Biologically Constrained Barrel Cortex Model Integrates Whisker Inputs and Replicates Key Brain Network Dynamics
Authors: Tianfang Zhu
Dongli Hu
Jiandong Zhou
Kai Du
Anan LI
Venue: ICLR 2025 Spotlight (2025)
Keywords: Barrel cortex
biophysical modeling
sensory-motor integration
recurrent spiking neural networks
Primary area: applications to neuroscience & cognitive science
TL;DR: Training a biologically constrained barrel cortex model and exploring its biological interpretability.

Abstract: The brain's ability to transform sensory inputs into motor functions is central to neuroscience and crucial for the development of embodied intelligence. Sensory-motor integration involves complex neural circuits, diverse neuronal types, and intricate intercellular connections. Bridging the gap between biological realism and behavioral functionality presents a formidable challenge. In this study, we focus on the columnar structure of the superficial layers of mouse barrel cortex as a model system. We constructed a model comprising 4,218 neurons across 13 neuronal subtypes, with neural distribution and connection strengths constrained by anatomical experimental findings. A key innovation of our work is the development of an effective construction and training pipeline tailored for this biologically constrained model. Additionally, we converted an existing simulated whisker sweep dataset into a spiking-based format, enabling our network to be trained and tested on neural signals that more closely mimic those observed in biological systems. The results of object discrimination utilizing whisker signals demonstrate that our barrel cortex model, grounded in biological constraints, achieves a classification accuracy exceeds classical convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), by an average of 8.6%, and is on par with recent spiking neural networks (SNNs) in performance. Interestingly, a whisker deprivation experiment, designed in accordance with neuroscience practices, further validates the perceptual capabilities of our model in behavioral tasks.
Critically, it offers significant biological interpretability: post-training analysis reveals that neurons within our model exhibit firing characteristics and distribution patterns similar to those observed in the actual neuronal systems of the barrel cortex. This study advances our understanding of neural processing in the barrel cortex and exemplifies how integrating detailed biological structures into neural network models can enhance both scientific inquiry and artificial intelligence applications. The code is available at https://github.com/fun0515/RSNN_bfd.

Published as a conference paper at ICLR 2025
BIOLOGICALLY CONSTRAINED BARREL CORTEX MODEL INTEGRATES WHISKER INPUTS AND REPLI- CATES KEY BRAIN NETWORK DYNAMICS
Tianfang Zhu"r Dongli Hu Jiandong Zhou', Kai Du Anan 1.3.5
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology Institute for Artificial Intelligence, Peking University SHUST-Suzhou Institute for Brainsmatics, JITRI
"State Key Laboratory of General Artificial Intelligence, Peking University School of Biomedical Engineering, Hainan University
{funfunfur hudongli, j jiandongzhou, aali /uuust hww./ss.euu........ edu.cn 2 [Kkai . @pku. edu. on
ABSTRACT
The brain's ability to transform sensory inputs into motor functions is central to neuroscience and crucial for the development of embodied intelligence. Sensory- motor integration involves complex neural circuits, diverse neuronal types, and intricate intercellular connections. Bridging the gap between biological realism and behavioral functionality presents formidable challenge. In this study, we focus on the columnar structure of the superficial layers of mouse barrel cortex as model system. We constructed model comprising 4,218 neurons across 13 neuronal subtypes, with neural distribution and connection strengths constrained by anatomical experimental findings. A key innovation of our work is the de- velopment of an effective construction and training pipeline tailored for this bi- ologically constrained model. Additionally, whisker sweep dataset into a spiking- format, enabling our network to be trained and tested neural signals that more closely mimic those observed in biological systems. The results of object discrimination utilizing whisker sig- nals demonstrate that our barrel cortex model, grounded in biological constraints, achieves classification accuracy exceeds classical convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory net- works (LSTMs), by an average of 8.6%, and is on par with recent spiking neural networks (SNNs) in performance. Interestingly, whisker deprivation experiment, designed in accordance with neuroscience practices, further validates the percep- tual capabilities of our model in behavioral tasks. Critically, it offers significant biological interpretability: post-training analysis reveals that neurons within our model exhibit firing characteristics and distribution patterns similar to those ob- served in the actual neuronal systems of the barrel cortex. This study advances our understanding of neural processing in the barrel cortex and exemplifies how integrating detailed biological structures into neural network models can enhance both scientific inquiry and artificial intelligence applications. The code available at Inttps://ggithh com/ Fun 5/ RSNN__SFFFFFF
converted existing simulated
INTRODUCTION
The brain's 's remarkable ability to transform sensory inputs into coordinated motor actions is pivotal, not only in neuroscience but also in the development of embodied intelligence systems (Bartolozzi et al. 2022, Putra et al. (0024). This process, known as sensory-motor integration, relies on complex neural circuits, diverse neuronal types, and intricate inter-neuronal connections (Karadimas et al.) 2020
Mtuoo--Castüñcoo et al. 2021 Ziegler et al. 2023). These elements are fundamental for both
Equual contribution. Corresponding authors.

---

Published as a conference paper at ICLR 2025
Barrel cortex
D
Output
L1
Two linear layers
12/3
14
FSBS
FSCH
Pyr
L5
1.6
LA
RSNP
Pyr
Pyr_St
Thalamus
Thalamic
Lemniscal neurons
Other type neurons
Input
Figure The biophysical modeling of the barrel cortex based real anatomy. (A) The "whisker-barrel signaling pathway. Sensory signals from the whiskers are transmitted by the trigeminal ganglion neurons (1) to the brainstem neurons (2), then relayed to the barrel cortex via the thalamus neurons (3). (Figure modified from (Petersen 2019)). (B) An anatomical image of the barrel cortex in adult mice, sourced from the Allen Brain Atlas (Wang et al. 2020). (C) Thalamic projection neurons directed to the barrel cortex. Those highlighted in red represent core thala- mic projection neurons, originating from the ventral posteromedial thalamic nucleus, predominantly projecting to layer 4 (L4) of the cortex. (D) Outline of our biologically constrained network archi- tecture. External inputs are conveyed into the network through 200 thalamic neurons. The network comprises 13 neuronal populations with total of 4218 adaptive Leaky Integrate-and-Fire neurons. Red signifies excitatory neurons, whereas blue signifies inhibitory neurons. For instance, "Pyr_" denotes a specific subtype within the category of pyramidal neurons. The network's prediction is readout from the state of three excitatory populations within the network by two linear layers. De- tailed information of neuronal subtypes can be found in the supplementary material.
understanding brain function and advancing artificial intelligence systems capable of interacting seamlessly with the physical environment. However, achieving balance between biological real- ism and functional performance in computational models remains a significant challenge, these models must accurately replicate these complex biological processes.
The rodent barrel cortex, which processes tactile information from whiskers. serves as an exemplary model system for studying sensory-motor integration. Its well-documented columnar organization and neuronal circuitry provide robust framework for investigations (Petersen 2019 Staiger & Petersen 2021). Despite this, traditional computational models often oversimplity biological com- plexities to achieve functionality, thus losing crucial insights into the actual neural mechanisms. Conversely, highly detailed biologically realistic models (Fan & Markram 2019 D Angelo & Jirsal 2022], while rich data, are often computationally intensive and difficult to train, limiting their practical application in simulating behaviorally relevant tasks.
To address these challenges, our study introduces an effective construction and training pipeline specifically designed for biologically constrained model of barrel cortex, aimed at achieving bio- plausible sensory-motor integration. Our approach characterized by three primary contributions:
Biologically constrained barrel cortex model: We developed columnar model of the mouse barrel cortex, comprising 4,218 18 neurons across 13 neuronal subtypes. This model meticulously reflects the neural distribution and synaptic connections dictated by experi- mental anatomical findings (Huang et al., (022), ensuring an authentic replication of the biological structure of mouse barrel cortex.
2

