remarks to the Author):\nThe authors have signi=icantly improved the manuscript, and I believe this model, as it is, can serve as a reference for future studies.\n\nOur reply:\nWe thank the reviewer for helpful comments. They improved our manuscript.\n\nReviewer 3:\nI do have one more suggestion. In the model, there is only one generic type of inhibitory neurons, but recent studies found multiple functional types of inhibitory neurons and cell-type speci=ic connections in cortical areas (see references below, for instance). If the authors could discuss potential links between the model and the observed cell-type speci=ic connectome, I believe it can further strengthen the study.\n\nPfeffer, C., Xue, M., He, M. et al. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci 16, 1068–1076\n(2013). https://doi.org/10.1038/nn.3446\nJiang X, Shen S, Cadwell CR, Berens P, Sinz F, Ecker AS, Patel S, Tolias AS. Principles of connectivity among morphologically de=ined cell types in adult neocortex. Science. 2015 Nov\n27;350(6264):aac9462. doi: 10.1126/science.aac9462. PMID: 26612957; PMCID: PMC4809866.\n\nOur reply:\nThis is very helpful. Per the reviewer’s request, we have referenced this work and added a discussion of potential links between the model and the observed cell-type speciIic connectome (lines 415 -424).\n\nReviewer 3:\nThere is a typo in line 279; “Even though”.\n\nOur reply\nThank you. This has been corrected.\n\nReviewer #4 (Remarks to the Author):\nThis is an exciting paper, mainly because of the discovery in the model of a type of neural coding that was unknown before and is found in their data after its discovery in the model. This population of neurons codes for when the choice of response is going to be incorrect.\n\nOur reply:\nThank you.\n\nReviewer 4:\nThe paper now reviews SPAUN and Emergent, so they responded to that critique.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that emerged from the simulations. I noted in my previous review that if the issue of related models were addressed, then I think this rates as publishable in Nature. So, I recommend its publication.\n\nOur reply:\nThank you for the excellent suggestion.\n\nReviewer 4:\nMore speci=ic comments/wording suggestions/typos/notes:\nThis time around, I found the description of the striatal sub circuit and the TAN sub circuit to be confusing. The TAN is just one blob in the striatal sub circuit - wouldn’t it be better to simply refer to it as part (in the model, if not in reality) of the striatal sub circuit. And why not just call this the Basal Ganglia?\n\nOur reply:\nThis is very helpful. Per the reviewer’s suggestion, we now use the terms striatal sub circuit and Basal Ganglia to simplify the description. We agree that this is clearer.\n\nReviewer 4:\nLine 24: “predetermined mathematical systems” What do you mean by “predetermined”? There must be some other word to describe your intent here.\n\nOur reply:\nThis was indeed confusing. We now simply say “other related algorithms” (line 24).\n\nReviewer 4:\nFigure 1. You de=ine all of the different acronyms except MSN (medium spiny neurons) which doesn’t appear until line 135. De=ine it in the caption.\n\nOur reply:\nWe now deIined it the Iirst time we use the acronym. Thanks.\n\nReviewer 4:\nline 118: You mention something will be illustrated by voltage/time graphs, but don’t give a pointer - I believe you’re referring to Figure S1A?\n\nOur reply:\nWe Iixed this typo. The voltage time graph actually refers to Fig 1 b.\n\nReviewer 4:\nline 279: Eventhough -> Even though\n\nOur reply:\nThank you. This has been corrected.\n\nReviewer 4:\nLine 310: are characterized -> are characterized\n\nOur reply:\nThank you. This has been corrected.\n\nReviewer 4:\nLine 355: backprop is used throughout neural network land, including self-supervised or unsupervised learning; it isn’t only used for supervised learning.\n\nOur reply:\nThank you. This has been corrected.\n\nReviewer 4:\nLine 356: The sentence starting with “Confusingly…” is a non sequitur. Either provide some linking sentence or (better) start a new paragraph.\n\nOur reply:\nIndeed. We started a new paragraph.\n\nReviewer 4:\nLine 388: physiolgy -> physiology\n\nOur reply:\nThank you. This has been corrected.\n\nReviewer 4:\nIn the Methods section, you describe some properties of the circuits without giving any references for their origin. Since this is all supposed to be based on evidence, be sure to cite the evidence. This should be true anywhere you describe a gross circuit physiology. For example, at the end of the paragraph from 486-491, you mention that “the connection probability for cortico-cortical connection is 8%.” What evidence is this based on? Other examples include the stipulation that every neuron between the SVCR and the AC has the same number of outgoing as incoming connections, some properties of the striatum, etc.\n\nOur reply:\nPer the reviewer’s suggestion, we have added speciIic references to support all of these statements (revised manuscript lines 517-519). The revised manuscript also includes Table A1 in the supplementary material. It contains a list of anatomical and physiological references guiding each of the circuit components.\n\nThis is an exciting paper, mainly because of the discovery in the model of a type of neural coding that was unknown before and is found in their data after its discovery in the model. This population of neurons codes for when the choice of response is going to be incorrect.