nils-herrmann/clean_scirepeval huggingface dataset:
Relation Between Retinotopical and Orientation Maps in Visual Cortex

A recent study of cat visual cortex reported abrupt changes in the positions of the receptive fields of adjacent neurons whose preferred orientations strongly differed (Das & Gilbert, 1997). Using a simple cortical model, we show that this covariation of discontinuities in maps of orientation preference and local distortions in maps of visual space reflects collective effects of the lateral cortical feedback.
Foreword Neural Pulse Coding

Neurons use action potentials to signal over long distances, as summarized in Chapter 1 by Gerstner. The all-or-none nature of the action potential means that it codes information by its presence or absence, but not by its size or shape. In this respect, an action potential can be considered a pulse. This is an important fact about how brains are built, but it is equally important as a theoretical challenge to understanding the function of the brain. How do action potentials represent sensory states? How is information contained in the firing patterns of action potentials stored and retrieved? These are old questions that have been the focus of much research, but recent advances in experimental techniques are opening new ways to test theories for how information is encoded and decoded by spiking neurons in neural systems [Rieke et al., 19971. The papers in the collection provide a window into the current state of theoretical and computational thinking based on spikes.
Face image analysis for expression measurement and detection of deceit

The Facial Action Coding System (FACS) (10) is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These methods include unsupervised learning techniques for finding basis images such as principal component analysis, independent component analysis and local feature analysis, and supervised learning techniques such as Fisher's linear discriminants. These data-driven bases are compared to Gabor wavelets, in which the basis images are predefined. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96% accuracy for classifying twelve facial actions. Once the basis images are, learned, the ICA representation takes 90% less CPU time than the Gabor representation to compute. The results provide evidence for the importance of using local image bases, high spatial frequencies, and statistical independence for classifying facial actions. Measurement of facial behavior at the level of detail of FACS provides information for detection of deceit. Applications to detection of deceit are discussed.
Seeing White: Qualia in the Context of Decoding Population Codes

When the nervous system is presented with multiple simultaneous inputs of some variable, such as wavelength or disparity, they can be combined to give rise to qualitatively new percepts that cannot be produced by any single input value. For example, there is no single wavelength that appears white. Many models of decoding neural population codes have problems handling multiple inputs, either attempting to extract a single value of the input parameter or, in some cases, registering the presence of multiple inputs without synthesizing them into something new. These examples raise a more general issue regarding the interpretation of population codes. We propose that population decoding involves not the extraction of specific values of the physical inputs, but rather a transformation from the input space to some abstract representational space that is not simply related to physical parameters. As a specific example, a four-layer network is presented that implements a transformation from wavelength to a high-level hue-saturation color space.
Convex/Schur-Convex (CSC) Log-Priors

The "folk theorem" that sparsity inducing priors should be supergaussian can be rigorously stated in the low-noise limit, assuming the validity of a particular stochastic generative model. For the assumed model it is shown that supergaussianess is necessary, but not sufficient, for sparse signal coding when a Maximum A Posterior (MAP) coding is found.
Image Representations for Facial Expression Coding

The Facial Action Coding System (FACS) (9) is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These methods include unsupervised learning techniques for finding basis images such as principal component analysis, independent component analysis and local feature analysis, and supervised learning techniques such as Fisher's linear discriminants. These data-driven bases are compared to Gabor wavelets, in which the basis images are predefined. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96% accuracy for classifying 12 facial actions. The ICA representation employs 2 orders of magnitude fewer basis images than the Gabor representation and takes 90% less CPU time to compute for new images. The results provide converging support for using local basis images, high spatial frequencies, and statistical independence for classifying facial actions.
Functionally independent components of early event-related potentials in a visual spatial attention task.