---

Published as a conference paper at ICLR 2025
A
Connection probability
B
CV score
C
0.0
80
Source subtype
0.3
0.07
15
Test accuracy
60
0.2
0.14
10
0.21
40
8 m0,00, Maumb0s
0.1
Our 0.00, Weer 0.18
0.28
0.0
0.0
0.07
0.14
0.21 0.28
25
50
75
100
Target subtype
Epochs
Figure 2: Anatomical connection probability and network initialization. (A) Connection prob- abilities among 13 neuron subtypes, derived from (Huang et al. 2022), with excitatory connections represented in red borders and inhibitory connections in blue. (B) The impact of two significant parameters Osii and Wint discussed in Sec. 3.2] Network's 's initial states are assessed using the co- efficient of variation (CV) to measure the synchrony of neuronal firing. (C) Accuracy on a whisker- related dataset (see in Sec. 3.3 for three initial setrings, as denoted by rectangle symbols in (B).
Pipeline for model construction and training: Our novel pipeline is specifically tailored to balance the biological authenticity and behavioral functionality. This includes using intuitive neuron models and sparse connections to mimic the neural diversity and projection strength, introducing an order parameter to measure the synchrony of neuronal activity, thereby identifying trainable initial states. This pipeline is not only applicable to the barrel cortex but can also be extended other biologically constrained models.
Establishing novel spiking whisker sweep dataset: We leveraged previously devel- oped simulated whisker sweep dataset (Zhuang et al. 2017). originally generated using a physical engine (Coumans & Bail research. Our significant contribution was transforming this simulated data into a novel spiking-based dataset (see in Fig. B). which emulates the sensory signals from the thalamus area. This newly established spiking dataset provides a biologically meaningful platform for training and evaluating our model's sensory- integration capabilities.
with parameters informed by neuroscience
While trained on the Spiking Whisker Sweep Dataset (see in Fig. 3) the classification accuracy of our model surpasses that mainstream models like CNNs (Zhuang et al. 2017). RNNs (Collins)
let al. (017). and LSTMs (Ullah et al. 2017), and is competitive with recent SNNs (Yin al.) [2020] Yu et al. 2022 Yao et al. 2021). particularly in neuroscience sersssiienne-iiprreedde whisker deprivation experi- ments. Additionally,
model demonstrates superior biological interpretability. Our post-training analysis shows that neurons within our network exhibit firing patterns and distribution characteristics remarkably similar to those observed in actual barrel cortex, including aspects such as response selectivity, dynamical gradients, and node degree distribution. Our findings underscore the potential of incorporating biological constraints into neural network models to enhance their performance on sensory-man -ooor tasks and provide deeper insights into the underlying neural mechanisms. This ap- proach promises significant advances in developing AI systems with brain-liks perceptual and motor capabilities and contributes to neuroscientific efforts designed to unravel the complexities of brain function through advanced computational modeling.
RELATED WORK
The barrel cortex, distinct area discovered within the primary somatosensory cor- tex of rodents, assumes a pivotal role in the animal's 's spatial perception, precise localization capa- bilities, and motor functions. Neurons within this cortex are organized into mesoscale barrel-like formations (see in Fig. [3,, aligned with the layout of the animal's ''s snout whiskers, hence the name. Each "barrel" preferentially responds to tactile stimuli from the corresponding whisker, forming a distinctive "whisker-barre eral pathway process unfolds as follows: Sensory stimuli originating from the whiskers are initially conveyed through the brainstem, subsequently relayed the barrel cortex via the thalamus, facil- itating the integration and processing of tactile information (see in Fig. [T]. The unique structure and function of this "aaree"r-bbrrel" model make it prevalent tool for studying information in-
Barrel cortex:
sensory pathway (Petersen 2019 Staiger & Petersen] 2021). The gen-
3

---

Published as a conference paper at ICLR 2025
B 1.0
Helmet
Force
Sweep
Rate
0.6
Box
0.4
Torque
0.2
Sampling
Plane
0.01
Actial come
50
100
Artificial whiskers
Rale coding
Spike trains
Time step
Figure 3: Spiking whisker sweep dataset. (A) 3D objects sweep across the virtual whisker array in a physics engine (Zhuang et al.) 2017), recording the force and torque signals from whisker base sensors. Then, the analog inputs are converted into spiking inputs by generating spike trains within a time window through the sigmoid function and Bernoulli sampling. (B) Bernoulli sampling probability calculated by the sigmoid function and the actual generated spikes firing rate.
tegration and processing the brain. Advances the understanding cell types, locations, and projection patterns (Peron et al. 2015 Sermet et al. 2019; Zhang & Zagha 2023 Estebanez et al., 2016 Gardères et al. (224]] in the mouse barrel cortex indicate that the sensor-motor integration in biological systems possesses detailed anatomical principles.
Drawing upon the continuously accumulating quantitative anatomical data, neuroscientists have attempted to reconstruct local circuitry of the barrel cortex on silicon, aiming to validate our understanding the sensory system. For instance, by employing the Izhikevich single-neuron model (Izhikevich) (003). three-barrel model of the superficial layer was constructed to mimic the alterations in synaptic strength that occur subsequent whisker deprivation (Huang et al., 2022). The dendritic model of spiny stellate cells (Lavzin et al. 2012). direction selectivity model (Kremer et al., 2011), and layer 4 local network model in Fimrl KO mice (Domanski et al. 2019) have also been constructed. These biological computing models accurately reflect the complex activities in the sensory system, yet they lack perceptual functions in behavioral contexts. This limitation stems primarily from two key factors: Firstly, the intricate dynamics and inherent instabilities of biological models, such as neuronal refractory periods, are not conducive to computational gradients. Secondly, the substantial cost associated with live experiments and the challenges in precisely capturing sensory signals have led to scarcity of datasets, which in turn, hinders the direct training of biological models akin to machine learning paradigms. Consequently, the application potential of these biological models remains constrained.
Biologically constrained model for barrel cortex:
Trainable biologically constrained model: However, mainstream trainable models overlook the detailed structure of cortical circuits. For instance, artificial neural networks typically adopt reg- ular fully-connected (multilayer perceptrons) or locally-connected (convolutional neural networks) architectures, which are discrepant from the intricately heterogeneous connectivity found in real cortex (Oh et al., 2014). Furthermore, current models do not strictly differentiate between neuron types (excitatory inhibitory) and numbers. which have been a focal point in recent neuroscien- tific efforts to decipher brain function
et al.) [021] Yao et al. 2023). Therefore, an emerging direction is to integrate more detailed biological mechanisms into traditional compu- tational models, such as introducing dendrites to enhance the spatiotemporal dynamics of network models (Zheng et al., 2024). spiking neural network (Chen al. 2022), consisting of over 50,000 neurons distributed across four distinct subtypes, was trained five biologically pertinent visual tasks, leveraging the findings from neuroscience on the mouse primary visual cortex (Billch et al. 2020). However, to best knowledge, network models that possess both the topological structure of the sensory cortex and perceptual behavioral functions are currently rare.
3 METHODOLOGY
3.1 MODEL CONSTRUCTION
Network topology: To enhance biological authenticity, we established a biologically constrained barrel cortex model with reference to the study in neuroscience. The number, location, type, and con-
4

