nils-herrmann/clean_scirepeval huggingface dataset:
Computational Models of Thalamocortical Augmenting Responses

Repetitive stimulation of the dorsal thalamus at 7–14 Hz produces an increasing number of spikes at an increasing frequency in neocortical neurons during the first few stimuli. Possible mechanisms underlying these cortical augmenting responses were analyzed with a computer model that included populations of thalamocortical cells, thalamic reticular neurons, up to two layers of cortical pyramidal cells, and cortical inhibitory interneurons. Repetitive thalamic stimulation produced a low-threshold intrathalamic augmentation in the model based on the deinactivation of the low-threshold Ca2+ current in thalamocortical cells, which in turn induced cortical augmenting responses. In the cortical model, augmenting responses were more powerful in the “input” layer compared with those in the “output” layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. Thalamic stimulation was more effective in eliciting augmenting responses than cortical stimulation. Intracortical inhibition had an important influence on the genesis of augmenting responses in cortical neurons: A shift in the balance between intracortical excitation and inhibition toward excitation transformed an augmenting responses to long-lasting paroxysmal discharge. The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses.
Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association

In natural visual experience, different views of an object or face tend to appear in close temporal proximity. A set of simulations is presented which demonstrate how viewpoint invariant representations of faces can be developed from visual experience by capturing the temporal relationships among the input patterns. The simulations explored the interaction of temporalsmoothing of activity signals with Hebbian learning Foldiak, 1991 in both a feed-forward system and a recurrent system. The recurrent system was a generalization of a Hopfield network with a lowpass temporal filter on all unit activities. Following training on sequences of graylevel images of faces as they changed pose, multiple views of a given face fell into the same basin of attraction, and the system acquired representations of faces that were approximately viewpoint invariant.
Cholinergic modulation of spike timing and spike frequency adaptation in neocortical neurons

Neocortical neurons in vivo are spontaneously active and intracellular recordings have revealed strongly fluctuating membrane potentials arising from the irregular arrival of excitatory and inhibitory synaptic potentials. In addition to these rapid fluctuations, more slowly changing influences from diffuse activation of neuromodulatory systems alter the excitability of cortical neurons by modulating a variety of potassium conductances. In particular, acetylcholine, which affects learning and memory, reduces the slow afterhyperpolarization, which contributes to spike frequency adaptation. We used whole cell patch clamp recordings of pyramidal neurons in neocortical slices and computational simulations to show, first, that when fluctuating inputs were added to a constant current pulse, spike frequency adaptation was reduced as the amplitude of the fluctuations was increased. High-frequency, high-amplitude fluctuating inputs that resembled in vivo conditions  elicited only weak spike frequency adaptation. Second, bath application of carbachol, a cholinergic agonist, significantly increased the firing rate in response to a fluctuating input but minimally displaced the spike times by less than 3 milliseconds, comparable to the spike jitter observed when a visual stimulus is repeated under in vivo conditions (see  for more details on experiments and simulations). These results suggest that cholinergic modulation may preserve information encoded in precise spike timing, but not in interspike intervals, and that cholinergic mechanisms other than those involving adaptation may contribute significantly to cholinergic modulation of learning and memory.
A Computational Model of Birdsong Learning by Auditory Experience and Auditory Feedback

In addition to the goal of acquiring a precise description of the acoustic environment, central auditory processing also provides useful information for animal behaviors, such as navigation and communication. Singing is a learned behavior of male songbirds for protecting territories and attracting females (Konishi, 1985; Catchpole and Slater, 1995). It has been experimentally shown that singing behavior depends on auditory information in two ways. First, the phonetic features of a bird’s song depends on the bird’s auditory experience during a limited period after birth. Second, the development of songs of a juvenile bird depends on the auditory feedback of its own vocalization.
Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons.

We investigate the spatial and temporal aspects of firing patterns in a network of integrate-and-fire neurons arranged in a one-dimensional ring topology. The coupling is stochastic and shaped like a Mexican hat with local excitation and lateral inhibition. With perfect precision in the couplings, the attractors of activity in the network occur at every position in the ring. Inhomogeneities in the coupling break the translational invariance of localized attractors and lead to synchronization within highly active as well as weakly active clusters. The interspike interval variability is high, consistent with recent observations of spike time distributions in visual cortex. The robustness of our results is demonstrated with more realistic simulations on a network of McGregor neurons which model conductance changes and after-hyperpolarization potassium currents.
Independent component representations for face recognition

In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which separates the high-order moments of the input in addition to the second-order moments. ICA was performed on a set of face images by an unsupervised learning algorithm derived from the principle of optimal information transfer through sigmoidal neurons. The algorithm maximizes the mutual information between the input and the output, which produces statistically independent outputs under certain conditions. ICA was performed on the face images under two different architectures. The first architecture provided a statistically independent basis set for the face images that can be viewed as a set of independent facial features. The second architecture provided a factorial code, in which the probability of any combination of features can be obtained from the product of their individual probabilities. Both ICA representations were superior to representations based on principal components analysis for recognizing faces across sessions and changes in expression.
Modeling active dendritic processes in pyramidal neurons

