nils-herrmann/clean_scirepeval huggingface dataset:
Learning Overcomplete Representations

In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex. Previous work has focused on finding the best representation of a signal using a fixed overcomplete basis (or dictionary). We present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data. We show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency. This can be viewed as a generalization of the technique of independent component analysis and provides a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.
EVENT-RELATED BOLD ACTIVATION OF THE OPTIC RADIATION

Independent Component Analysis (ICA) was applied to the fMRI BOLD time series obtained in event-related fMRI studies (Bruker 3T MR imager, TR=500 ms, TE=70 ms, flip angle = 90 degrees; matrix = 64x64; FOV = 250x250 mm; slice thickness = 5 mm with 2-mm gaps). Six subjects (2 males and 4 females, aged 22 +/3 yr) participated in 2 separate sessions each comprising ten 30-sec epochs. 8 Hz bursts of flickering-checkerboard visual stimulation was presented during the first 0.5 s of each epoch. The event-related fMRI time series data were first subjected to slice-timing adjustment, off-brain removal and principal subspace dimension reduction (to 100 dim.). Infomax ICA (http://www.cnl.salk.edu/~scott/ica.html) was then used to segregate the resultant fMRI BOLD signals into task-related brain responses, other brain processes, and machine/environmental noise. For each subject, ICA returned a component active in the optic radiation of the visual pathway, plus one or more components active in the V1 cortices. The time courses of these two component types had different onset latencies and waveforms. The event-related activation of the optic radiation preceded the activation of the V1 cortices. In addition, the time course of the optic radiation had larger peak-to-peak amplitudes and longer duration. ICA is sensitive method for separating event-related activations in brain processes within small regions of the brain. Supported by: NIH, Swartz Foundation, and NSC of Taiwan
Color Opponency Constitutes a Sparse Representation for the Chromatic Structure of Natural Scenes

The human visual system encodes the chromatic signals conveyed by the three types of retinal cone photoreceptors in an opponent fashion. This color opponency has been shown to constitute an efficient encoding by spectral decorrelation of the receptor signals. We analyze the spatial and chromatic structure of natural scenes by decomposing the spectral images into a set of linear basis functions such that they constitute a representation with minimal redundancy. Independent component analysis finds the basis functions that transforms the spatiochromatic data such that the outputs (activations) are statistically as independent as possible, i.e. least redundant. The resulting basis functions show strong opponency along an achromatic direction (luminance edges), along a blue-yellow direction, and along a red-blue direction. Furthermore, the resulting activations have very sparse distributions, suggesting that the use of color opponency in the human visual system achieves a highly efficient representation of colors. Our findings suggest that color opponency is a result of the properties of natural spectra and not solely a consequence of the overlapping cone spectral sensitivities.
EVENT-RELATED CHANGES IN OSCILLATORY BRAIN ACTIVITY

Decomposition of temporally overlapping subepochs from 3-s electroencephalographic (EEG) epochs time locked to the presentation of visual target stimuli in a selective attention task produced many more components with common scalp maps before stimulus delivery than after it. In particular, this was the case for components accounting for posterior alpha and central mu rhythms. Moving-window ICA decomposition thus appears to be a useful technique for evaluating changes in the independence of activity in different brain regions, i.e. event-related changes in brain dynamic modularity. However, common component clusters found by movingwindow ICA decomposition strongly resembled those found by decomposition of the whole EEG epochs, suggesting that such whole epoch decomposition reveals stable independent components of EEG signals.
A universal scaling law between gray matter and white matter of cerebral cortex.

Neocortex, a new and rapidly evolving brain structure in mammals, has a similar layered architecture in species over a wide range of brain sizes. Larger brains require longer fibers to communicate between distant cortical areas; the volume of the white matter that contains long axons increases disproportionally faster than the volume of the gray matter that contains cell bodies, dendrites, and axons for local information processing, according to a power law. The theoretical analysis presented here shows how this remarkable anatomical regularity might arise naturally as a consequence of the local uniformity of the cortex and the requirement for compact arrangement of long axonal fibers. The predicted power law with an exponent of 4/3 minus a small correction for the thickness of the cortex accurately accounts for empirical data spanning several orders of magnitude in brain sizes for various mammalian species, including human and nonhuman primates.
Comparison of current-driven and conductance-driven neocortical model neurons with Hodgkin-Huxley voltage-gated channels.