---

Published as a conference paper at ICLR 2025
nectivity of neurons in our model faithfully replicate the findings from the anatomical study (Huang jt al., 2022). This study utilized immunohistochemistry confocal imaging, and other biological techniques elucidate specific neuronal distribution patterns in the superficial layers of the mouse barrel cortex. In general, our model comprises 13 neuronal subtypes, including 3 excitatory and 10 inhibitory subtypes, totaling 4,218 neurons, along with an additional 200 thalamic neurons. Follow- ing the classical "whisker-barrel" signaling pathway (see in Fig. [A C), external inputs are initially processed by 200 thalamic neurons and then primarily propagate four neuronal subtypes found in layer 4 (L4) of the barrel cortex. Considering that the projecting neurons are predominantly excita- tory in brain, we use the state of three excitatory communities processed through two linear layers as the readout for the entire network (see in Fig. DD)
The number of connections between neurons is generated through sampling based on experimentally measured projection strength (see in Fig. (A). Assuming there are presynaptic neurons and m postsynaptic neurons, the potential number of connections nxm. We introduce boolean matrix M to constrain the number of connections. The calculation formula is as follows:
W = WOM, W E &R R"
Mij =D, Mi E {0, [0,1).P 1..
TIT PMF(M))) = p-J, - + (1-p)) p) (1-p) 00
where W and W' denote the original and constrained connection weight matrices, respectively. M represents a boolean matrix of the same size as W, where an element of indicates the presence of a corresponding connection, and 0 indicates its absence. p and á stand for the experimentally measured connection probability and Kronecker delta, respectively. PMF signifies the Probability Mass Function. Weights and gradients are only computed and updated on existing connections. By repeating this process to establish all connections, our model can be collectively viewed as spiking recurrent neural network, with each neuronal population construed hidden layer within it.
Single-neuron dynamic model:
We employ the adaptive Leaky Integrate-and-Fire (aLIF) Yin al.) 2020) model as the backhone for depicting single-neuron dynamics. In comparison to the frequently employed Izhikevich (Izhikevich] (200)) and GLIF (Teeter et al. 2018) neural models in neuroscience, the aLIF model offers simplified computational requirements and more amenable to training. In contrast to the straightforward LIF neurons, the aLIF model sets the time constant as trainable parameters, thereby effectively capturing the dynamic variations among neurons within cortical networks. The membrane potential V at time is updated according to this formula:
(2) where R and Tm respectively denote the resistance and time constant of cell membranes, with representing the input current. S is a heaviside function used to indicate whether the neuron emits a spike. If a spike is emitted, the membrane potential V undergoes soft reset by subtracting the current firing threshold 6.
V(I) e -
(1- R I(()
-0())((())))
To avoid abnormal firing, the fired spike triggers temporary increase in the firing threshold 0. The firing threshold ((t) at time can be calculated using the following formula:
O(l) Oznit 3 ()()
7()) Fady 1 1) +
Fady)
S((
where Ornit is the minimum firing threshold. B is a constant that regulates the increment of the threshold induced by spikes 1), which we set to 1.8. Tedp represents the adaptive time constant of the firing threshold. Notably, we set Tm and Tadp as two additional trainable parameters to investigate the differentiation of neural dynamics in the trained model (see in Sec.
To reflect neuronal diversity, we have additionally specified the categories of input current to the neurons: excitatory or inhibitory currents. Neurons in the network receive not only external input currents T. but also currents Tree from connected neurons. The total current T(t) received by neuron at time can be calculated by the following formula:
T(t)= = (aT + (t), W. > 0
(te = Σ H,W?' St(t - 1), H, E [-1, Wree >
(4)
5

---

Published as a conference paper at ICLR 2025
Table Classification accuracies (%) among models. ANNS are tested the original real-valued whisker sweep dataset (Zhuang et al. 2017). while SNNs are tested on both the real-valued dataset and the spiking-bant -bsed dataset constructed in Sec. 3.3
ST CNN
DB LSTM
RNN+
UGRNN
ANNs
Zhuang al.
2017
Ullah et al.
2017
(Collins et al.
2017)
Real-valued
67.2
70.9
78.2
76.4
SRNN 256
STSC SNN
TA SNN
Barrel model
SNNs
Yin et al. 2020)
Yu et al. 2022
Yao et al. 2021
(ours)
Spiking-baas
81.8
80.0
81.8
81.8
Real-valued
85.5
87.3
83.6
89.1
Table 2: Classification accuracies SNNs in whisker deprivation experiments. Tested on the spiking whisker sweep dataset.
Number of deprived whiskers
SNNs
3
5
SRNN 256
Yin et al
2020
81.8
69.0
66.2
61.6
59.0
57.0
55.0
53.4
53.2
STSC SNN
Yu ct
2022
80.0
71.9
68.4
64.8
65.2
61.7
58.3
57.5
57.2
TA SNN
Yao et al. 2021
81.8
72.4
66.3
64.5
60.4
56.8
54.7
47.8
48.4
Barrel model (ours)
81.8
73.8
69.7
67.9
66.1
63.7
60.4
60.1
58.0
where We and Wiree represent the weights of the external connection and the connection with the i-th presynaptic neuron, respectively. H; is a binary constant used to indicate the type of the i-th presynaptic neuron, where represents an excitatory type and -1 represents an inhibitory type. S represents the spike emitted by the i-th presynaptic neuron. To distinguish the effects of excitatory and inhibitory currents, all connection weights are clamped to remain positive.
Based on the aforementioned single-neuron dynamics, we can apply Backpropagation- Through- Time (BPTT) to train the entire network. To compute the gradients the spike-timing, we employ Gaussian function as the surrogate gradient:
grad (()
(5)
where the mean of distribution N is the firing threshold 6, and the variance is a constant a used to scale the membrane potential. Unless otherwise specified, the parameters of neural dynamics in our network are set to the same value.
3.2 MODEL TRAINING
Another primary issue in training biologically constrained models is that, as complex dynam- ical system, subtle variations in initial parameters can lead to bifurcation in our biological net- work (Wang) 2022). Therefore, searching for appropriate initialization parameters is crucial for stable training of the network state. Here, we investigate two critical parameters: the initial firing threshold fnat and the initialized connection weight Wanit- After generating neuronal connections according to Eq. I all connection weights are randomly sampled from a uniform distribution within the range [0, W snat)
Chaotic neuronal activity, opposed to regular firing, typically signifies a more abundant learnable representational space. Consequently, we introduce the coefficient of variation (CV) as an order parameter to quantify the synchronization of neuronal electrical activity (Tian et al., 2022). Prior to training, we first input samples from the dataset into networks with varying initial states, and quantify the activity of all neurons during the forward propagation process. For initial network, the spike emissions during the forward propagation of external inputs with time length T among N neurons recorded, forming an N x T boolean matrix. Then coefficients of variation can be calculated with this formula:
N
CV
(6)
N

---

Published as a conference paper at ICLR 2025
1360 P-value 0.001 Cells
2242 P-value 0.001 Cells
A
Box
B
0.6
Box
P-0d001 orts
0.6
Box
P-0 odls
0.7 O
Nocnal cells
Nomal oels
0.4
0.4
120
0.2
0.2
Helmet
Plane
0.0
0.0
Box
160 spikes
0.2
-0.2
Helmet
20 spikes
Helmet
Plane
Helmet
Plane
0.4
0.44
Plane
20 spikes
-0.5
-0.25
0.25
0.5
-0.5
-0.25
0.25
0.5
Network
Initial network
Trained network
Figure 4: Firing selectivity of neurons in the trained network. (A) Count the spikes for each neuron across three categories of inputs, and use this as a vote to determine its displacement in the directions representing the three categories. (B) Visualization results of imput-specific firing in the initialized network (left) and the trained network (right). Neurons with a P-value less than 0.001, as determined by one-way ANOVA, are considered to exhibit significant firing selectivity.
where μ4, and og represent the mean firing rate and standard deviation, respectively, of the i-th neuron. We consider the average value of CV across all input samples the final score for this initial network. 2 illustrates the evolution of CV score, where a higher value typically indicates more disordered neuronal activity (see in the supplementary material). Fig presents the training performance of three differently initialized networks on the dataset constructed in Sec (33 with the initialization exhibiting the higher CV score yielding the best training performance.
3.3 SPIKING WIISKERR SWEEP DATASET
The absence of corresponding behavioral datasets poses a significant hindrance to the training of biologically constrained models. High time cost of in vivo behavioral experiments and difficulty in accurately recording whisker signals have resulted in a scarcity of available datasets that directly correspond to the barrel cortex. As a result, we converted a virtual whisker sweep dataset (Zhuang eet al. 2017) into the spiking format, which serves as platform for training and analyzing our barrel model. while also facilitating comparisons with mainstream computational models.
In the field of sensory-moto integration in neuroscience, scientists have leveraged anatomical knowledge of rat heads and whiskers to create a physically and biologically realistic whisker array This model comprises 31 whiskers,
model within physics engine (Coumans & Bail 2016622022 accurately reflecting mechanical properties such as length, intrinsic curvature, and relative positions. By recording the forces and torques experienced by each whisker as tiree-dimensional models from Shape dataset (Chang et al., (2015) sweep across the whisker array, a virtual whisker signal clas- sification task can be established.
However, this whisker sweep dataset was originally created for training convolutional neural net- works, and thus requires converting signals into spiking- inputs before they can be used for our biologically constrained network training. Recent neuroscientific research has revealed that whisker vibration signals undergo a transformation from temporal coding to cortical rate coding in the thalamus (Lee et al.] 2024). Inspired by this finding, we have developed method to generate spike trains from force and torque signals recorded by sensors, utilizing a rate coding approach (see in Fig. 3.. For the magnitude of the force detected by whisker sensor at a particular moment [F), we map it to a probability p of firing a pulse using the sigmoid function:
(7)
Ite (hn-
where c is the shift coefficient. Subsequently, based on the probability p. independent spikes are generated within a fixed-length time window using Bernoulli sampling. We set the length of time windows to 5, meaning that the time step is extended to five times its original length. The sampling probability versus the actual pulse firing rate is illustrated in Fig. BB. The torque is processed in the same manner as the force. Specifically, the shape of a single sample in our spiking whisker sweep dataset is (550, 31, 3, 2). representing 550 time steps, 31 whiskers, 3 sensors the base of each
7

