nils-herrmann/clean_scirepeval huggingface dataset:
Population dynamics and theta rhythm phase precession of hippocampal place cell firing: A spiking neuron model

O'Keefe and Recce ([1993] Hippocampus 68:317–330) have observed that the spatially selective firing of pyramidal cells in the CA1 field of the rat hippocampus tends to advance to earlier phases of the electroencephalogram theta rhythm as a rat passes through the place field of a cell. We present here a neural network model based on integrate‐and‐fire neurons that accounts for this effect. In this model, place selectivity in the hippocampus is a consequence of synaptic interactions between pyramidal neurons together with weakly selective external input. The phase shift of neuronal spiking arises in the model as a result of asymmetric spread of activation through the network, caused by asymmetry in the synaptic interactions. Several experimentally observed properties of the phase shift effect follow naturally from the model, including 1) the observation that the first spikes a cell fires appear near the theta phase corresponding to minimal population activity, 2) the overall advance is less than 360°, and 3) the location of the rat within the place field of the cell is the primary correlate of the firing phase, not the time the rat has been in the field. The model makes several predictions concerning the emergence of place fields during the earliest stages of exploration in a novel environment. It also suggests new experiments that could provide further constraints on a possible explanation of the phase precession effect.
Learning Viewpoint Invariant Representations of Faces in anAttractor

In natural visual experience, diierent views of an object tend to appear in close temporal proximity as an animal manipulates the object or navigates around it. We investigated the ability of an attractor network to acquire view invariant visual representations by associating rst neighbors in a pattern sequence. The pattern sequence contains successive views of faces of ten individuals as they change pose. Under the network dynamics developed by Griniasty, Tsodyks & Amit (1993), multiple views of a given subject fall into the same basin of attraction. We use an independent component (ICA) representation of the faces for the input patterns (Bell & Sejnowski, 1995). The ICA representation has advantages over the principal component representation (PCA) for viewpoint-invariant recognition both with and without the attractor network, suggesting that ICA is a better representation than PCA for object recognition.
Bayesian Unsupervised Learning of Higher Order Structure

Multilayer architectures such as those used in Bayesian belief networks and Helmholtz machines provide a powerful framework for representing and learning higher order statistical relations among inputs. Because exact probability calculations with these models are often intractable, there is much interest in finding approximate algorithms. We present an algorithm that efficiently discovers higher order structure using EM and Gibbs sampling. The model can be interpreted as a stochastic recurrent network in which ambiguity in lower-level states is resolved through feedback from higher levels. We demonstrate the performance of the algorithm on benchmark problems.
How the Basal Ganglia Make Decisions

The primate basal ganglia are a collection of subcortical structures that have long been considered part of the extrapyramidal motor system, the part of the motor system concerned with automatic aspects of movement. Despite a large amount of data regarding their anatomy and physiology, the role of the basal ganglia in both action planning and decision making remains enigmatic. Anatomical labeling studies have suggested that the striatum receives projections from the cerebral cortex that coarsely preserves topography, and that the basal ganglia maintain a segregation of information streams (Goldman-Rakic and Selemon 1986; Hoover and Strick 1993; Parent 1990). We suggest that the connectivity of the basal ganglia is ideally suited to selecting optimal actions for given cognitive and sensory states. We demonstrate how a computational network of pools of neurons connected in the arrangement found in the basal ganglia can perform what we term a “winner-lose-all” function. The winner-lose-all mechanism refers to the fact that the neurons of the output stage of the basal ganglia, the internal segment of the globus pallidus (GPi), are tonically active and are inhibited when corresponding striatal afferents fire. Thus the GPi neuron that is selected is actually inhibited because it loses rather than wins the competition. Diffuse excitatory projections from the subthalamic nucleus prevent all but the winning pallidal neuron pool from being inhibited. Because the thalamic targets of the GPi projection in turn feed back to the approximate cortical area of the originating afferent, this cortical-subcortical loop is ideally suited not only for the aforementioned action-selection, but also for the generation of sequences appropriate for given cortical states. We demonstrate how the circuitry of the basal ganglia can learn to select the best action for different cortical states and how the feedback representation of the action-selection leads to the generation of sequences of actions.
Learning to make predictions in the cerebellum may explain the anticipatory modulation of the vestibulo-ocular reflex (VOR) gain with vergence