Intrinsic noise and random synaptic inputs generate a fluctuating current across neuron membranes. We determine the statistics of the output spike train of a biophysical model neuron as a function of the mean and variance of the fluctuating current, when the current is white noise, or when it derives from Poisson trains of excitatory and inhibitory postsynaptic conductances. In the first case, the firing rate increases with increasing variance of the current, whereas in the latter case it decreases. In contrast, the firing rate is independent of variance (for constant mean) in the commonly used random walk, and perfect integrate-and-fire models for spike generation. The model neuron can be in the current-dominated state, representative of neurons in the in vitro slice preparation, or in the fluctuation-dominated state, representative of in vivo neurons. We discuss the functional relevance of these states to cortical information processing.
Abstract View PROPAGATION IN THALAMOCORTICAL DENDRITES

Dendritic propagation of low threshold Ca2+ potentials (LTCPs) and fast Na+ action potentials (APs) were investigated using in vivo and in vitro computational models of 3-D reconstructed neurons from the somatosensory and visual thalamus. Hot-spot, monotonic, bimodal, proximal, uniform channel densities were considered. For each, localized burst inhibition of various intensities elicited a range of LTCP amplitudes. In all cases, LTCPs were uniform and synchronous events: it was not possible to elicit an LTCP that was either localized or spatiotemporally structured on a relevant scale even when individual trees were differentially modulated. We suggest that this "whole-cell" nature of LTCPs provides an intracellular synchronization signal. In trials using uniform somato-dendritic Na+/K+ channel densities, APs peaked uniformly and nearly synchronously throughout the arbor (initiating tens of usec earlier in some dendrites that had received inhibition). However, the dendritic K+ channels rapidly repolarized their membrane, which truncated calcium gating and hindered further Na+ spiking. In trials using a soma-only Na+/K+ distribution, APs passively backpropagated into the arbor at a rate of ~250um/ms, depolarizing all branches by at least 35mV with a spike shaped timecourse. The absence of dendritic Na+/K+ channels allowed the arbor to continue on an LTCP timecourse, which left the arbor sufficiently depolarized to generate additional Na+ spikes in a burst. We conclude that 1) dendritic Na+/K+ channels are not necessary to produce strong depolarizing signals throughout the dendritic arbor, 2) their absence in the dendrites would enhance bursting, and 3) only moderate dendritic densities could provide virtually instantaneous AP feedback to synaptic mechanisms. Supported by: HHMI
Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory–inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero), firing is caused by the fluctuations only. (3) In the balanced case, firing is irregular. These theoretical predictions were in excellent agreement with simulations of an integrate-and-fire neuron that included multiple conductances and received hundreds of synaptic inputs. The results show that, in the balanced regime, weak correlations caused by signals shared among inputs may have a multiplicative effect on the input-output rate curve of a postsynaptic neuron, i.e. they may regulate its gain; in the unbalanced regime, correlations may increase firing probability mainly around threshold, when output rate is low; and in all cases correlations are expected to increase the variability of the output spike train.
CEREBELLAR GLOMERULI : DOES LIMITED EXTRACELLULAR CALCIUM DIRECT A NEW KIND OF PLASTICITY ?

A class of synaptic learning models, in which a sum of postsynaptic activity from many neurons drives plasticity, has generally been considered biologically infeasible. After all, postsynaptic cell bodies may be far apart, and there are no backward signals known to sum activity in a terminal-specific manner. However, some specialized synapses, known as glomeruli, become ensheathed by glial cells, and we suggest that these structures may allow for just such a postsynaptic summation. The ensheathment may force enclosed, neighboring dendrites to share a limited resource: extracellular calcium (ECa). We propose the theory that the ECa concentration in glomeruli may encode the level of spike activity in postsynaptic cells. We investigate here cerebellar glomeruli, where dendrites from granule cells swirl around a mossy fiber terminal, and the ensemble is tightly ensheathed in an astrocyte. Computer analyses of 3D simulated glomeruli, with realistic channel kinetics and Monte Carlo modeling of calcium diffusion using MCell, indicate the range of conditions under which ECa will be proportional to the sum of granule cell activity. We also show how these ECa changes can be interpreted from an information-processing point of view, generating a novel learning rule for control of plasticity at the mossy fiber/granule cell synapse. This learning rule approaches a sparsely distributed and statistically independent coding in the parallel fibers. Although traditional neural models emphasize only neurotransmitters and connectivity, these results highlight the need to quantitatively address the 3D context in which axons and dendrites are found. Supported by: Sloan-Swartz, McDonnell-Pew, NIH, NSF, HHMI
Abstract View HIGH SPATIAL AND TEMPORAL RESOLUTION ESTIMATES OF CALCIUM DYNAMICS IN DENDRITIC SPINES USING MCELL SIMULATIONS