---

Published as a conference paper at ICLR 2025
A
Py
B
Pearson correlation: 0.727
0
P-value: 0.005
Probability densty
Normalized firing rate
experimental
2,5
15
Pyr
1.00
0g.
9.0
9.5
10u6
10.5
11.
11.5
0.75
2.5
0.50
0
7
0.25
2.5
Inh
Ith, RSNP
0.00
0.5
9.0
9.5
10.0
10.5
11.0
11.5
Neuron subtype
10111213
Fm
Tadp
Figure 5: Emergent similarities in neural dynamics between the trained network and neuro- science experiments. (A) Kernel density estimation plots of Tm and Tedp for the four L4 subtypes in the trained network. Red vertical lines denote the mean value, while green vertical lines indicate the identical initial values assigned. The complete set of 13 subtypes is presented in the supplemen- tary material. (B) The comparison of the 13 subtypes after training with experimental-biie -based firing rates, quantified as the Pearson correlation coefficient. The firing rates are normalized to the interval 10.1, 1] draw the chart. Due to the identical initial dynamic parameters of all neurons, the firing rates are the same pre-training (red reference line).
whisker, and 2 channels for force and torque, respectively. There are 91 samples for each of the three classes, and the dataset randomly split into training and testing sets in 8:2 ratio.
4 EXPERIMENTAL RESULTS
In this section, we compared comprehensively the ability of our model to perceive objects with sev- eral mainstream computational models based on the whisker sweep dataset. Then, we emphasize the biological interpretability of our trained model, delving into the dynamic evolution that tran- spires within our model throughout the training phase. This exploration offers insights into how the model's behavior mimics and elucidates underlying biological mechanisms. Details of the model training are provided in the Appendix.
4.1 PERFORMANCE COMPARISON WITH BASELINE MODELS
We first evaluated the ability of our barrel cortex constrained model to distinguish objects based on whisker signals and contrasted its classification accuracy with other prominent computational models. As shown in Tab. [ our model's accuracy on the whisker sweep dataset surpassed tradi- tional ANNs, reaching peak of 89.19 1% the original real-valued dataset and matching the best performance of recent SNNs at 81.8% on our spiking- dataset.
Subsequently, introduced commonly used whisker deprivation experiment from neuroscience to further compare the whisker-related perceptual abilities of our model with other SNNs. Neuro- scientists commonly trim cauterize specific whiskers of mice in behavioral experiments study the plasticity of sensory neural system (Gunner et al. 2019 Pan et al. 2022). We mimicked this scenario by randomly occluding different numbers of whisker signals in the spiking whisker sweep dataset (experimental details are provided in the Appendix). The results (see in Tab. ( demonstrate that our model consistently achieved the highest classification accuracy in all experimental settings, showcasing not only improved fidelity in sensory circuitry but also perceptual classification capabil- ities that rival those of mainstream computational models.
4.2 NEURAL SPECIFIC FIRING SELECTIVITY
Next, we focus examining the internal modifications that transpire within our model as it develops perceptual capabilities, starting with the firing selectivity. The selective firing observed by neuro- scientists in the sensory cortex is prominent feature for elucidating the mechanisms of sensory functions in the brain (Guy et al,, 2023). In vivo experiments have revealed that the barrel cortex contains vast range of neurons with specific responses; these neurons exhibit strong reactions to
8

---

Published as a conference paper at ICLR 2025
Initial in-dogree
B
50-
Trained in-degree
s0
Number of nodes
Accuracy (%)
20
40
60
80
40-
Initial out-degree
50-
Trained out-degree
Fully
P-0.5
P-0033
0 20
40 60
80
100
Connection probability
Weighted degree
Figure 6: (A) Alterations in weighted degree distribution pre- and post-training. (B) Training perfor- mance of networks created with varying connection probabilities. From left to right: anatomically based connection probabilities, random connection probabilities, fully connected network, and fixed connection probability P. Each configuration
trained ten times to plot the violin diagram. All connection configurations, with the exception random connections, were initialized using the method described in Sec. 3.2 with the process detailed in the supplementary material.
signals of particular directions (Wilent Contreras, 2005
Estebanez et al. 2016) and deflection angles (Lavzin et al. 2012) but are relatively insensitive irrelevant signals.
The approach for quantifying neuronal firing selectivity in our model illustrated in Fig. FA. Specifi- cally, we sequentially imput the three types of samples from dataset into the network, count category- related spikes for each cell, and then employ one-way Analysis of Variance (ANOVA) to judge whether significant differences exist. A neuron considered input-specific if the resulting P-value less than 0.001. To visualize this category selectivity, we divided three equally spaced directions and voted for its position according to the number of spikes by cells respond to each category.
Statistical results show that after training, the proportion of neurons with significant firing selectivity has increased from 32.2% 53.2%. Moreover, the differentiation of category preference becomes more evident (see in Fig. 4B). In the initialized network, the number of spikes responded by neurons different categories of inputs balanced, so they are clustered in the central area. However, in the trained network, cluster of neurons is gathered in the direction of each category, indicating that the number of spikes released in response to that category of input far exceeds that of the other two categories. The same selective firing analysis for other SNNs can be found in the supplementary material. Reproducing neural firing selectivity similar to that brains enhances the biological interpretability of our network.
4.3 DIFFERENTIATED NEURONAL DYNAMICS
Furthermore, we compared the dynamical similarity of 13 neural subtypes between our trained net- work and those statistically observed in neuroscience experiments, thereby quantifying the biolog- ical plausibility of our model. The heterogeneous dynamical properties across neocortex serve as the structural foundation for the coordinated function of different regions (Wang) 2022). Although all neuronal subtypes were initially assigned the same values for Tm and Tadp elaborated in Sec. 3.] during the training process, these trainable parameters self-updated in response optimizing the objective function, leading to the differentiation of individual dynamics for each neuron.
Fig. deemonstrats that our network, under the optimization constraint of the classification task only, spontancously differentiates into dynamic gradients across 13 neuronal subtypes (see in Fig. 55A) Then, we quantitatively compared the similarity between our neural dynamic gradients and ex- perimental results (see in Fig. ()). Corresponding to these neuron subtypes, Izhikevich neuronal models (Izhikevich) 2003) have been established neuroscience to fit the data from biological ex- periments (Huang al. 2022h. We preserved the dynamic parameters of all neuronal subtypes post-training and then applied an identical continuous 550 ms, ImV constant current to our neurons and their corresponding Izhikevich neurons, recording their firing rates and comparing their simi- larity. The extemaa current was applied in isolation to each subtype, rather than across the entire network, to prevent cross-contamination of effects. Then, caleulate the Pearson correlation

