nils-herrmann/clean_scirepeval huggingface dataset:
COMPUTER SIMULATION OF SYNAPTIC ULTRASTRUCTURE AND MICROPHYSIOLOGY IN THE CHICK CILIARY GANGLION

Using MCell, a general Monte Carlo simulator of subcellular signaling (http://mcell.cnl.salk.edu), it is now possible to simulate the process of synaptic transmission on actual 3D microanatomy derived from serial electron microscopic tomography. Here we present the methods used to construct a realistic MCell model of a chick ciliary ganglion (CG) synapse. A 96 m3 region of synaptic ultrastructure was obtained by segmentation of active zone locations, and preand postsynaptic membranes within a tomographic volume with better than 10 nm resolution. Published and estimated values for the numbers and distributions of nicotinic acetylcholine (ACh) receptors and ACh-esterase enzymes, together with reaction mechanisms and rate constants, were mapped onto membranes. Via the MCell Model Description Language, these data were combined with the 3D structure and estimates of the number of ACh molecules in a quantum. The resulting MCell model can be used to run a simulation of miniature postsynaptic current generation at the CG in about 90 seconds on a 1 GHz Pentium III workstation. The model allows parameter sweep studies evaluating the behavior of this in-silico CG system and thereby estimate difficult to determine parameters such as the rate constant for agonist binding and the number of competent receptors accessible. This permits results from laboratory and computational experiments to be compared quantitatively. Supported by: NSF IBN-9985964 (TMB, TJS), NIH RR04050 & DC03192 (MHE), NIH NS12601 (DKB, JSC), NSF AC10086092 (TJS, MHE)
Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons.

Dopamine acts mainly through the D1/D5 receptor in the prefrontal cortex (PFC) to modulate neural activity and behaviors associated with working memory. To understand the mechanism of this effect, we examined the modulation of excitatory synaptic inputs onto layer V PFC pyramidal neurons by D1/D5 receptor stimulation. D1/D5 agonists increased the size of N-methyl-d-aspartate (NMDA) component of excitatory postsynaptic currents (EPSCs) through a postsynaptic mechanism. In contrast, D1/D5 agonists caused a slight reduction in the size of the non-NMDA component of EPSCs through a small decrease in release probability. With 20 Hz synaptic trains, we found that the D1/D5 agonists increased depolarization of summating the NMDA component of excitatory postsynaptic potential (EPSP). By increasing the NMDA component of EPSCs, yet slightly reducing release, D1/D5 receptor activation selectively enhanced sustained synaptic inputs and equalized the sizes of EPSPs in a 20-Hz train.
AS A MECHANISM FOR ATTENTIONAL MODULATION

When attention is shifted to the receptive field of a neuron, the firing of the neuron may become more synchronized with other similar units, as observed in somatosensory cortex [Steinmetz et al,  (2000)], or with the local field potential at gamma frequencies, as reported for extrastriate cortex [Fries et al, Science 291, 1560 (2001)]. Using simulations, we investigated how such changes in synchrony may affect the response properties of a model downstream neuron. The synaptic drive consisted of periodic excitatory and inhibitory components, each characterized by the input rate of pulses and by a temporal jitter in their arrival times, with smaller jitter meaning higher synchrony. We found two important effects. First, when the excitatory inputs were represented by a constant depolarizing current, the output firing rate decreased sharply with increasing inhibitory jitter. In this case the synchrony of the inhibitory drive acts as a gate: for large jitter spikes are not transmitted whereas for small jitter spikes are transmitted. Second, when both excitation and inhibition fluctuated periodically but were out of phase, the gain of the curve of firing rate versus injected current changed as a function of the phase difference. These predictions were confirmed in vitro by injecting currents into rat cortical neurons using a dynamic clamp. We propose that attention may modulate the response of a circuit and change its sensitivity to stimuli by shifting the synchrony of local inhibitory neurons. Supported by: CIRCS, Sloan-Swartz Ctr for Theoretical Neurobiology, HHMI
Abstract View EXACT SOLUTIONS FOR THE NON-LEAKY INTEGRATE-AND-FIRE MODEL NEURON DRIVEN BY CORRELATED STOCHASTIC INPUTS

