nils-herrmann/clean_scirepeval huggingface dataset:
COMPLEX SPECTRAL-DOMAIN INDEPENDENT COMPONENT ANALYSIS OF ELECTROENCEPHALOGRAPHIC DATA

Independent component analysis (ICA) has proved to be a highly useful tool for modeling brain data and in particular electroencephalographic (EEG) data. In this paper, a new method is presented that may better capture the underlying source dynamics than ICA algorithms hereto employed for brain signal analysis. We suppose that a brief, impulse-like activation of an effective signal source elicits a short sequence of spatio-temporal activations in the measured signals. This leads to a model of convolutive signal superposition, in contrast to the instantaneous mixing model commonly assumed for independent component analysis of brain signals. In the spectral-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded mixture of complex signals into independent components by a complex version of the infomax ICA algorithm. Some results from a visual spatial selective attention experiment illustrate the differences between real time-domain ICA and complex spectral-domain ICA, and highlight properties of the obtained complex independent components.
Cortical hyperpolarization-activated depolarizing current takes part in the generation of focal paroxysmal activities

During paroxysmal neocortical oscillations, sudden depolarization leading to the next cycle occurs when the majority of cortical neurons are hyperpolarized. Both the Ca2+-dependent K+ currents (IK(Ca)) and disfacilitation play critical roles in the generation of hyperpolarizing potentials. In vivo experiments and computational models are used here to investigate whether the hyperpolarization-activated depolarizing current (Ih) in cortical neurons also contributes to the generation of paroxysmal onsets. Hyperpolarizing current pulses revealed a depolarizing sag in ≈20% of cortical neurons. Intracellular recordings from glial cells indirectly indicated an increase in extracellular potassium concentration ([K+]o) during paroxysmal activities, leading to a positive shift in the reversal potential of K+-mediated currents, including Ih. In the paroxysmal neocortex, ≈20% of neurons show repolarizing potentials originating from hyperpolarizations associated with depth-electroencephalogram positive waves of spike-wave complexes. The onset of these repolarizing potentials corresponds to maximal [K+]o as estimated from dual simultaneous impalements from neurons and glial cells. Computational models showed how, after the increased [K+]o, the interplay between Ih, IK(Ca), and a persistent Na+ current, INa(P), could organize paroxysmal oscillations at a frequency of 2–3 Hz.
Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components

We apply a variational method to automatically determine the number of mixtures of independent components in high-dimensional datasets, in which the sources may be nonsymmetrically distributed. The data are modeled by clusters where each cluster is described as a linear mixture of independent factors. The variational Bayesian method yields an accurate density model for the observed data without overfitting problems. This allows the dimensionality of the data to be identified for each cluster. The new method was successfully applied to a difficult real-world medical dataset for diagnosing glaucoma.
Synaptic Interactions

Modeling synaptic interactions in network models poses a particular challenge. Not only should such models capture the important physiological properties of synaptic ifiteractions, but they must do so in a computationally efficient manner in order to facilitate simulations of large networks. This article reviews several types of models that address these goals. Synaptic currents are mediated by ion channels activated by neurotransmitter released from presynaptic terminals. Kinetic models are expressive enough to describe the behavior of ion channels underlying synaptic currents. Although full representation of the molecular details of the synapse generally requires highly complex kinetic models, we focus here on simpler kinetic models that are more computationally efficient. We show that these models can capture the time course and dynamics of several types of synaptic responses, allowing them to be useful tools for modeling synaptic interactions in large networks.
Face Modeling by Information Maximization 1

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pair-wise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of maximum information transfer through sigmoidal neurons . ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on principal component analysis for recognizing faces across days and changes in expression. A computational neuroscience perspective on face modeling and information maximization is discussed.
Single-Trial Variability in Event-Related BOLD Signals

Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. Here, we use Infomax independent component analysis (ICA) to detect and visualize variations in single-trial HRs in event-related fMRI data. Six subjects participated in four fMRI sessions each in which ten bursts of 8-Hz flickering-checkerboard stimulation were presented for 0.5-s (short) or 3-s (long) durations at 30-s intervals. Five axial slices were acquired by a Bruker 3-T magnetic resonance imager at interscan intervals of 500 ms (TR). ICA decomposition of the resulting blood oxygenation level-dependent (BOLD) data from each session produced an independent component active in primary visual cortex (V1) and, in several sessions, another active in medial temporal cortex (MT/V5). Visualizing sets of BOLD response epochs with novel BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas. Contrary to expectation, in four of the six subjects the V1 component HR contained two positive peaks in response to short-stimulus bursts, while components with nearly identical regions of activity in long-stimulus sessions from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image visualization can reveal dramatic and unforeseen HR variations not apparent to researchers analyzing their data with event-related response averaging and fixed HR templates.
Short‐ and medium‐term plasticity associated with augmenting responses in cortical slabs and spindles in intact cortex of cats in vivo