---

Published as a conference paper at ICLR 2025
efficient between these two firing rates. In the initial network, all neurons were assigned identical dynamic parameters, resulting in equal firing rates (red reference line in Fig. 5B) across subtypes and no Pearson correlation. However, after training, the Pearson correlation coefficient increased to 0.73 with a P-value of 0.005. Given that biological neuron models typically have longer simulation steps compared to al. neurons, we employed simple normalization scale their firing rates to the same range [0.1, 1.0] to better visualize them together. The normalization process is as follows:
(fr frmmm)
fr' 0.1 + 0.9 X (frmex - frmin)
(8)
where fr and fr' represent the original and normalized firing rates. frmar and fr denote the highest and lowest firing rates among 13 neural subtypes. This normalization does not affect the calculation of the Pearson correlation coefficient. The results indicate that our trained network spontaneously developed dynamic gradients analogous to those observed in the real barrel cortex.
4.4 DEGREE DISTRIBUTION AND CONNECTIVITY PROBABILITY ABLATION
Finally, we investigated the alterations in the network's 's topological structure through statistical anal- ysis of the weighted degree distribution among its nodes. Contrary to the abstract neurons in machine learning models, each neuron in our model is a well-defined biological entity, allowing our network to be viewed from graph-theoretic perspective (Lynn & Bassett (018)). We define the sum of the weights of neuron-related connections as the weighted in-degree and out-degree, and then tally the number of nodes for each weighted degree.
Results show that both the in-degree and out-degree distribution of our network exhibit a pronounced long-tail effect after training (see in Fig. (,),, indicating that the network has spontaneously evolved important hub nodes during training process. The degree distribution of neurons in the network more dispersed than initially. which is consistent with reports on the scale-free properties of brain networks in related work (Zheng et al. 20200] Henriksen et al.] 2016)] Yu et al., 2018).. The two general conditions for the generation of scale-free networks are model growth and preferential attachment (Henriksen et al., 2016). However, our network can also reproduce similar properties merely by optimizing the weights of existing connections through training, which provide a new direction for establishing structurally realistic brain network models in neuroscience. The results using the same statistical method on other models can be found in the supplementary material.
We additionally quantified the impact of anatomic connection probabilities within our network, com- paring its training performance to networks established with random connections, fully connected network and regular connection probabilities of 0.5 and 0.3. The results suggest that networks which maintain the anatomic connection probabilities are not only the most biologically authentic but also the most effective train in whisker-related sensory task (see in Fig. (3).
5 CONCLUSION
In this paper, we develop an effective constructing and training pipeline for biologically constrained sensory-motor integration networks. Our model, grounded in the authentic anatomical structure of the mouse barrel cortex, matches mainstream computing models in sensory performance and spontancously aligns with the firing characteristics, dynamics, and degree distribution observed in real neural systems during the training process. Through the innovative pipeline and converted spiking-based whisker sensory dataset, we advanced the bridging of the gap between biological realism and computational performance in current sensory related models.
Limitations and future work: In the initialization process, we introduced the Coefficient of Varia- tion (CV) measure as a general principle, but a more granular analysis depends on further integration with research outcomes from brain dynamics. Figure in the supplementary material presents raster plots of our model, where abnormal synchronous firing (with higher CV score indicating stronger intensity) can still be observed in the initial network. This is due to all neurons being assigned same parameter values at the start. Although this does not affect the training and disappears in the trained model, it reduces the biological authenticity of the initial network. Further constraining neuronal ac- tivity can bring it closer to biologically realistic networks. For example, there is research potential in employing more nuanced biological constraints, such as membrane potential dynamics (Rossbroich et al. 2022) and synaptic scaling, guide the initialization.
10

---

Published as a conference paper at ICLR 2025
ACKNOWLEDGMENTS
We thank Dr. Chengxu Zhuang for helping us reproduce the whisker sweep dataset. This work was financially supported by the ST1 2030-Major Projects (20212D02010022) and National Natural Science Foundation of China grants (T2122015, 32471149).
REFERENCES
Chiara Bartolozzi, Giacomo Indiveri, and Elisa Donati. Embodied neuromorphic intelligence. Na- Ture Communications, 2022.
Yazan N Billch, Binghuang Cai, Sergey L Gratiy, Kael Dai, Ramakrishnan Iyer, Nathan W Gouwens, Reza Abbasi-Asl, Xiaoxuan Jia, Joshua H Siegle, Shawn R Olsen, et al. System- atic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron, 106(3):388-40) 2020.
Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, L. Yi, and Fisher Yu. Shapenet: An information-rich 3d model repository. ArXiv, 11s/1512.03012, 2015. URL
https://api. www.nt htt.:aar..rrrrooposssii555/////
Guozhang Chen, Franz Scherr, and Wolfgang Maass. A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing. science advances, 8(44): cabq7592, 2022.
Jasmine Collins, Jascha Sohl-Dickstein, and David Sussillo. Capacity and trainability in recurrent neural networks, 2017. URL
www.aaiwwwaaa..aaaaaoooooooooort http//bb//l111 09913
Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, 2016-2021.
robotics and machine learning. http::/pybullet..om
Aleksander PF Domanski, Sam Booker, David JA Wyllie, John TR Isaac, and Peter C Kind. Cellular and synaptic phenotypes lead to disrupted information processing in fmrl-ko mouse layer barrel cortex. Nature Communications, 10(1):4814, 2019.
Egidio D' Angelo and Viktor Jirsa. The quest for multiscale brain modeling. Trends in neurosciences, 45(10):777-790, 2022.
Luc Estebanez, Julien Bertherat, Daniel E Shulz, Laurent Bourdieu, and (ran-Francois Léger. A ra- dial map of multi-whisker correlation selectivity in the rat barrel cortex. Nature Communications, 7(1):13528, 2016.
Xue Fan and Henry Markram. A brief history of simulation neuroscience. Frontiers in Neu- roinformatics, 13, 2019. ISSN 1662-5196. doi: 10.3388//iin..2019.0003 URL https:
//www. www.::ffottiiiiinmo htt.:e/siiii.rrrrtriireeeeeeeeee
3389/333399 fnn/ffnnnff... 2019.00033
Pierre-Marie Gardères, Sébastien Le Gal, Charly Rousseau, Alexandre Mamane, Dan Alin Ganea, and Florent Haiss. Coexistence of state, choice, and sensory integration coding barrel cortex li/iü. Nature Communications, 15(1):4782, 2024.
Georgia Gunner, Lucas Cheadle, Kasey M Johnson, Pinar Ayata, Ana Badimon, Erica Mondo, M Aurel Nagy, Liwang Liu, Shane M Bemiller, Ki-Wook Kim, et al. Sensory lesioning induces microglial synapse climination via adam10 and fractalkine signaling. Nature neuroscience, 22 17):1075-1088, 2019.
Julien Guy, Martin Möck, and Jochen Staiger. Direction selectivity of inhibitory interneurons in mouse barrel cortex differs between interneuron subtypes. Cell Reports, 42(1):111936, 2023. ISSN 2211-1247. doi: http://db..ggg///..0ooo.o.eeeo22
URL hhtpps: www.
sci sciencedireec httpee/c.tt...ccccccccc/c//eaaae
837X
Sid Henriksen, Rich Pang. and Mark Wronkiewicz. A simple generative model of the mouse mesoscale connectome. eLife, 5xe 12366 mar 2016. ISSN 2050-084X. doi: 10.7554/eLife.1253 12366.
UtI.hhttps://doi.coo 75540..5555e11 12366