One way to understand the integration properties of a neuron driven by many synaptic inputs is to consider its total input as a stochastic variable with given statistical properties, and calculate the statistics of the response. Commonly, one assumes that the total input has a Gaussian distribution and that samples taken at different times are uncorrelated. Actual input currents, however, have a correlation time Tc, due to factors like synaptic time constants, synchrony in the input spikes or concerted oscillatory activity, which may change in a stimulusor attention-dependent way, as observed in the somatosensory (Steinmetz et al., 2000) and visual (Fries et al., 2001) systems. To understand how the correlation time affects neural responses, we studied the firing times of an integrate-and-fire model neuron driven by binary inputs. The mean firing rate R and the coefficient of variation CV (a measure of spike train variability) were calculated analytically as functions of the mean, variance and correlation time of the inputs. We found that an increase in Tc typically increases both R and CV, but these effects depend differentially on the relative values of the input mean and variance: when the variance is large relative to the mean, R is highly sensitive to changes in Tc but CV is not; when the variance is relatively small the sensitivities are reversed. The behaviors obtained as Tc becomes large are also different: R saturates whereas CV diverges. The results agreed with simulations, and suggest that the spike train variability and firing rate of neurons may be strongly modulated through changes in the temporal correlations of the driving inputs.  The Howard Hughes Medical Institute
Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons.

Pyramidal cells and interneurons in rat prefrontal cortical slices exhibit subthreshold oscillations when depolarized by constant current injection. For both types of neurons, the frequencies of these oscillations for current injection just below spike threshold were 2--10 Hz. Above spike threshold, however, the subthreshold oscillations in pyramidal cells remained low, but the frequency of oscillations in interneurons increased up to 50 Hz. To explore the interaction between these intrinsic oscillations and external inputs, the reliability of spiking in these cortical neurons was studied with sinusoidal current injection over a range of frequencies above and below the intrinsic frequency. Cortical neurons produced 1:1 phase locking for a limited range of driving frequencies for fixed amplitude. For low-input amplitude, 1:1 phase locking was obtained in the 5- to 10-Hz range. For higher-input amplitudes, pyramidal cells phase-locked in the 5- to 20-Hz range, whereas interneurons phase-locked in the 5- to 50-Hz range. For the amplitudes studied here, spike time reliability was always highest during 1:1 phase-locking, between 5 and 20 Hz for pyramidal cells and between 5 and 50 Hz for interneurons. The observed differences in the intrinsic frequency preference between pyramidal cells and interneurons have implications for rhythmogenesis and information transmission between populations of cortical neurons.
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.
Book Review: Gain Modulation in the Central Nervous System: Where Behavior, Neurophysiology, and Computation Meet

Gain modulation is a nonlinear way in which neurons combine information from two (or more) sources, which may be of sensory, motor, or cognitive origin. Gain modulation is revealed when one input, the modulatory one, affects the gain or the sensitivity of the neuron to the other input, without modifying its selectivity or receptive field properties. This type of modulatory interaction is important for two reasons. First, it is an extremely widespread integration mechanism; it is found in a plethora of cortical areas and in some subcortical structures as well, and as a consequence it seems to play an important role in a striking variety of functions, including eye and limb movements, navigation, spatial perception, attentional processing, and object recognition. Second, there is a theoretical foundation indicating that gain-modulated neurons may serve as a basis for a general class of computations, namely, coordinate transformations and the generation of invariant responses, which indeed may underlie all the brain functions just mentioned. This article describes the relationships between computational models, the physiological properties of a variety of gain-modulated neurons, and some of the behavioral consequences of damage to gain-modulated neural representations.
Learning to evaluate Go positions via temporal difference methods

The game of Go has a high branching factor that defeats the tree search approach used in computer chess, and long-range spatiotemporal interactions that make position evaluation extremely difficult. Development of conventional Go programs is hampered by their knowledge-intensive nature. We demonstrate a viable alternative by training neural networks to evaluate Go positions via temporal difference (TD) learning. Our approach is based on neural network architectures that reflect the spatial organization of both input and reinforcement signals on the Go board, and training protocols that provide exposure to competent (though unlabelled) play. These techniques yield far better performance than undifferentiated networks trained by self-play alone. A network with less than 500 weights learned within 3000 games of 9x9 Go a position evaluation function superior to that of a commercial Go program.
Abstract View SPATIAL AND CHROMATIC FILTERS DERIVED FROM AN INFORMATION-THEORETIC ANALYSIS OF NATURAL SCENES