Calcium plays a critical signaling role in many biological systems. For example, in the postsynaptic neuron, increases in Ca2+ concentration are believed to be essential for the induction of long-term potentiation and long-term depression, changes in synaptic efficacy thought to be the cellular mechanisms underlying learning and memory. Measurements of intracellular Ca2+ have typically relied on the use of fluorescent imaging where the sensors used buffer the Ca2+, obscuring concentration measurements, and have limited spatial and temporal resolution. In postsynaptic spines, the spatial localization of Ca2+-dependent effectors such as calmodulin, CaMKII and calcineurin make the measurement of Ca2+ concentration in the entire spine less than ideal. Using MCell, a Monte Carlo simulator, to monitor both the influx of Ca2+ into a spine and dendrite and its reactions with intracellular molecules, we have reproduced fluorescent transients measured in neocortical and hippocampal neurons. This has allowed determination of the unperturbed Ca2+ dynamics and suggests kinetics and concentrations of endogenous Ca2+ binding proteins. We have measured Ca2+ concentrations in microdomains throughout the spine showing highly heterogenous concentrations of free intracellular Ca2+. Furthermore, we show the spatial and temporal activation of different Ca2+-dependent effectors resulting from asynchronous pairing of preand postsynaptic action potentials. To complement experimental studies, we show how computer simulations can offer an alternate and complementary method of assaying intracellular Ca2+ dynamics and reactions. Supported by: NSF, HHMI, NIH & HFSP.
Properties of independent components of self-motion optical flow

In this paper we describe the properties of independent components of optical flow of moving objects. Video sequences of objects seen by an observer moving at various angles, directions and distances are used to produce optical flow maps. These maps are then, recessed using independent component analysis, which yields filters that resemble the receptive fields of dorsal medial superior temporal cells of the primate brain. Contraction, expansion, rotation and translation receptive fields have been identified. Our results support Barlow's sensory coding theory and are in-line with other work on independent components of image and video intensities.
Synaptic plasticity in morphologically identified CA1 stratum radiatum interneurons and giant projection cells

Long‐term potentiation (LTP) of synaptic efficacy was examined in interneurons and giant cells in the stratum radiatum region of the hippocampal CA1 subfield. Cells were visually selected using differential interference contrast (DIC) optics and filled with biocytin while being recorded using whole‐cell patch‐clamp techniques. Electrophysiological criteria, including spike height, width, and degree of spike adaptation shown to sustained depolarization, proved inadequate for differentiating interneurons from giant cells. We found that cells in the stratum radiatum, however, could be reliably differentiated using DIC optics or following intracellular staining. The response of the two cell types to tetanic stimulation further dissociated them. Long‐term potentiation, dependent on the activation of NMDAr (N‐methyl‐D‐aspartate receptors), could reliably be induced in interneurons with stimuli administered at 200 Hz, but not 100 Hz. Giant cells, in contrast, exhibited NMDA receptor‐dependent LTP in response to 100‐Hz stimuli, but not the 200‐Hz stimuli. LTP induction in interneurons also appeared temperature‐dependent, being much more robust at 34°C than at room temperature. The LTP in both cell types required postsynaptic calcium influx, and was not due to the passive propagation of LTP induction in neighboring pyramidal cells. These results suggest that different cell types within the hippocampal formation may preferentially alter synaptic connectivity in a frequency‐specific manner. Hippocampus 2000;10:673–683.
MOVING-WINDOW ICA DECOMPOSITION OF EEG DATA REVEALS EVENT-RELATED CHANGES IN OSCILLATORY BRAIN ACTIVITY

Decomposition of temporally overlapping sub- epochs from 3-s electroencephalographic (EEG) epochs time locked to the presentation of visual target stimuli in a selective attention task produced many more components with common scalp maps before stimulus delivery than after it. In particular, this was the case for components accounting for posterior alpha and central mu rhythms. Moving-window ICA decomposition thus appears to be a useful technique for evaluating changes in the independence of activity in different brain regions, i.e. event-related changes in brain dynamic modularity. However, common component clusters found by moving- window ICA decomposition strongly resembled those found by decomposition of the whole EEG epochs, suggesting that such whole epoch decomposition reveals stable independent components of EEG signals.
Independent Component Analysis of Simulated ERP Data