---

Published as a conference paper at ICLR 2025
Chao Huang, Fleur Zeldenrust, and Tansu Celikel. Cortical representation of touch in silico. Neu- roinformatics, 20(4):1013-1039) 2022.
Eugene M Izhikevich. Simple model of spiking neurons. IEEE Transactions on neural networks, 14(6))1569-1555 15009, 2003.
Spyridon Karadimas, Kajana Satkunendrarajah, Alex M Laliberte, Dene Ringuette, Ilyya Weiss- papir, Lijun Li, Simon Gosgnach, and Michael G Fehlings. Sensory cortical control of movement. Nature neuroscience, 23(1):75-84, 2020.
Yves Kremer, Jean-Frange Dean-François Léger, Dan Goodman, Romain Brette, and Laurent Bourdieu. Late emergence of the vibrissa direction selectivity map in the rat barrel cortex. The Journal of neuro- science the official journal of the Society for Neuroscience, (1(29):00689 10700, July 2011.
ISSN 0270-6474. doi: (0.1523fnecrrocii6441--000 10.2011. URL https: https:://errppppmtttt org/
axticles/PMC662255
Maria Lavzin, Sophia Rapoport, Alon Polsky, Liora Garion, and Jackie Schiller. Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature, 490(74200:397- 401, 2012.
Kuo-Sheng Lee, Alastair J. Loutit, Dominica de Thomas Wagner, Mark Sanders, Mario Prsa, and Daniel Huber. Transformation of neural coding for vibrotactile stimuli along the ascend- ing somatosensory pathway. Neuron, 2024. ISSN 0896-6273. doi: https:/ddi..rg//00.1010
neuron.2024.07 005. URL https: www.sccieneeirecttt com/ scnm//eeenn/artillle
p2115089662322404485
Christopher W. Lynn and Danielle S. Bassett. The physics of brain network structure, func-
tion and control. Nature Reviews Physics, 1:318-332, 2018. URL https://api.
semanticscholate wwwphhoolrrrrgg/oooooooee wwtg/oorpsssiiaacooooooooo :ETID:5229255
Rodrigo Musoo-Castanidaa, Brian Zingg, Katherine S Matho, Xiaoyin Chen, Quanxin Wang, Nicholas N Foster, Anan Li, Arun Narasimhan, Karla E Hirokawa, Bingxing Huo, et al. Cel- lular anatomy of the mouse primary motor cortex. Nature, 598(7877):159-166) 2021.
Seung Wook Oh, Julie A. Harris, Lydia Ng, Brent Winslow, Nicholas Cain, Stefan Mihalas, Quanxin Wang, Christopher Lau, Leonard Kuan, Alex M. Henry, Marty T. Mortrud, Ben- jamin Ouellette, Thue Nghi Nguyen, Staci Sorensen, Cliff Slaughterbeck. Wayne Wake- man, Yang Li, David Feng, Anh Ho. Eric Nicholas, Karla E. Hirokawa, Phillip Bohn, Kevin Joines, Hanchuan Peng, Michael Hawrylyez, John W. Phillips, John G. Hohmann, Paul E. Wohnoutka, Charles R. Gerfen, Christof Koch, Amy Bernard, Chinh Dang, Allan R. Jones, and Hongkui Zeng. mesoscale connectome of the mouse brain. Nature, 508:207 2014. URL https: //api semanticsciieiec ar. org/ Corpus ID: :1::::4455555 4465407)
Libiao Pan, Lu Zheng, Xiaotong Wu, Zhenggang Zhu, Siyu Wang. Yi Lu, Yang He, Qian Yang, Xiaolin Ma, Xiaomeng Wang, et al. A short period of early life oxytocin treatment rescues social behavior dysfunction via suppression of hippocampal hyperactivity in male mice. Molecular Psychiatry, 27(10):115-4111 4171, 2022.
Simon Peron, Jeremy Freeman, Vijay Iyer, Caiying Guo, and Karel Svoboda. A cellular resolution map of barrel cortex activity during tactile behavior. Neuron, 86(3):783-799,
2015. 2015.
Carl CH Petersen. Sensorimotor processing in the rodent barrel cortex. Nature Reviews Neuro- science, 20(9):53--546, 2019.
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias, and Muham- mad Shafique. Embodied neuromorphic artificial intelligence for robotics: Perspectives, chal- lenges, and research development stack, 2024. URL
03325
Julian Rossbroich, Julia Gygax, and Friedemann Zenke. Fluctuation-driver initialization for spiking neural network training. Neuromorphic Computing and Engineering, 2(4):044016, December 2022. ISSN 2634-4386. doi: 10.108872634443386604777666
|RL /xxx doi. ozq/o2.
1 /708/2234-4386//
12

---

Published as a conference paper at ICLR 2025
B Semihcan Sermet, Pavel Truschow, Michael Feyerabend, Johannes M Mayrhofer, Tess B Oram, Ofer Yizhar, Jochen Staiger, and Carl CH Petersen. Pathway- layer-and cell-type-speciffe thalamic imput to mouse barrel cortex. elife, 8:e52665, 2019.
Jochen F Staiger and Carl CH Petersen. Neuronal circuits in barrel cortex for whisker sensory perception. Physiological reviews, 101(1):353-415 2021.
Corinne Teeter, Ramakrishnan Iyer, Vilas Menon, Nathan Gouwens, David Feng, Jim Berg, Aaron Szafer, Nicholas Cain, Hongkui Zeng, Michael Hawrylycz. et al. Generalized leaky integrate- and-fire models classify multiple neuron types. Nature communications, 9(1):709, 2018.
Yang Tian, Zeren Tan, Hedong Hou, Guoqi Li, Aohua Cheng, Yike Qiu, Kangyu Weng, Chun Chen, and Pei Sun. Theoretical foundations of studying criticality in the brain. Network Neuroxcience, 6(4): 10 2022. ISSN 2472-1751. doi: 10.1 1.2.1666/eenaa000 000269, URLhttps://doi. org 10. 10l.0.112/neeeee netn 00269
Amin Ullah, Jamil Ahmad, Khan Muhammad, Muhammad Sajjad, and Sung Wook Baik. Action recognition video sequences using deep bi-directional Istm with enn features. IEEE access, 6: 1155-1166, 2017.
Quanxin Wang, Song-Lin Ding, Yang Li, Josh Royall, David Feng, Phil Lesnar, Nile Graddis, Maitham Naeemi, Benjamin Facer, Anh Ho, al. The allen mouse brain common coordinate framework: 3d reference atlas. Cell, 18 (4):936-555 2020.
Xiao-Jing Wang. Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition. Annual review of neuroscience, 45:533-560, 2022.
W Bryan Wilent and Diego Contreras. Dynamics of excitation and inhibition underlying stimulus selectivity in rat somatosensory cortex. Nature neuroscience, 1030:///...00/mmm11 103./n0133nnnm
October 2005.
ISSN 1097-6256. doi: 10.1038/mn 1500//n1545.... URR.httts://ddi.ooo
Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, Zhaoxu Yang, and Guoqi Li. Temporal-wise attention spiking neural networks for event streams classification. In 2021
IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10201-10210, 2021. doi:
10. N0.1100JCCV44992220..00000
Zizhen Yao, Cindy TJ van Velthoven, Michael Kunst, Meng Zhang, Delissa McMillen, Changkyu Lee. Won Jung, Jeff Goldy, Aliya Abdelhak, Matthew Aitken, et al. high-resolution transcrip- tomic and spatial atlas of cell types in the whole mouse brain. Nature, 624(79911:317-332, 2023.
Bojian Yin, Federico Corradi, and Sander M. Bohté. Effective and efficient computation with multiple-timescale spiking recurrent neural networks. CoRR, abs/2005.11633, 2020. URL
https: http://ppppppppp/opppb/p/p//paaa abs/2005.116..
Chengting Yu, Zheming Gu, Da Li, Gaoang Wang, Aili Wang, and Erping Li. Stsc-snn: Spatio- temporal synaptic connection with temporal convolution and attention for spiking neural net- doi: 10.3389/fnins.
works. Frontiers in Neuroscience, 16, December 2022. ISSN 1662-453X.
2022.1079357. URL http:t/://dx doi. org. 10. 3389/ fnins. 2022.0222222 1079357
Qingbao Yu, Yuhui Du, Jiayu Chen, Jing Sui, Tülay Adalē, Godfrey D. Pearlson, and Vince D. Calhoun. Application of graph theory to assess static and dynamic brain connectivity: Approaches for building brain graphs. Proceedings of the IEEE, 106:886-906, 2018. URL Ittpp:///pp.. semanticscho www.aaa.ooooo/ooooooooee 11:3309022
Zhaoran Zhang and Edward Zagha. Motor cortex gates distractor stimulus encoding in sensory cortex. Nature communications, 14(1):2097, 2023.
Hanle Zheng, Zhong Zheng, Rui Hu, Bo Xiao, Yujie Wu, Fangwen Yu, Xue Liu, Guogi Li, and Lei Deng. Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nature Communications, 15(1):277, 2024.
13