Changes in the eye vergence modify the gain of the vestibuloocular reflex (VOR). In a previous dynamical model, this modulation was controlled by the cerebellum using vergence angle information (Coenen & Sejnowski; NIPS 96). However, during a vergence eye movement, the change in the VOR gain anticipates the vergence change (Snyder & King; ). We demonstrate here how our previous dynamical model and a predictive cerebellar model may be combined to explain these findings. In the predictive model, the cerebellum receives inputs from vergence-disparity cells in the cortex to construct a prediction of vergence angle. By replacing the regular vergence input in our previous dynamical model by the vergence prediction, results similar to the experimental anticipatory gain changes are observed. The inferior olive in the predictive cerebellar model is responsible for computing a prediction error and for selecting the Purkinje cells to be recruited for learning a particular prediction. The learning model is based on the least-mean square (LMS) algorithm to construct predictions from previous context information. This model is a special case of a more general predictive function for the cerebellum that may provide a consistent explanation for the apparently ubiquitous task participation of the cerebellum. We discuss briefly how this more general model may explain some experimental results observed in the cerebellum with positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In conclusion, a predictive cerebellar model receiving vergence+parity cell inputs learns to predict the time course of vergence eye movement and successfully changes ihe VOR gain in anticipation of vergence changes. 3rd Joint Symposium on Neural Computation Proceedings 203 ,
Probabilistic Synaptic Transmission in Hippocampal Neurons: Role of Presynaptic Calcium

lie factors governing the apparently stochastic release of neurotransmitter at central synapses are largely unknown. One potential factor is fluctuation in calcium entry into release sites - for instance, failure of transmitter release could be due to failure of calcium entry into preynaptic terminals. We tested this hpthesis by recording action potential-evoked calcium transients at single presynaptic sites of cultured hippocampal neurons. Calcium concentration rises were observed reliably at putative release sites, with no apparent calcium entry failures. However, in some release sites, there was evidenceforf7uctuations in amplitude of calcium transients that exceeded variation estimated @om baseline noise. This trial-to-trial fluctuation in calcium entry could potentially play a role in determining transmitter release probability.
Exploration bonuses and dual control

Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation (Cozzolino, Gonzalez-Zubieta & Miller, 1965) arising from a form of dual control. This system-atizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behavior. This general approach is applied to two-dimensional mazes with moveable barriers and its performance is compared with Sutton's DYNA system.
Edges are the Independent Components of Natural Scenes

Field (1994) has suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and Barlow (1989) has reasoned that such responses should emerge from an unsupervised learning algorithm that attempts to find a factorial code of independent visual features. We show here that non-linear 'infomax', when applied to an ensemble of natural scenes, produces sets of visual filters that are localised and oriented. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximisation network of Olshausen & Field (1996). In addition, the outputs of these filters are as independent as possible, since the infomax network is able to perform Independent Components Analysis (ICA). We compare the resulting ICA filters and their associated basis functions, with other decorrelating filters produced by Principal Components Analysis (PCA) and zero-phase whitening filters (ZCA). The ICA filters have more sparsely distributed (kurtotic) outputs on natural scenes. They also resemble the receptive fields of simple cells in visual cortex, which suggests that these neurons form an information-theoretic co-ordinate system for images.
Blind Separation of Event-Related Brain Responses into Independent Components

Functional imaging of brain activity based on changes in blood flow does not supply information about the relative timing of brief bursts of neural activity in different brain areas. Multichannel electric or magnetic recordings from the scalp provide high temporal resolution, but are not easily decomposed into the separate activities of multiple brain networks. We report here a method for the blind separation of event-related brain responses into spatially stationary and temporally independent subcomponents using an Independent Component Analysis algorithm. Applied to electroencephalographic responses from an auditory detection task, each of the most active identified sources accounted for all or part of a previously identified response component. This spatiotemporal decomposition was robust to changes in sensors and input data length, and was stable within subjects. The method can be used to assess the timing, strength, and stability of event-related activity in brain networks during cognitive tasks, regardless of source location.
Selective Integration: A Model for Disparity Estimation

Local disparity information is often sparse and noisy, which creates two conflicting demands when estimating disparity in an image region: the need to spatially average to get an accurate estimate, and the problem of not averaging over discontinuities. We have developed a network model of disparity estimation based on disparity-selective neurons, such as those found in the early stages of processing in visual cortex. The model can accurately estimate multiple disparities in a region, which may be caused by transparency or occlusion, in real images and random-dot stereograms. The use of a selection mechanism to selectively integrate reliable local disparity estimates results in superior performance compared to standard back-propagation and cross-correlation approaches. In addition, the representations learned with this selection mechanism are consistent with recent neurophysiological results of von der Heydt, Zhou, Friedman, and Poggio  for cells in cortical visual area V2. Combining multi-scale biologically-plausible image processing with the power of the mixture-of-experts learning algorithm represents a promising approach that yields both high performance and new insights into visual system function.
Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input