Neurons in the early stages of visual processing should represent the statistical properties of natural scenes efficiently. In previous studies, independent component analysis (ICA) was applied to images based on monochromatic or trichromatic cone arrays (Bell & Sejnowski, 1997; Lee et al., 1999). Here, we compare the results of this approach to physiological data based on the experimental conditions in Hanazawa et al., 2000. The resulting whitening filters could be categorized into the three classes of type I neurons found in the LGN (two classes for r-g, a third for y-b). In particular, both luminance and red-green information were coded together, as found in pLGN cells. Furthermore, the ICA filters can be categorized into simple-cell and double-opponent (DO)-cell types. The simple-cell type showed a spatial organization similar to the filters that emerge when ICA is applied to monochromatic natural images and their chromatic selectivity was identical to the luminosity function. The DO-cell type had spatially-oriented chromatically-opponent subfields, and had two subtypes based on their color selectivity (red-green and yellow-blue opponency). Characteristics of the simple-cell type and the DO-cell type suggest that they are responsible for form and color information, respectively. This was confirmed by reconstructing an image with single cell types. The separation of form and color channels is a statistically efficient way to encode natural scenes that arose during evolution, but these properties could also depend on visual experience during development in a local environment.
Automatic Analysis of Spontaneous Facial Behavior: A Final Project Report

The Facial Action Coding System (FACS) is the leading standard for measuring facial expressions in the behavioral sciences (Ekman & Friesen, 1978). FACS coding is presently performed manually by human experts, it is slow, and requires extensive training. Automating FACS coding could have revolutionary effects in our understanding of human facial expression and on the development of computer systems that understand facial expressions. Two teams, one at University of California San Diego and the Salk Institute, and another at University of Pittsburgh and Carnegie Mellon University, were challenged to develop prototype systems for automatic recognition of spontaneous facial expressions. Working with spontaneous expressions required solving technical and theoretical challenges which had not been previously addressed in the field. This document describes the system developed by the UCSD team. The approach employs 3-D pose estimation and warping techniques to reduce image variability due to general changes in pose. Machine learning techniques are then applied directly on the warped images or on biologically inspired representations of these images. No efforts are made to detect contours or other hand-crafted image features. This system employed general purpose learning mechanisms that can be applied to recognition of any action unit. The approach is parsimonious and does not require defining a different set of feature parameters or image operations for each facial action. The system was tested on a set of eyelid and eyebrow movements and successfully identified these movements in novel subjects. We showed that 3D tracking and warping followed by machine learning techniques directly applied to the warped images, is a viable and promising technology for automatic facial action recognition. One exciting aspect of the approach presented here is that information about movement dynamics emerged out of filters which were derived from the statistics of images. We believe all the pieces of the puzzle are ready for the development of automated systems that recognize spontaneous facial actions at the level of detail required by FACS. The main factor impeding development in this field is the lack of sufficiently large databases for training a greater variety of action units, and which may become a standard for comparison between different approaches. Based on our experience in this project we estimate that a database of 500 subjects, with 1 minute of rich facial behavior per subject, would be sufficient for dramatic improvements in the field.
DOPAMINE FACILITATES THE SUSTAINED FIRING OF RAT LAYER V PREFRONTAL PYRAMIDAL CELLS IN VITRO

In delayed match-to-sample tasks in monkeys, recordings from some neurons in prefrontal cortex (PFC) in vivo show sustained increases in the firing rate during the delay period between the stimulus onset and the response. The performance on the task is impaired when the dopamine levels in the PFC are either too low or too high, suggesting that dopaminergic modulation is involved in encoding the stimulus. We examined the effects of dopamine on rat PFC neurons recorded in vitro coupled by a dynamic clamp to a simulated population of impinging cortical synapses. Conductances were injected into the PFC cell to simulate particular mixtures of AMPA, NMDA and GABA_A inputs triggered in response to the cell own firing, in a feedback loop with the simulation. The amplitudes, time spread, durations and time delays of these conductance injections were adjusted to mimic realistic cortical inputs. Short (100 ms) somatic depolarization of the cell yielded little or no sustained firing in the hybrid network. When 40 M dopamine was added to the bath, and the simulated NMDA currents were increased by 30% in accordance with in vitro data, the same depolarization elicited increases in firing rates that lasted several seconds, mimicking the activity observed in vivo. The probability of the establishment and the average duration of this sustained increase in firing rate were studied as a function of dopamine concentration. These results are consistent with the hypothesis that delay period activity in PFC neurons is sustained by network activity and that dopamine modulation may be central in supporting sustained activity. Supported by the Howard Hughes Medical Institute.
Computational model of carbachol‐induced delta, theta, and gamma oscillations in the hippocampus