---

Published as a conference paper at ICLR 2025
Muhua Zheng, Antoine Allard, Patric Hagmann, Yasser Alemán-Gómez, and M. Ángeles Ser- rano. Geometric renormalization unravels self-similarity of the multiscale human connec- tome. Proceedings of the National Academy of Sciences, 117(33):20244 20253, 2020. doi:
10.1073/pnas. 192.a...9222817... URL htt.sttts:// http:://www...aa.. pnas wttpw/ppppppppppp.oooootto wwt.///////////o////// /atoo//abb/0000 1073////1.10/ 1922248117
Chengxu Zhuang, Jonas Kubilius, Mitra JZ Hartmann, and Daniel L Yamins. Toward goal- driven neural network models for the rodent whisker-trigeminall system. In I. Guyon, U. Von Luxburg, S. Bengio, Wallach, R. Fergus, Vishwanathan, and Garnett (eds.), Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL https::/pppceedinnn.eaarrr.....s Fil.aaaab1aaaaaccccaaaa777222233
Saapseer_iieeeppppeeeeee files/paper/2017
Katharina Ziegler, Ross Folkard, Antonio Gonzalez, Jan Burghardt, Sailaja Antharvedi- Je- sus Martin-Cortesd Emilio Isafas-Camacho, Sanjeev Kaushalya, Linette Liqi Tan, Thomas Kuner, et al. Primary somatosensory cortex bidirectionally modulates sensory gain and noci- ceptive behavior in layer-specific manner. Nature communications, 14(1):2999, 2023.
A APPENDIX
A.1 IMPLEMENTATION DETAILS
(BPTT) train our network, adopting the cross- entropy loss function. Our network model was trained for 250 epochs using the Adam optimizer, with initial learning rate of 0.08, which was decayed by factor of 0.8 every 20 epochs. The batch size was set to 128. Iin and W snst discussed in section 3.2 were set to 0.04 and 0.06 respectively. All experiments were carried networks (ANNs) under comparison (Zhuang et al., 2017 Collins et al. 2017 Ullah et al.) 2017). adhere the existing experimental setup (Zhuang al. 2017), utilizing the original real- valued
We employ TekkproppgatioonThroug-----
single NVIDIA AT00 80 GB GPU. For the artificial neural
data inputs. In the experiments presented in Tab.[] spiking neural networks (SNNs) (Yu et al. 2022 Yin et al., 2020; Yao al. based dataset devised in Sec. 3.3 Additionally, unless otherwise specified, other experiments of our model are based on the spiking- based dataset.
2021 are tested on both the original real-valued dataset and the spiking-
Whisker deprivation: We randomly set varying numbers of whisker signals in the test set samples zero and recorded the accuracy of trained models. This process was repeated multiple times, and the average accuracy was presented as the results in Tab. The experiment of depriving one whisker was repeated times, while the other experiments were each repeated 50 times.
14