Neuromodulation can change not only the mean firing rate of a neuron, but also its pattern of firing. Therefore, a reliable neural coding scheme, whether a rate coding or a spike time based coding, must be robust in a dynamic neuromodulatory environment. The common observation that cholinergic modulation leads to a reduction in spike frequency adaptation implies a modification of spike timing, which would make a neural code based on precise spike timing difficult to maintain. In this paper, the effects of cholinergic modulation were studied to test the hypothesis that precise spike timing can serve as a reliable neural code. Using the whole cell patch-clamp technique in rat neocortical slice preparation and compartmental modeling techniques, we show that cholinergic modulation, surprisingly, preserved spike timing in response to a fluctuating inputs that resembles in vivo conditions. This result suggests that in vivo spike timing may be much more resistant to changes in neuromodulator concentrations than previous physiological studies have implied.
Quantifying neighbourhood preservation in topographic mappings

Mappings that preserve neighbourhood relationships are important in many contexts, from neurobiology to multivariate data analysis. It is important to be clear about precisely what is meant by preserving neighbourhoods. At least three issues have to be addressed: how neighbourhoods are defined, how a perfectly neighbourhood preserving mapping is defined, and how an objective function for measuring discrepancies from perfect neighbourhood preservation is defined. We review several standard methods, and using a simple example mapping problem show that the different assumptions of each lead to non-trivially different answers. We also introduce a particular measure for topographic distortion, which has the form of a quadratic assignment problem. Many previous methods are closely related to this measure, which thus serves to unify disparate approaches.
Dynamic Features for Visual Speechreading: A Systematic Comparison

Humans use visual as well as auditory speech signals to recognize spoken words. A variety of systems have been investigated for performing this task. The main purpose of this research was to systematically compare the performance of a range of dynamic visual features on a speechreading task. We have found that normalization of images to eliminate variation due to translation, scale, and planar rotation yielded substantial improvements in generalization performance regardless of the visual representation used. In addition, the dynamic information in the difference between successive frames yielded better performance than optical-flow based approaches, and compression by local low-pass filtering worked surprisingly better than global principal components analysis (PCA). These results are examined and possible explanations are explored.
Unsupervised Learning Of Invariant Representations Of Faces Through Temporal Association

The appearance of an object or a face changes continuously as the observer moves through the environment or as a face changes expression or pose. Recognizing an object or a face despite these image changes is a challenging problem for computer vision systems, yet we perform the task quickly and easily. This simulation investigates the ability of an unsupervised learning mechanism to acquire representations that are tolerant to such changes in the image. The learning mechanism finds these representations by capturing temporal relationships between 2-D patterns. Previous rnodels of temporal association learning have used idealized input representations. The input to this model consists of graylevel images of faces. A two-layer network learned face representations that incorporated changes of pose up to rt30°. A second network learned representations that were independent of facial expression.
Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model.

The Independent Component Analysis (ICA) algorithm is a new information-theoretic approach to the problem of separating multichannel electroencephalographic (EEG) data into independent sources. We tested the potential usefulness of the ICA algorithm for EEG source decomposition by applying the algorithm to simulated EEG data. These data were synthesized by projecting 6 known input signals from single- and multiple-dipole sources in a three-shell spherical model head to 6 simulated scalp sensors. In different simulations, we (1) altered the relative source strengths, (2) added multiple low-level sources (weak brain sources and sensor noise) to the simulated EEG, and (3) permuted the simulated dipole source locations and orientations. The algorithm successfully and reliably separated the activities of relatively strong sources from the activities of weaker brain sources and sensor noise, regardless of source lo- cations and dipole orientations. These results suggest that the ICA algorithm should be able to separate temporally independent but spatially overlapping EEG activities arising from relatively strong brain and/or non-brain sources, regardless of their spatial distributions.
Control of Spatiotemporal Coherence of a Thalamic Oscillation by Corticothalamic Feedback