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that emerged from the simulations. I recommend its publication.\n\nWe thank the reviewer for their support for our work."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The revised manuscript has clarified my concerns. I am pleased to see that the authors have withdrawn their misleading claim of \"zero-shot learning\". I also agree that the role of subnetwork structure requires an extensive study and a separate publication. I recommend the publication of this manuscript.", "reviewer_3": "The authors addressed all of my concerns.", "reviewer_4": "Review of Pathak, et al.\nThis is an exciting paper, mainly because of the discovery in the model of a type of neural coding that was unknown before and is found in their data after its discovery in the model. This population of neurons codes for when the choice of response is going to be incorrect.\n\nSo, I believe that this work is important because of the discovery of a novel neural code that emerged from the simulations. I recommend its publication.\n\nAs I have been in favor of publication all along, this review is mostly about style, rather than substance. In particular, the new version does have at least one issue, which should be addressed; basically, the authors go overboard with the fact that the model is not trained. The paragraph describing that is very redundant, and should be shortened considerably. There are some smaller issues, which are addressed in my detailed comments below. For example, there are way too many scare quotes throughout the article.\n\nBy the way, in your response to Review 2, you state: “Readers may have the same question as the reviewer. Thus, we added text to the revised manuscript (lines 121-131) that raises these questions and how they can be addressed in future work.”\n\nLines 121-131 do not do this at all. They are about something else completely. After reading the entire article (I did not read the supplementary material), I still did not find anything resembling this in the text.\n\nMore specific comments/wording suggestions/typos/notes:\nThere are so many of these! Sigh.\n\nLines 31-48: this is way too much! I would remove completely most of lines 42-47. Here is a rewrite of part of the paragraph (and the beginning of the next), starting with “The model…” on line 37:\n\n“The model is then presented with an experimental task involving visual stimuli and rewards. Separately, non-human primates (NHPs) have also been exposed to this same experimental task. However, the model has not been built based on any of the animals’ physiological or behavioral data. Instead, the model is presented with the same visual stimuli as the animals, as well as simulated rewards. In response, the model learns the same task, at about the same rate, as the NHPs. In doing so, the model produces extended simulated physiological cell firing and synaptic change activity, across multiple separate anatomically modeled brain regions. This activity, which we term “physiological computation”, is then directly compared with corresponding physiological measures from the monkey experiments.\n\nIn sum, we built a physiological computational model of the corticostriatal circuit designed to be mechanistically accurate across multiple scales, from spiking neurons to local field potentials, informed solely by the physiological literature. This circuit…”\n\nIf you don’t like my rewrite, at least make these paragraphs less repetitive.\n\nYou have a great definition of “physiological computation” in the discussion, on lines 377-379. It would be great to move that into the paragraphs above, and then stop using scare quotes around it afterwards.\n\nSimilar to my comments above, for the sentence starting on line 67:\nChange this:\nWe developed a generalizable, tractable, biomimetic, multi-scale brain model that categorizes visual stimuli after a delay. The model can be described as carrying out “physiological computation”, because it was not directly designed for this task, nor was it “trained” with any empirical data from any target datasets, as per modeling approaches based on artificial neural networks, nor was it designed from pre-existing dynamical systems or other equations or algorithms.\n\nto this:\nWe developed a generalizable, tractable, biomimetic, multi-scale brain model that categorizes visual stimuli after a delay. The model can be described as carrying out “physiological computation”, because it was not directly designed for this task, but only uses bottom-up mechanistic information-processing operations that arise directly from the physiological steps taken by cells within well-defined anatomical circuitry from distinct brain regions.\n[I.e., this is one possible place for the definition in the discussion.]\n\nOn the issue of scare quotes. Please remove these in lines:\n80, 88, 90, 91, 92, 101, 121, 123, 130, 131, 168, 170, 171, 215 (suggest replacing “held” with “maintained”, w/o quotes), 230, 234, 268, 273 (here, I suggest deleting the parenthetical “activity” altogether), 305 (here I suggest replacing “discovering” with “predicting” (no quotes)), 359, 395, 396, 758.\n\nSimilarly, it seems unnecessary to use italics in lines 361-364, and line 371 (I suggest replacing “back-propagation, or backprop” with one word: backpropagation, not in italics.