Field potential recordings from the rat hippocampus in vivo contain distinct frequency bands of activity, including δ (0.5–2 Hz), θ (4–12 Hz), and γ (30–80 Hz), that are correlated with the behavioral state of the animal. The cholinergic agonist carbachol (CCH) induces oscillations in the δ (CCH‐δ), θ (CCH‐θ), and γ (CCH‐γ) frequency ranges in the hippocampal slice preparation, eliciting asynchronous CCH‐θ, synchronous CCH‐δ, and synchronous CCH‐θ with increasing CCH concentration (Fellous and Sejnowski, Hippocampus 2000;10:187–197).
Nonlinear blind source separation using kernel feature spaces

In this work we propose a kernel-based blind source separation (BSS) algorithm that can perform nonlinear BSS for general invertible nonlinearities. For our kTDSEP algorithm we have to go through four steps: (i) adapting to the intrinsic dimension of the data mapped to feature space F , (ii) finding an orthonormal basis of this submanifold, (iii) mapping the data into the subspace of F spanned by this orthonormal basis, and (iv) applying temporal decorrelation BSS (TDSEP) to the mapped data. After demixing we get a number of irrelevant components and the original sources. To find out which ones are the components of interest, we propose a criterion that allows to identify the original sources. The excellent performance of kTDSEP is demonstrated in experiments on nonlinearly mixed speech data.
Simulating a lesion in a basis function model of spatial representations: comparison with hemineglect.

The basis function theory of spatial representations explains how neurons in the parietal cortex can perform nonlinear transformations from sensory to motor coordinates. The authors present computer simulations showing that unilateral parietal lesions leading to a neuronal gradient in basis function maps can account for the behavior of patients with hemineglect, including (a) neglect in line cancellation and line bisection experiments; (b) neglect in multiple frames of reference simultaneously; (c) relative neglect, a form of what is sometime called object-centered neglect; and (d) neglect without optic ataxia. Contralateral neglect arises in the model because the lesion produces an imbalance in the salience of stimuli that is modulated by the orientation of the body in space. These results strongly support the basis function theory for spatial representations in humans and provide a computational model of hemineglect at the single-cell level.
Analysis and visualization of single‐trial event‐related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single‐trial multichannel EEG data from event‐related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra‐brain sources. Both the data and their decomposition are displayed using a new visualization tool, the “ERP image,” that can clearly characterize single‐trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye‐movement artifacts. Results show that ICA can separate artifactual, stimulus‐locked, response‐locked, and non‐event‐related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single‐trial EEG records, (2) identification and segregation of stimulus‐ and response‐locked EEG components, (3) examination of differences in single‐trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between‐subject component stability of ICA decomposition of single‐trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event‐related potentials, and event‐modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event‐related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data.  2001.
Precision of pulse-coupled networks of integrate-and-fire neurons

Some sensory tasks in the nervous system require highly precise spike trains to be generated in the presence of intrinsic neuronal noise. Collective enhancement of precision (CEP) can occur when spike trains of many neurons are pooled together into a more precise population discharge. We study CEP in a network of N model neurons connected by recurrent excitation. Each neuron is driven by a periodic inhibitory spike train with independent jitter in the spike arrival time. The network discharge is characterized by [iopmath latex="$sigma_W$"] W [/iopmath], the dispersion in the spike times within one cycle, and [iopmath latex="$sigma_B$"] B [/iopmath], the jitter in the network-averaged spike time between cycles. In an uncoupled network [iopmath latex="$sigma_Bsim1/sqrt{N}$"] B ~ 1/(N)1/2 [/iopmath] and [iopmath latex="$sigma_W$"] W [/iopmath] is independent of N. In a strongly coupled network [iopmath latex="$sigma_Bsim 1/sqrt{log N}$"] B ~ 1/(log N)1/2 [/iopmath] and [iopmath latex="$sigma_W$"] W [/iopmath] is close to zero. At intermediate coupling strengths, [iopmath latex="$sigma_W$"] W [/iopmath] is reduced, while [iopmath latex="$sigma_B$"] B [/iopmath] remains close to its uncoupled value. The population discharge then has optimal biophysical properties compared with the uncoupled network.
AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS

While it is now generally accepted that independent component analysis is a good tool for isolating both artifacts and cognitive related activations in EEG data, there is still little consensus about criteria for automatic rejection of artifactual components and single trials. Here we developed a graphical software to semi-automatically assist experimenter in rejecting independent components and noisy single data trials based on their statistical properties. We used kurtosis to detect peaky activity distributions that are characteristic of some types of artifact and entropy to detect unusual activity patterns. EEG-LAB, a user-friendly graphic interface running under Matlab, allows the user to tune and calibrate the rejection criteria, to accept or override the suggested components and trials labeled for rejection, and to compare the results with other rejection methods.