Spatial visual attention modulates the first negative-going deflection in the human averaged event-related potential (ERP) in response to visual target and non-target stimuli (the N1 complex). Here we demonstrate a decomposition of N1 into functionally independent subcomponents with functionally distinct relations to task and stimulus conditions. ERPs were collected from 20 subjects in response to visual target and non-target stimuli presented at five attended and non-attended screen locations. Independent component analysis, a new method for blind source separation, was trained simultaneously on 500 ms grand average responses from all 25 stimulus-attention conditions and decomposed the non-target N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetrical component pair (N1aR and N1aL) was evoked by the left and right visual field stimuli, respectively. Component N1aR peaked ca. 9 ms earlier than N1aL. Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. The amplitudes of these components were no reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatio-temporally overlapping bilateral component (N1b) that peaked at ca. 180 ms and was strongly enhanced by attention. Stimuli presented at unattended locations evoked a fourth component (P2a) peaking near 240 ms. A fifth component (P3f) was evoked only by targets presented in either visual field. The distinct response patterns of these components across the array of stimulus and attention conditions suggest that they reflect activity in functionally independent brain systems involved in processing attended and unattended visuospatial events.
Independent Component Analysis for Mixed Sub- Gaussian and Super- Gaussian Sources

An extension of the infomax algorithm of Bell & Sejnowski (1995) is presented that is able to separate the mixed suband super-Gaussian source distributions. The same learning rule has been derived by Giiolami & Fyfe (1997) from the negentropy perspective for projection pursuit. Using a Laplacian prior we also propose a learning rule that is especially convenient to realize in hardware. The natural gradient extension is presented fiom different perspectives and the use of preprocessing steps is proposed to further speed up the convergence. Simulation results show that the algorithm is able to separate 20 source with a variety of source distributions. On real data, Jung et al. (1997) and McKeown et al. (1997) demonstrate the successful use of the extended ICA algorithm to analyze EEG and fMRI recordings.
Self–sustained rhythmic activity in the thalamic reticular nucleus mediated by depolarizing GABAA receptor potentials

Intracellular recordings from reticular thalamic (RE) neurons in vivo revealed inhibitory postsynaptic potentials (IPSPs) between RE cells that reversed and became depolarizing at the hyperpolarized membrane potentials that occur during sleep. These excitatory IPSPs can directly trigger low–threshold spikes (LTSs). The oscillatory mechanisms underlying IPSP–triggered LTSs crowned by spike bursts were investigated in models of isolated RE networks. In a one–dimensional network model, external stimulation evoked waves of excitation propagating at a constant velocity of 25–150 cells per second. In a large–scale, two–dimensional model of the reticular nucleus, the network showed transient or self–sustained oscillations controlled by the maximum conductance of the low–threshold calcium current and the membrane potential. This model predicts that the isolated reticular nucleus could initiate sequences of spindle oscillations in thalamocortical networks in vivo.
View NEURAL CODING IN MODELS OF CORTICAL NEURON NETWORKS

In previous work we have reported that simulated networks of Hodgkin-Huxley neurons with inhibitory feedback produce output spike trains whose rate is directly proportional to the intensity of an applied input current waveform. In addition, noise is suppressed at higher and higher frequencies in the composite output spike train (combination of the spike trains from all of the neurons) as the average input drive is increased and as neurons are added. This allows the transmission of information with large bandwidths despite the slow and imprecise characteristics of individual neurons and synapses. These results have now been extended to the case with spike train inputs, synaptic dynamics of facilitation and depression, and synaptic noise from intracortical connections, as found in vivo. Input/output fidelity and spectral noise power are used as a metric to rank different network configurations. Trials were conducted first with global inhibitory feedback and then with both inhibitory and excitatory feedback incorporating fast spiking inhibitory neurons in the microcircuit. Repeating spatiotemporal patterns in the neuron ensemble were found through cross correlation and compared for similarity across different network configurations with the input spike train held constant. The information content of these patterns was estimated as a percentage of the total information in the composite output spike train. Support Contributed By: Howard Hughes Medical Institute
A Theory of Geometric Constraints on Neural Activity for Natural Three-Dimensional Movement