\n\nSmall typos/syntax, etc., issues:\nline 90: i.e., “physiological computation.” -> i.e., their physiological computation.\nline 113: you call it L-FLIC, later, line 121, LFLIC. Pick one, and be consistent for the rest of the paper.\nLines 110-112 are nearly completely repeated in lines 126-128. Condense or reword these two paragraphs, as they are pretty redundant.\nLine 121: delete the parenthetical remark.\nlines 194-195: What is the point of this sentence fragment starting with “but it is…”? Is this about using rule-based computations? You could just delete it.\nparagraph 196-221 is very long; consider breaking it into smaller paragraphs (e.g., maybe start a new one at line 213 with “The activity during..” Up to you.\n\nFigure 2 caption: “reach plateau performance by about trial 300”. It looks much more like trial 450 or 500. It certainly doesn’t plateau at 300.\n\nLines 242-243. “these changes in the model are compared directly against empirical changes in nonhuman primates (NHPs) (Figure 2b and 3b).”\nFigure 2B does not compare them directly, nor does 3B. What are you referring to here?\n\nFigure 3A: I note here that in the simulation, the cyan line is not above 0 in the stimulus on case.\n\nLine 281: delete “very” in “very significantly”.\nLine 347: et. al. -> et al. (Typo Detection is the all-around *worst* superpower to have…)\nLine 386: not -> are not. Also, the traditional schema for this kind of list of examples seems to call for three examples, not just two. It would be good to add a third here.\n\nLines 402-403: I think you’re pointing to this idea, but I think it might be good to be more explicit about how ICN’s could be important if contingencies change? Up to you.\n\nLine 409: “new and never previously presented” -> novel.\n\nLines 422-423: Change:\nother studies strongly suggest feedback inhibition to be performed by SOM cells (86), whereas still other work suggests SOM martinotti cells to cause feedforward inhibition (42, 84).\nTo:\nother studies strongly suggest feedback inhibition is performed by SOM cells (86). Other work suggests SOM martinotti cells cause feedforward inhibition (42, 84).\n\nLine 427: “as seen through” -> “consistent with”\n\nLine 448: Saying your simulation encompasses sensory encoding is going too far. There isn’t anything in your simulations about actual sensory encoding. For example, see your own text on lines 771-772.\n\nMethods section: There are issues here with the format (e.g., compare line 470 with lines 480-481), but I assume those will be taken care of at the page proof stage.\n\nLine 518: evidences -> evidence\nLines 522-523: This is the first mention, I believe, of cortical columns. Your model doesn’t have cortical columns, as far as I can tell. I would delete mention of cortical columns.\n\nLine 717: factors e.g. the -> factors, e.g., the\nLines 742-743. You say that “they are also not typically constructed to generate extended cognitive operations such as the behavioral perception, learning, working-memory, and decision task presented herein.” This is exactly what SPAUN and Emergent do. So this is an incorrect statement.\n\nLine 773: smoothening -> smoothing\nLine 835: it’s -> its\nsigned,\nGary Cottrell\nSigned,\nGary Cottrell"}, "author_response": "We erroneously had omitted this. We now have made a statement that clearly responds\n(corresponding to what we responded to reviewer 2)\nMore specific comments/wording suggestions/typos/notes:\nThere are so many of these! Sigh.\n\nLines 31-48: this is way too much! I would remove completely most of lines 42-47. Here is a rewrite of part of the paragraph (and the beginning of the next), starting with “The model…” on line 37:\n\n“The model is then presented with an experimental task involving visual stimuli and rewards. Separately, non-human primates (NHPs) have also been exposed to this same experimental task. However, the model has not been built based on any of the animals’ physiological or behavioral data. Instead, the model is presented with the same visual stimuli as the animals, as well as simulated rewards. In response, the model learns the same task, at about the same rate, as the NHPs. In doing so, the model produces extended simulated physiological cell firing and synaptic change activity, across multiple separate anatomically modeled brain regions. This activity, which we term “physiological computation”, is then directly compared with corresponding physiological measures from the monkey experiments.\n\nIn sum, we built a physiological computational model of the corticostriatal circuit designed to be mechanistically accurate across multiple scales, from spiking neurons to local field potentials, informed solely by the physiological literature. This circuit…”\n\nIf you don’t like my rewrite, at least make these paragraphs less repetitive.\nThanks for the suggested rewrite. We have incorporated a slightly edited version of it in\nlines 37-48.\n\nYou have a great definition of “physiological computation” in the discussion, on lines 377-379. It would be great to move that into the paragraphs above, and then stop using scare quotes around it afterwards.\n\nSuggestion taken.