A recently-derived algorithm for performing Independent Component Analysis (ICA) (Bell & Sejnowski, 1995) based on information maximization is a new information-theoretic approach to the problem of separating multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data into temporally independent and spatially stationary sources (Makeig et al., 1996). In a previous report, we have shown that the algorithm can separate simulated EEG source waveforms (independent simulated brain source activities mixed linearly at the scalp sensors), even in the presence of multiple low-level model brain and sensor noise sources (Ghahremani et al., 1996). Here, we demonstrate the ability of the ICA algorithm to decompose brief event-related potential (ERP) data sets into temporally independent components (Makeig et al., 1997) by applying it to simulated ERP-length EEG data synthesized from 3-sec (600-point) electrocorticographic (ECoG) epochs recorded from the cortical surface of a human undergoing pre-surgical evaluation (Bullock et al., 1995a, 1995b). Six asynchronous single-channel ECoG data epochs were projected through single-and multiple-dipole model sources in a three-shell spherical head model (Dale & Sereno, 1993) to six simulated scalp sensors to create simulated EEG data. In two sets of simulation experiments, we altered relative source strengths, added multiple low-level sources (synthesized from ECoG data and uniform-or Gaussian-distributed noise), and permuted the simulated dipole source locations and orientations. The algorithm reliably separated the activities of the relatively strong sources, regardless of source location, dipole orientation, and low-level source distributions. Recovery of the original component waveforms was much better using ICA than using PCA without or without Varimax or Promax rotation. Thus, the ICA algorithm should identify relatively strong, temporally independent and spatially overlapping ERP components arising from multiple brain and/or non-brain sources, regardless of their spatial distributions. This shows that the ICA algorithm can decompose ERPs generated by uncorrelated sources. A third ERP simulation tested how the algorithm treated a simulated ERP epoch constructed using model ERP generators whose activations were partially correlated. In this case, the algorithm parsed the simulated ERP waveforms into a sum of temporally independent and spatially stationary components reflecting the changing topography of correlated source activity in the simulated ERP data. Each of the affected components sums activity from one or more concurrently-active brain generators. This suggests the ICA algorithm may also be useful for identifying event-related changes in the correlation structure of either spontaneous or event-related EEG data. Paradoxically, adding four simulated " no response " epochs to the training data minimized …
DYNAMIC CLAMP OF CORTICAL NEURONS IN VITRO SIMULATES IN VIVO ACTIVITY PATTERNS

Intracellular recordings from neocortical neurons during active states in vivo have depolarized membrane potentials (Vm), highly fluctuating background activity and a high variability of spontaneous discharge. To investigate to what extent background activity can account for these properties, we used a dynamic clamp with in vitro recordings and computational models to recreate the activity pattern seen in cortical neurons in vivo. The biophysical characteristics of synaptic background activity were represented by a point-conductance model using two stochastic variables. This model produced a "fluctuating conductance" with the same spectral characteristics as the activity resulting from thousands of excitatory and inhibitory synapses releasing randomly in a correlated manner. The output conductance from this single compartment model was injected in pyramidal cells and interneurons in rat prefrontal cortical slices. The neurons produced the characteristics typical of neurons intracellularly recorded in vivo: a reduced input resistance, a depolarized Vm (around -60 mV), large-amplitude Vm fluctuations and a high variability of spontaneous firing (CV around unity). We study the variation of these quantities when the amount of synchrony and the relative balance between inhibition and excitation were manipulated in the model. These combinations of experiments and models suggest that the electrophysiological characteristics of neocortical neurons in vivo can be recreated in vitro by using a point-conductance representation of synaptic background activity. Supported by: HHMI, MRC, NIH
Gap junction effects on precision and frequency of a model pacemaker network.

We investigated the precision of spike timing in a model of gap junction-coupled oscillatory neurons. The model incorporated the known physiology, morphology, and connectivity of the weakly electric fish's high-frequency and extremely precise pacemaker nucleus (Pn). Two neuron classes, pacemaker and relay cells, were each modeled with two compartments containing Hodgkin-Huxley sodium and potassium currents. Isolated pacemaker cells fired periodically, due to a constant current injection; relay cells were silent but slightly depolarized at rest. When coupled by gap junctions to other neurons, a model neuron, like its biological correlate, spiked at frequencies and amplitudes that were largely independent of current injections. The phase distribution in the network was labile to intracellular current injections and to gap junction conductance changes. The model predicts a biologically plausible gap junction conductance of 4-5 nS (200-250 MOmega). This results in a coupling coefficient of approximately 0.02, as observed in vitro. Network parameters were varied to test which could improve the temporal precision of oscillations. Increased gap junction conductances and larger numbers of cells (holding total junctional conductance per cell constant) both substantially reduced the coefficient of variation (CV = standard deviation/mean) of relay cell spike times by 74-85% and more, and did so with lower gap junction conductance when cells were contacted axonically compared with somatically. Pacemaker cell CV was only reduced when the probability of contact was increased, and then only moderately: a fivefold increase in the probability of contact reduced CV by 35%. We conclude that gap junctions facilitate synchronization, can reduce CV, are most effective between axons, and that pacemaker cells must have low intrinsic CV to account for the low CV of cells in the biological network.