Although the orientation of an arm in space or the static view of an object may be represented by a population of neurons in complex ways, how these variables change with movement often follows simple linear rules, reflecting the underlying geometric constraints in the physical world. A theoretical analysis is presented for how such constraints affect the average firing rates of sensory and motor neurons during natural movements with low degrees of freedom, such as a limb movement and rigid object motion. When applied to nonrigid reaching arm movements, the linear theory accounts for cosine directional tuning with linear speed modulation, predicts a curl-free spatial distribution of preferred directions, and also explains why the instantaneous motion of the hand can be recovered from the neural population activity. For three-dimensional motion of a rigid object, the theory predicts that, to a first approximation, the response of a sensory neuron should have a preferred translational direction and a preferred rotation axis in space, both with cosine tuning functions modulated multiplicatively by speed and angular speed, respectively. Some known tuning properties of motion-sensitive neurons follow as special cases. Acceleration tuning and nonlinear speed modulation are considered in an extension of the linear theory. This general approach provides a principled method to derive mechanism-insensitive neuronal properties by exploiting the inherently low dimensionality of natural movements.
Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention

Human event-related potentials (ERPs) were recorded from 10 subjects presented with visual target and nontarget stimuli at five screen locations and responding to targets presented at one of the locations. The late positive response complexes of 25–75 ERP average waveforms from the two task conditions were simultaneously analyzed with Independent Component Analysis, a new computational method for blindly separating linearly mixed signals. Three spatially fixed, temporally independent, behaviorally relevant, and physiologically plausible components were identified without reference to peaks in single-channel waveforms. A novel frontoparietal component (P3f) began at ∼140 msec and peaked, in faster responders, at the onset of the motor command. The scalp distribution of P3f appeared consistent with brain regions activated during spatial orienting in functional imaging experiments. A longer-latency large component (P3b), positive over parietal cortex, was followed by a postmotor potential (Pmp) component that peaked 200 msec after the button press and reversed polarity near the central sulcus. A fourth component associated with a left frontocentral nontarget positivity (Pnt) was evoked primarily by target-like distractors presented in the attended location. When no distractors were presented, responses of five faster-responding subjects contained largest P3f and smallest Pmp components; when distractors were included, a Pmp component appeared only in responses of the five slower-responding subjects. Direct relationships between component amplitudes, latencies, and behavioral responses, plus similarities between component scalp distributions and regional activations reported in functional brain imaging experiments suggest that P3f, Pmp, and Pnt measure the time course and strength of functionally distinct brain processes.
Classifying Facial Actions

The Facial Action Coding System (FACS)  is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.
Running enhances neurogenesis, learning, and long-term potentiation in mice.

Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain structure that is important for memory function. Consequently, spatial learning and long-term potentiation (LTP) were tested in groups of mice housed either with a running wheel (runners) or under standard conditions (controls). Mice were injected with bromodeoxyuridine to label dividing cells and trained in the Morris water maze. LTP was studied in the dentate gyrus and area CA1 in hippocampal slices from these mice. Running improved water maze performance, increased bromodeoxyuridine-positive cell numbers, and selectively enhanced dentate gyrus LTP. Our results indicate that physical activity can regulate hippocampal neurogenesis, synaptic plasticity, and learning.
Predictive Sequence Learning in Recurrent Neocortical Circuits

Neocortical circuits are dominated by massive excitatory feedback: more than eighty percent of the synapses made by excitatory cortical neurons are onto other excitatory cortical neurons. Why is there such massive recurrent excitation in the neocortex and what is its role in cortical computation? Recent neurophysiological experiments have shown that the plasticity of recurrent neocortical synapses is governed by a temporally asymmetric Hebbian learning rule. We describe how such a rule may allow the cortex to modify recurrent synapses for prediction of input sequences. The goal is to predict the next cortical input from the recent past based on previous experience of similar input sequences. We show that a temporal difference learning rule for prediction used in conjunction with dendritic back-propagating action potentials reproduces the temporally asymmetric Hebbian plasticity observed physiologically. Biophysical simulations demonstrate that a network of cortical neurons can learn to predict moving stimuli and develop direction selective responses as a consequence of learning. The space-time response properties of model neurons are shown to be similar to those of direction selective cells in alert monkey V1.
Direction Selectivity from Predictive Sequence Learning in Recurrent Neocortical Circuits *