The role of active ion channels in dendritic function is among the most interesting and complex aspects of information processing in single neurons While the behavior of isolated channels or the passive electrical properties of dendrites can be studied in isolation, the interaction of multiple nonlinear ionic currents within a geometrically complex structure is described by equations that cannot be solved analytically and often resist intuition. Detailed computer models thus provide a crucially needed framework within which hypotheses about active dendritic mechanisms can be expressed and tested. Realistic neuronal models help determine whether known biophysical properties can account for experimental observations and provide insight into computational functions of these mechanisms. The chief obstacle in constructing such models is obtaining constraints for their parameters. Until recently, not enough experimental data were available on the physiological properties of cortical neurons to be able to create accurate models. In early models of hippocampal and neocortical neurons (e. properties of the channels were mixed and matched from cortical and noncortical neurons, including motoneurons in the spinal cord, sympathetic ganglion cells, and even the squid giant axon. Still scantier data were available regarding the spatial distribution of channels present in dendrites, axons, and synaptic terminals. Fortunately, the application of dendritic patch recordings, ca2+ imaging, and immunocytochem-istry and other molecular techniques is now yielding an abundance of precise data on the properties and localization of ion channels in the dendritic membrane. Furthermore , dendritic recordings and imaging of ca2+-or voltage-sensitive dyes provide windows into dendritic behavior not available with traditional somatic recordings. Thus the time is ripe for detailed models of active dendritic computation. The basics of compartmental models are presented in chapter 3, this volume, and numerical methods for solving them in chapter 14. In the present chapter, we show how compartmental simulations can be used to model neurons with active dendritic ion channels; our focus is chiefly on modeling pyramidal neurons such as those of the neocortex and hippocampus, but much of the material can be applied more widely. In section 5.2, we present definitions of channel types, their densities, and their localization, and we discuss the steps involved in combining experimental data into a
Dynamic remapping

The term dynamic remapping has been used in many different ways, but one of the clearest formulations of this concept comes from the mental rotation studies by Georgopoulos et al. (1989; see also REACHING: CODING IN MOTOR CORTEX). In these experiments, monkeys were trained to move a joystick in the direction of a visual stimulus, or 90" counterclockwise from it. The brightness of the stimulus indicated which movement was required on a particular trial: a dim light corresponding to a 90" movement and a bright light to a direct movement. An analysis of reaction time suggested that, by default, the initial motor command always pointed straight at the target and then continuously rotated if the cue indicated a 90" rotation, an interpretation that was subsequently confirmed by single-unit recordings. The term remapping is also commonly used whenever a sensory input in one modality is transformed to the sensory representation in another modality. The best-known example in primates is the remapping of auditory space, which is headcentered in the early stages of auditory processing, into the retinotopic coordinates used in the superior colliculus (Jay and Sparks, 1987; Stein and Meredith, 1993). This type of remapping, equivalent to a change of coordinates, is closely related to sensorimotor transformations. It does not have to be performed over time but could be accomplished by the neuronal circuitry connecting different representations. This review is divided into three parts: In the first part, we briefly describe the types of cortical representations typically encountered in dynamic remapping. We then summarize the results from several physiological studies in which it has been possible to characterize the responses of neurons involved in temporal and spatial remappings. Finally, in the third part, we review modeling efforts to account for these processes.
Reliable disparity estimation through selective integration.

A network model of disparity estimation was developed based on disparity-selective neurons, such as those found in the early stages of processing in the visual cortex. The model accurately estimated multiple disparities in regions, which may be caused by transparency or occlusion. The selective integration of reliable local estimates enabled the network to generate accurate disparity estimates on normal and transparent random-dot stereograms. The model was consistent with human psychophysical results on the effects of spatial-frequency filtering on disparity sensitivity. The responses of neurons in macaque area V2 to random-dot stereograms are consistent with the prediction of the model that a subset of neurons responsible for disparity selection should be sensitive to disparity gradients.
Spatially independent activity patterns in functional MRI data during the stroop color-naming task.

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.
A Computational Model of How the Basal Ganglia Produce Sequences

We propose a systems-level computational model of the basal ganglia based closely on known anatomy and physiology. First, we assume that the thalamic targets, which relay ascending information to cortical action and planning areas, are tonically inhibited by the basal ganglia. Second, we assume that the output stage of the basal ganglia, the internal segment of the globus pallidus (GPi), selects a single action from several competing actions via lateral interactions. Third, we propose that a form of local working memory exists in the form of reciprocal connections between the external globus pallidus (GPe) and the subthalamic nucleus (STN). As a test of the model, the system was trained to learn a sequence of states that required the context of previous actions. The striatum, which was assumed to represent a conjunction of cortical states, directly selected the action in the GP during training. The STN-to-GP connection strengths were modified by an associative learning rule and came to encode the sequence after 20 to 40 iterations through the sequence. Subsequently, the system automatically reproduced the sequence when cued to the first action. The behavior of the model was found to be sensitive to the ratio of the striatal-nigral learning rate to the STN-GP learning rate. Additionally, the degree of striatal inhibition of the globus pallidus had a significant influence on both learning and the ability to select an action. Low learning rates, which would be hypothesized to reflect low levels of dopamine, as in Parkinson's disease, led to slow acquisition of contextual information. However, this could be partially offset by modeling a lesion of the globus pallidus that resulted in an increase in the gain of the STN units. The parameter sensitivity of the model is discussed within the framework of existing behavioral and lesion data.
Memory and Neural Networks