The mammalian thalamus is the gateway to the cortex for most sensory modalities. Nearly all thalamic nuclei also receive massive feedback projections from the cortical region to which they project. In this study, the spatiotemporal properties of synchronized thalamic spindle oscillations (7 to 14 hertz) were investigated in barbiturate-anesthetized cats, before and after removal of the cortex. After complete ipsilateral decortication, the long-range synchronization of thalamic spindles in the intact cortex hemisphere changed into disorganized patterns with low spatiotemporal coherence. Local thalamic synchrony was still present, as demonstrated by dual intracellular recordings from nearby neurons. In the cortex, synchrony was insensitive to the disruption of horizontal intracortical connections. These results indicate that the global coherence of thalamic oscillations is determined by corticothalamic projections.
Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks

We have explored two approaches to recognizing faces across changes in pose. First, we developed a representation of face images based on independent component analysis (ICA) and compared it to a principal component analysis (PCA) representation for face recognition. The ICA basis vectors for this data set were more spatially local than the PCA basis vectors and the ICA representation had greater invariance to changes in pose. Second, we present a model for the development of viewpoint invariant responses to faces from visual experience in a biological system. The temporal continuity of natural visual experience was incorporated into an attractor network model by Hebbian learning following a lowpass temporal filter on unit activities. When combined with the temporal filter, a basic Hebbian update rule became a generalization of Griniasty et al. (1993), which associates temporally proximal input patterns into basins of attraction. The system acquired representations of faces that were largely independent of pose.
Influence of dendritic structure on firing pattern in model neocortical neurons

ectoderm, can be divided into two distinct domains . The dorsal AER is characterized by the presence of AER-specific markers, whereas the ventral AER is distinguished by the additional expression of En-I. Loss of En-1 function appears to allow a ventral expansion of the AER. In this situation, the Wnt7a-negative region at the distal tip of the mutant limbs might be analogous to the dorsal wild-type AER, whereas the ectoderm expressing Wnt-7a in addition to AER-specific markers might represent the ventral AER. Alternatively, the distal Wnt-7anegative ectoderm might demarcate the entire functional domain of the AER, but several pieces of evidence suggest that this is not the case. Both molphological criteria and gene-expression data suggest that the AER extends beyond the ventral boundary of the Wnt-7a-negative domain. Furthermore, preliminary studies suggest a parallel proximoventral expansion of progress zone markers (data not shown). Finally, limb structures that normally develop only distally were duplicated proximoventrally in En-I mutant mice, a phenotype consistent with a functional expansion of the AER. Thus our data indicate that En-1 is required for delineating the ventral AER boundary and for restricting expression of signalling molecules, such as Fgf-8 and Bmp-2, to the distalmost ectoderm, a function reminiscent of engrailed's role in compartment border formation in D r o s ~ p h i l a ~ ~ ~ ~ .
Optimizing Cortical Mappings

"Topographic" mappings occur frequently in the brain. A popular approach to understanding the structure of such mappings is to map points representing input features in a space of a few dimensions to points in a 2 dimensional space using some self-organizing algorithm. We argue that a more general approach may be useful, where similarities between features are not constrained to be geometric distances, and the objective function for topographic matching is chosen explicitly rather than being specified implicitly by the self-organizing algorithm. We investigate analytically an example of this more general approach applied to the structure of interdigitated mappings, such as the pattern of ocular dominance columns in primary visual cortex.
Tempering Backpropagation Networks: Not All Weights are Created Equal

Backpropagation learning algorithms typically collapse the network's structure into a single vector of weight parameters to be optimized. We suggest that their performance may be improved by utilizing the structural information instead of discarding it, and introduce a framework for "tempering" each weight accordingly. 
 
In the tempering model, activation and error signals are treated as approximately independent random variables. The characteristic scale of weight changes is then matched to that of the residuals, allowing structural properties such as a node's fan-in and fan-out to affect the local learning rate and backpropagated error. The model also permits calculation of an upper bound on the global learning rate for batch updates, which in turn leads to different update rules for bias vs. non-bias weights. 
 
This approach yields hitherto unparalleled performance on the family relations benchmark, a deep multi-layer network: for both batch learning with momentum and the delta-bar-delta algorithm, convergence at the optimal learning rate is sped up by more than an order of magnitude.
Workshop in Computational Neuroscience.

The Woods Hole Workshop on Computational Neuroscience at the Marine Biological Laboratory was held for one week each August in 1992 to 1994. Each year, twenty investigators who are concerned with the computational functions of nervous systems had intense discussion on a wide range of topics in computational neuroscience, including neural mechanisms for computation, neural systems for long-term memory, neural decisions, and active perception. In addition, some members of the workshop lectured in the concurrent Computational Neuroscience Course at MBL, and students were invited to attend the workshop.