Neocortical circuits are dominated by massive excitatory feedback: more than eighty percent of the synapses made by excitatory cortical neurons are onto other excitatory cortical neurons. Why is there such massive recurrent excitation in the neocortex and what is its role in cortical computation? Recent neurophysiological experiments have shown that the plasticity of recurrent neocortical synapses is governed by a temporally asymmetric Hebbian learning rule. We describe how such a rule may allow the cortex to modify recurrent synapses for prediction of input sequences. The goal is to predict the next cortical input from the recent past based on previous experience of similar input sequences. We show that a temporal difference learning rule for prediction used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric Hebbian plasticity observed physiologically. Biophysical simulations demonstrate that a network of cortical neurons can learn to predict moving stimuli and develop direction selective responses as a consequence of learning. The space-time response properties of model neurons are shown to be similar to those of direction selective cells in alert monkey V 1.
Synaptic currents, neuromodulation, and kinetic models

Linking this article to brain theory are questions regarding generalizations to other discharges and synapses, long-term changes (neuromodulation, plasticity), participating membrane and molecular mechanisms, and the roles of deterministic issues and noise. Coding fidelity is useful biologically in some sensory or motor situations (although not always); its compensation poses an interesting problem. Behaviors are reproduced satisfactorily by models, mainly formal and incorporating variables recognized in living preparations that refer to presynaptic spikes, thresholds, currents, conductances, and so forth. Models ignore issues such as the neuron's regional inhomogeneity and the individual synapse's complexity. Circuit-oriented concepts are indispensable for understanding nervous systems. Synapses are operational units therein, but claiming their hegemony is similar to claiming that the atom holds the key that unravels the universe. However, the relative significance of the whole should not be exaggerated. Synaptic coding and neuronal entrainments, although not sufficient, are indispensable for comprehending neural networks and the nervous systems that they are intended to mimic. Networks with simple units reveal surprisingly complex behaviors; those with more realistic units show great promise and will add important dimensions to the body of knowledge (Aihara, Takabe, and Toyoda, 1993). In: Michael A. Arbib CEd-1 The Handbook o f Bra in Theorg & Neural Networks, Cambridge, MA: MIT Press 1995
A Model for Encoding Multiple Object Motions and Self-Motion in Area MST of Primate Visual Cortex

Many cells in the dorsal part of the medial superior temporal (MST) region of visual cortex respond selectively to specific combinations of expansion/contraction, translation, and rotation motions. Previous investigators have suggested that these cells may respond selectively to the flow fields generated by self-motion of an observer. These patterns can also be generated by the relative motion between an observer and a particular object. We explored a neurally constrained model based on the hypothesis that neurons in MST partially segment the motion fields generated by several independently moving objects. Inputs to the model were generated from sequences of ray-traced images that simulated realistic motion situations, combining observer motion, eye movements, and independent object motions. The input representation was based on the response properties of neurons in the middle temporal area (MT), which provides the primary input to area MST. After applying an unsupervised optimization technique, the units became tuned to patterns signaling coherent motion, matching many of the known properties of MST cells. The results of this model are consistent with recent studies indicating that MST cells primarily encode information concerning the relative three-dimensional motion between objects and the observer.
Removing electroencephalographic artifacts: comparison between ICA and PCA

Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of the independent component analysis (ICA) algorithm for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifact sources in EEG records with results comparing favourably to those obtained using principal component analysis (PCA).