The brain's operation depends on networks of nerve cells, called neurons, connected with each other by synapses. Scientists can now mimic some of the brain's behaviours with computer-based models of neural networks. One major domain of behaviour to which this approach has been applied is the question of how the brain acquires and maintains new information; that is, what we would call learning and memory. Neural networks employ various learning algorithms, which are recipes for how to change the network to store new information, and this chapter surveys learning algorithms that have been explored over the last decade. A few representative examples are presented here to illustrate the basic types of learning algorithms; the interested reader is encouraged to consult recent books listed in the section on Further reading, which present these algorithms in greater detail and provide a more complete survey. It is important to keep in mind that the models of neural networks that are simulated in computers are far simpler than the highly complex and often messy neural systems that nature has devised. Although much simpler, neural network models capture some of the most basic features of the brain and may share some general properties that will allow us to understand better the operation of the more complex system. Neural network models are built from simple processing units that work together in parallel, each concerned with only a small piece of information.
Analyzing and Visualizing Single-Trial Event-Related Potentials

Event-related potentials (ERPs), are portions of electroencephalographic (EEG) recordings that are both time- and phase-locked to experimental events. ERPs are usually averaged to increase their signal/noise ratio relative to non-phase locked EEG activity, regardless of the fact that response activity in single epochs may vary widely in time course and scalp distribution. This study applies a linear decomposition tool, Independent Component Analysis (ICA) , to multichannel single-trial EEG records to derive spatial filters that decompose single-trial EEG epochs into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain networks. Our results on normal and autistic subjects show that ICA can separate artifactual, stimulus-locked, response-locked, and. nonevent related background EEG activities into separate components, allowing (1) removal of pervasive artifacts of all types from single-trial EEG records, and (2) identification of both stimulus- and response-locked EEG components. Second, this study proposes a new visualization tool, the 'ERP image', for investigating variability in latencies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. We show that sorting single-trial ERP epochs in order of reaction time and plotting the potentials in 2-D clearly reveals underlying patterns of response variability linked to performance. These analysis and visualization tools appear broadly applicable to electrophyiological research on both normal and clinical populations.
Independent component analysis of fMRI data: Examining the assumptions

Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log‐likelihood of observing each voxel's time course conditioned on the ICA model. The probability of observing the time courses from white‐matter voxels was higher compared to other observed brain regions. Regions containing blood vessels had the lowest probabilities. The statistical distribution of probabilities over all voxels did not resemble that expected for a small number of independent components mixed with Gaussian noise. These results suggest the ICA model may more accurately represent the data in specific regions of the brain, and that both the activity‐dependent sources of blood flow and noise are non‐Gaussian.  Brain Mapping 6:368–372, 1998.
Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization

Computational models of neural map formation can be considered on at least three different levels of abstraction: detailed models including neural activity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, and constrained optimization from which equations governing weight dynamics can be derived. Constrained optimization uses an objective function, from which a weight growth rule can be derived as a gradient flow, and some constraints, from which normalization rules are derived. In this article, we present an example of how an optimization problem can be derived from detailed nonlinear neural dynamics. A systematic investigation reveals how different weight dynamics introduced previously can be derived from two types of objective function terms and two types of constraints. This includes dynamic link matching as a special case of neural map formation. We focus in particular on the role of coordinate transformations to derive different weight dynamics from the same optimization problem. Several examples illustrate how the constrained optimization framework can help in understanding, generating, and comparing different models of neural map formation. The techniques used in this analysis may also be useful in investigating other types of neural dynamics.
Thalamocortical oscillations in sleep and wakefulness

metric representation and tedious parameter estimation, the BPNN probabilistic modeling gives a more relaxed and more reliable solution, as evidenced by successful application to textured image modeling and segmentation of both artificial and real-world textures. It can be argued that the proposed BPNN modeling approach requires more parameters (interconnection weights of the BPNN) than the number of clique parameters used in a conventional M R F modeling. Even though this challenge is valid, there is no way to increase the number of clique parameters for a better performance without increasing the order of the neighborhood system under the very restricted MRF formulation. More importantly, when the order of the neighborhood system increases in an M R F model, the number of clique parameters increases exponentially, and the parameter estimation performance degrades rapidly. Conversely, the BPNN probabilistic modeling increases its number of interconnection weights linearly (i.e., the input dimension increases), with slight performance degradation observed from our experience. In spite of its seemingly superior performance based on a limited set of simulations, the proposed BPNN texture modeling can possibly suffer performance degradation in the presence of texture rotation, texture scaling, and also larger number of gray levels. All these difficulties are yet to be overcome before the BPNN texture modeling can be of practical use.