Reviews:
[{"review_id": "oIXhiHjQvt", "reviewer": "Reviewer_YywY", "summary": "The authors presents a biologically constrained model of the mouse barrel cortex, comprising 4,218 neurons across 13 neuronal subtypes, with neural distribution and connection strengths constrained by anatomical experimental findings. \n\nThe authors claim to develop an efficient training algorithm tailored for this model and convert an existing simulated whisker sweep dataset into a spiking-based format. \n\nTheir model achieves a classification accuracy exceeding classical ANN architectures by an average of 8.6%, and is on par with recent spiking neural networks (SNNs) in performance, and outperforms SNNs in whisker deprivation experiments.\n\nPost-training analysis reveals that neurons within the model exhibit firing characteristics and distribution patterns similar to those observed in the actual neuronal systems of the barrel cortex.", "questions": "- Why can't the authors feed continuous signals into the spiking network and let it convert to spiking?\n- In the comparison with convolutional networks, are they tested on the original dataset or on the spiking one?\n- Occlusion results are interesting. A panel with performance decay across the different models would be very useful. What is the comparison with the mentioned biological experiments?\n- Did the authors investigate the importance of the heterogeneity due to different neuron types? E.g. the comparison between homogeneous and uniform models classification performances. Also, I would ask the authors improve the introduction of dynamic gradients.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-The authors develop an efficient training framework specifically designed for a biologically constrained model of the barrel cortex, aimed at achieving realistic sensory-motor integration.   \n-The authors meticulously construct a biologically realistic network comprising 4,218 neurons across 13 neuronal subtypes, ensuring an authentic replication of the biological structure of the barrel cortex.   \n-The classification accuracy of their model surpasses that of mainstream models like CNNs, RNNs, and LSTMs, and is competitive with recent SNNs, particularly in neuroscience-inspired whisker deprivation experiments.", "weaknesses": "- Reliability of the biological model: There are always free parameters difficult to be constrained, such as noise shape and exact synaptic efficacy. Small changes in these parameters can lead to completely different dynamical states and computational properties, and I am not sure the the CV is enough to asses the plausibility of the models activity. Can the author comment on how does this affect the generalizability of the findings?   \n\n- Generality of the comparison with standard ANNs: Why is this model so much better than ANN? Is the improvement due to spikes or to the architecture? How would an ANN perform if it had a similar topology inspired by the barrel cortex? Fig.6B makes me think that most of the improvement is due to the architecture. Can the author discuss on why?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uX5xBeVhVn", "reviewer": "Reviewer_uNoP", "summary": "The following paper investigates the gap between biological realism and behavioural functionality of embodied intelligence to develop an efficient training algorithm. The paper introduces a framework for bridging the gap between realism and functionality based on the barrel cortex, where it covers over three contributions: biologically realistic network construction, efficient training framework and establishing a novel spiking whisker sweep dataset.", "questions": "Figure 1A: The figure description seems to need some clarity onto the labels (1,2,3). It is unclear whether the number defines the region or the pathway.\nFigure 2A: From my perspective, the connection probability and indication of excitatory and inhibitory connections of the subtypes are not clear. These two appear to be mixed in the diagram and thus require more clarification in the diagram.\nEquation 3: Please elaborate more on what beta defines.\nMore clarity on the dataset used for table 1 and 2: It is mentioned that it uses a spiking whisker sweep dataset in page 3 but it would be better if it is mentioned in the table descriptions as well for better clarity.\nThe use of the barrel cortex as a foundation to bridge the gap between efficient training and biological plausibility is well-motivated. The introduction of a biologically constrained model, trained by synchronising neuronal electrical activity, is an interesting approach as well. Given the paper’s focus on an efficient training", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper clearly indicates the gap that exists between the training and biological plausibility of models and current progress in closing this gap.\nI really like the idea on how the original weights are constrained to simulate the connections to represent the 13 neuronal subtypes.\nMapping from real to discrete space using a sigmoid function for the dataset is interesting.\nTraining for synchronisation of neuronal activity is a novel and effective approach, enhancing both stability and biological realism in sensory-motor integration models.", "weaknesses": "The paper mentions that it has a better classification accuracy compared to models of CNNs, RNNs, LSTMs and SNN. CNNs, for e.g. work on the spatial structure of the dataset, and thus this should be interesting to see in both the original and currently introduced spiking dataset since spikes deal more on the temporality of the data.\nThe structure of the paper is lacking (it seems to be going back and forth spontaneously between different sections in my opinion) although the information provided is sufficient. An example is that some of the information mentioned in Figure 2 are better mentioned after an explanation on the methodology. These took quite some time to understand until reaching section 3. Moreover, the task mentioned in contributions on the spiking dataset (page 3) would benefit better if it also uses figure 3 as a supplementary material to explain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nL8W9HFPkh", "reviewer": "Reviewer_PD9T", "summary": "This paper developed a trainable Barrel circuit, whose connection backbone is constrained by biological data of Barrel cortex, to classify object based on (simulated) whisker input. The network is composed of identical adaptative LIF neurons, with tranable timescale parameters. The parameter distribution and response property are analyzed after the training, which is argued to have qualitative similiarity with biological data. The model is compared with several baseline ANN and SNN models based on spiking-based dataset.", "questions": "See above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper build up a trainable bioplausible model for touch, combining the function with bio-interpretation. This is an important and interesting direction in general. On the techinical side, the paper applies the coefficient of variation to determine the desirable initial configuration of a dynamical network model. It also convert a touch dataset into spiking version by Bernoulli sampling based on firing rate. On the discovery side, the author find bio-related feature in the network after the training, including neural selectivity, shift in timescale parameters, similiar response activity as biological data, long-tailed degree distribution, etc. These make it a promissing machine learning approach to study touch.", "weaknesses": "While the overall picture is promissing, the novelty and significance of the paper may not meet the standard of ICLR.\n- The training of aLIF network has been realized in a list of previous works, e.g. Chen2022 cited in the paper. And using connection probability to constrain the network topology is also not new\n- The proposed CV measure is only showcased in Fig2 based on three selected configurations. It is not clear whether the CV is a significant/convincing measure of the initialization for training and whether it is generalizable to other dynamical bio-plausible models. For example, it is not clear how three configuration is selected and the results is not averged by different random seeds. Also, in FigC, the different initialization do not affact the initial traning process(0-10 epoch), which does not make sense to me if three initialization actually results in bifurcation of the dynamics regions. And the Fig.2 D all shows global synchrony dynamics (even for high CV), which still lack of variability and is not a realistic working regime in the brain .\n-  The conversion of real-valued dataset to spiking-based dataset is not a significant contribution, and it is not clear whether the converted spiking data actually mimic the input in biological circuit.\n- As a result, while the overall picture is interesting, the specific technical contribution of this paper beyond the related work is limited.\n\nOn the experiment side, the significance of result is also my concern:\n- Compare with ANN: the ANN is compared based on spiking dataset. So it is not clear whether the poor performance of ANN is due to the data type of input. Since it is not necessary to use spiking data as input to spiking network, the author should also compare the model with ANN on original non-spiking dataset, to disentangle the whether it is the network dynamic or just the data type that acturally make the difference.\n- Firing selectivity: it is not a suprise to see the symmetry breaking after the training. The author should compare the selectivity property with other SNN models (after training on this task) to stress the significance of the result.\n- The shift of parameter is also not a suprise. It would be a suprise if the shifted distribution mimic that in real circuit. Without such direct comparison, it is not clear whether the shift is a specific property of this model, that have a meaning or just a general phenomenon due to training, given heterogeneous connection backbone.\n- The firing rate profile, it is not clear how the this profile is computed: is identical curent applied to each subpopulation seperately or applied to the entire network? Besides, the profiles of different neuron type differ not only in firing rate but also specific spike patterns, like tonic, bursting,...and their responsive property to stimulus ('on','off','on-off'). Can author show more detailed comparison with biological response profile as support.\n- For the long-tail degree distribution, it is also not clear whether it is a specific property of this model or a general property of training. I suggest the author to compare the result with other SNN / ANN, to clarify this.\n\nLastly, the code of the model is not provided, so it is hard to evaluate the solidness of the results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JmZxijZsfb", "reviewer": "Reviewer_tE3s", "summary": "The paper presents a novel biologically constrained spiking network model of the mouse barrel cortex, introduces an initialization technique for it based on the coefficient of variation of the network activity, and evaluates both in conjunction on a newly spike converted whisker sweep dataset. The proposed method shows favorable performance compared to other even non-constrained network models, and fully trained versions of it more closely reproduce biologically observed network characteristics such as increased neuron selectivity, neuron type dependent firing rate and timescale distribution, and neuron weighted degree distribution.", "questions": "1) The firing activity in figure 2D seem very synchronized and periodic, does this reflect real work data, and do you have example visualization of such data?\n2) Any explanation as to why the anatomic connection probability is best? Were the initialization adjusted individually for each of the connection probabilities?\n3) For each of the network characteristics findings; do you believe you find this effect due to the biological constrains or simply gradient based training in networks? \n4) Where any normalization or regularization techniques used for training, or gradient clipping?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper is really well written with predominently precise use of terminology.\n- It tackles an interesting and really challenging problem; effective training of biologically constrained networks, and computationally characterizing them.\n- Achieving competitive performance on benchmarks given this many biological constraints, even if potentially computationally more expensive, is no easy feat.\n- Also impressive is the sheer number of directions in which training the networks increased the similarity to biological network statistics.\n- The dimensions along which the network was characterized are well motivated, and the used quantitative measures are really creative and nicely connected back to neuroscience literature.", "weaknesses": "That the training methodology is really more efficient was not convincingly shown, and to call it a training framework might be a stretch:\n   * 1.1) An effective initialization technique was shown (the coefficient of variation approach), yet no qualitative assessment of efficiency, such as Performance/FLOPs or Performance/Samples is provided to warrant the statement of “efficient” from my understanding. \n   * 1.2) Also consider, that figure 2C doesnt even show the efficacy convincingly, as initially all three visualized initializations perform similarly well (until around epoch 20), and subsequently run into training stability issues (examplified by the sudden drop/rise in performance). In fact even the supposed best initialization (according to the coefficient of variation) displays two mayor drops in performance during training. This could be more indicative of exploding gradient issues or extreme parameter regimes, which would rather be fixed by gradient clipping or appropriate regularization and normalization techniques: no mention of such things in the methods?\n   * 1.3) Furthermore in panel 2D the network displays pathological firing synchronization in both cases, unless I’m missing something (how does a biological recording from that brain region typically look like?). This could be indicative of a lack of decorrelating mechanisms or insufficient inhibition, which to my knowledge the brain typically has.\n   * 1.4) Lastly, no comparison to other initialization techniques typical in the spiking literature was conducted, such as simply scaling synapse weights by in-degree, or more sophisticated ones (e.g. see Rossbroich et. al 2022).\n* However, somewhat reformulating the claims and contributions probably is sufficient to make the paper consistent.\n\nThe computational characterizations consistently lacks a control / comparison to interpret the results, and are possibly merely side effects of simple gradient descent training in networks?\n   * 2.1) For neuron specific firing selectivity: it would be interesting to see how the CNNs or LSTMs neuron specificity (or similar measure) behaves? If this doesnt happen for them it’s more convincing that this is because of the biological constraint and not simply due to generic gradient optimization.\n   * 2.2) For the differentiated neuronal dynamics: when initializing to a single value and training with backprop naturally the distribution become smudged, and when margenalizing over neuron type there will naturally be differences. Whether these differences here are statistically significant from random drift, or whether they reflect more closely the biological ground truth, remains unclear from the paper? The firing rate example is more convincing, yet the effect might be primarily driven by only three subtypes (6,7 and 9) and the impact of normalizing the firing rates is unclear to me. Some sort of additional control would further substantiate the point.\n   * 2.3) For the weight degree distribution: one-sided metrics evaluated on variables with low variance will typically also display a longer tailed distribution in networks after training, as the variable gets smudged and the interval is only open to one side. Without a control of sorts it’s hard to understand whether in this case this is an effect of the cortical constrains or simply due to backpropagation and this being a directed graph. Evaluating this or a similar metric for e.g. the LSTM could possibly serve as a control / comparison here too.\n   * 2.4) Regarding the connectivity probability: this is more convincing, yet only IFF the network was properly re-initialized for the changed connectivity, e.g. using the proposed coefficient of variation technique.\n   * However, it probably cannot be expected to also get explanations as to “why” we see these effects in a single paper, and therefore an extension of the future work section and making ther readers aware of these possible limitation should be enough to address most of these issues.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Spotlight)