\n\nSimilar to my comments above, for the sentence starting on line 67:\nChange this:\nWe developed a generalizable, tractable, biomimetic, multi-scale brain model that categorizes visual stimuli after a delay. The model can be described as carrying out “physiological computation”, because it was not directly designed for this task, nor was it “trained” with any empirical data from any target datasets, as per modeling approaches based on artificial neural networks, nor was it designed from pre-existing dynamical systems or other equations or algorithms.\n\nto this:\nWe developed a generalizable, tractable, biomimetic, multi-scale brain model that categorizes visual stimuli after a delay. The model can be described as carrying out “physiological computation”, because it was not directly designed for this task, but only uses bottom-up mechanistic information-processing operations that arise directly from the physiological steps taken by cells within well-defined anatomical circuitry from distinct brain regions.\n[I.e., this is one possible place for the definition in the discussion.]\n\nThank you for this suggested rewrite. We have incorporated a slightly edited version of\nthis in lines 67-69.\n\nOn the issue of scare quotes. Please remove these in lines:\n80, 88, 90, 91, 92, 101, 121, 123, 130, 131, 168, 170, 171, 215 (suggest replacing “held” with “maintained”, w/o quotes), 230, 234, 268, 273 (here, I suggest deleting the parenthetical “activity” altogether), 305 (here I suggest replacing “discovering” with “predicting” (no quotes)), 359, 395, 396, 758.\n\nSimilarly, it seems unnecessary to use italics in lines 361-364, and line 371 (I suggest replacing “back-propagation, or backprop” with one word: backpropagation, not in italics.\n\nAll these suggestions have been incorporated.\n\nSmall typos/syntax, etc., issues:\nline 90: i.e., “physiological computation.” -> i.e., their physiological computation.\nline 113: you call it L-FLIC, later, line 121, LFLIC. Pick one, and be consistent for the rest of the paper.\nLines 110-112 are nearly completely repeated in lines 126-128. Condense or reword these two paragraphs, as they are pretty redundant.\nLine 121: delete the parenthetical remark.\nlines 194-195: What is the point of this sentence fragment starting with “but it is…”? Is this about using rule-based computations? You could just delete it.\nparagraph 196-221 is very long; consider breaking it into smaller paragraphs (e.g., maybe start a new one at line 213 with “The activity during..” Up to you.\n\nAll suggestions have been incorporated.\n\nFigure 2 caption: “reach plateau performance by about trial 300”. It looks much more like trial 450 or 500. It certainly doesn’t plateau at 300.\n\nThis error has been corrected.\n\nLines 242-243. “these changes in the model are compared directly against empirical changes in nonhuman primates (NHPs) (Figure 2b and 3b).”\nFigure 2B does not compare them directly, nor does 3B. What are you referring to here?\n\nThe figure reference has been corrected.\n\nFigure 3A: I note here that in the simulation, the cyan line is not above 0 in the stimulus on case.\n\nWe have changed the caption to say “… Values that are significantly above zero…” This\ninstance is not significantly different from zero.\n\nLine 281: delete “very” in “very significantly”.\nLine 347: et. al. -> et al. (Typo Detection is the all-around *worst* superpower to have…)\nLine 386: not -> are not. Also, the traditional schema for this kind of list of examples seems to call for three examples, not just two. It would be good to add a third here.\n\nLines 402-403: I think you’re pointing to this idea, but I think it might be good to be more explicit about how ICN’s could be important if contingencies change? Up to you.\n\nLine 409: “new and never previously presented” -> novel.\n\nLines 422-423: Change:\nother studies strongly suggest feedback inhibition to be performed by SOM cells (86), whereas still other work suggests SOM martinotti cells to cause feedforward inhibition (42, 84).\nTo:\nother studies strongly suggest feedback inhibition is performed by SOM cells (86). Other work suggests SOM martinotti cells cause feedforward inhibition (42, 84).\n\nLine 427: “as seen through” -> “consistent with”\n\nSuggestion taken.\n\nLine 448: Saying your simulation encompasses sensory encoding is going too far. There isn’t anything in your simulations about actual sensory encoding. For example, see your own text on lines 771-772.\n\nPoint taken. We removed sensory encoding.\n\nMethods section: There are issues here with the format (e.g., compare line 470 with lines 480-481), but I assume those will be taken care of at the page proof stage.\n\nThanks for pointing out. We corrected indentation issue.\n\nLine 518: evidences -> evidence\nLines 522-523: This is the first mention, I believe, of cortical columns. Your model doesn’t have cortical columns, as far as I can tell. I would delete mention of cortical columns.\n\nLine 717: factors e.g. the -> factors, e.g., the\n\nCorrections made.\n\nLines 742-743. You say that “they are also not typically constructed to generate extended cognitive operations such as the behavioral perception, learning, working-memory, and decision task presented herein.” This is exactly what SPAUN and Emergent do. So this is an incorrect statement.\n\nWe have corrected the statement.\n\nLine 773: smoothening -> smoothing\nLine 835: it’s -> its\n\nTypos corrected."}]

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