Plastic changes in the synaptic responsiveness of neocortical neurones, which occur after rhythmic stimuli within the frequency range of sleep spindles (10 Hz), were investigated in isolated neocortical slabs and intact cortex of anaesthetized cats by means of single, dual and triple simultaneous intracellular recordings in conjunction with recordings of local field potential responses. In isolated cortical slabs (10 mm long, 6 mm wide and 4–5 mm deep), augmenting responses to pulse‐trains at 10 Hz (responses with growing amplitudes from the second stimulus in a train) were elicited only by relatively high‐intensity stimuli. At low intensities, responses were decremental. The largest augmenting responses were evoked in neurones located close to the stimulation site. Quantitative analyses of the number of action potentials and the amplitude and area of depolarization during augmenting responses in a population of neurones recorded from slabs showed that the most dramatic increases in the number of spikes with successive stimuli, and the greatest increase in depolarization amplitude, were found in conventional fast‐spiking (FS) neurones. The largest increase in the area of depolarization was found in regular‐spiking (RS) neurones. Dual intracellular recordings from a pair of FS and RS neurones in the slab revealed more action potentials in the FS neurone during augmenting responses and a significant increase in the depolarization area of the RS neurone that was dependent on the firing of the FS neurone. Self‐sustained seizures could occur in the slab after rhythmic stimuli at 10 Hz. In the intact cortex, repeated sequences of stimuli generating augmenting responses or spontaneous spindles could induce an increased synaptic responsiveness to single stimuli, which lasted for several minutes. A similar time course of increased responsiveness was obtained with induction of cellular plasticity. These data suggest that augmenting responses elicited by stimulation, as well as spontaneously occurring spindles, may induce short‐ and medium‐term plasticity of neuronal responses.
Attractor Reliability Reveals Deterministic Structure in Neuronal Spike Trains

When periodic current is injected into an integrate-and-fire model neuron, the voltage as a function of time converges from different initial conditions to an attractor that produces reproducible sequences of spikes. The attractor reliability is a measure of the stability of spike trains against intrinsic noise and is quantified here as the inverse of the number of distinct spike trains obtained in response to repeated presentations of the same stimulus. High reliability characterizes neurons that can support a spike-time code, unlike neurons with discharges forming a renewal process (such as a Poisson process). These two classes of responses cannot be distinguished using measures based on the spike-time histogram, but they can be identified by the attractor dynamics of spike trains, as shown here using a new method for calculating the attractor reliability. We applied these methods to spike trains obtained from current injection into cortical neurons recorded in vitro. These spike trains did not form a renewal process and had a higher reliability compared to renewal-like processes with the same spike-time histogram.
Face recognition by independent component analysis

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.
Practical Security in Public-Key Cryptography

View A HYBRID ELECTRO-DIFFUSION MODEL FOR NEURAL SIGNALING. A new method is introduced for modeling the three-dimensional movement of ions in neurons. Using the Nernst-Planck equation, concentration gradients and electric fields were evaluated using a weighted moving least-squares algorithm. We incorporate this method into MCell, a Monte-Carlo cell simulator, and present preliminary validation under several testing scenarios. We apply the method to a reactive-diffusive simulation of an action potential propagating through an unmyelinated axon, with discrete sodium and potassium channels modeled by a voltage-dependent Markov random process. For large diameter axons, the spatio-temporal dynamics of the membrane potential averaged over several runs converges to results obtained from the Hodgkin and Huxley model implemented in NEURON. The results also corroborate previous stochastic simulations by other workers demonstrating that at thin diameters channel noise is sufficient to induce action potentials*.
Dynamic Brain Sources of Visual Evoked Responses

It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a human visual selective attention task, we show that nontarget event-related potentials were mainly generated by partial stimulus-induced phase resetting of multiple electroencephalographic processes. Independent component analysis applied to the single-trial data identified at least eight classes of contributing components, including those producing central and lateral posterior alpha, left and right mu, and frontal midline theta rhythms. Scalp topographies of these components were consistent with their generation in compact cortical domains.
AND SIZE OF A FLASH CAN BE MODIFIED BY TEMPORAL PROPERTIES OF ITS NEIGHBORS

It is known that the perceived brightness of an object depends on spatial relationships with its neighbors (e.g., simultaneous contrast). We here examined whether the perceived brightness of a briefly flashed object depends on TEMPORAL relationships with its neighbors. Two flashes appeared separated by a fixation point: one lasted for 56 ms ('brief'), the other for 278 ms ('long'). Observers reported which flash appeared brighter. When the flashes had simultaneous onset, subjects reported that the brief flash looked dimmer than the long flash; however, when the flashes had simultaneous offset, the brief flash now appeared brighter. This is, to our knowledge, a novel illusion, which we term the Temporal Neighborhood Effect. The perceived brightness of the brief flash increased monotonically and linearly with increasing SOA. When the background was of higher luminance than the flashes, the effect was reversed. The differential effect was lost when the flashes were isoluminant with a colored background. These results suggest that the luminance of the brief flashes becomes temporally integrated with the background. Ten further experiments show that not only the brightness, but also the size and duration of a brief flash are modified by the onset and offset timings of neighbors. It seems that we are unaware of brightness changes, instead being only aware of the result of integrations over these changes. The duration of the integration window may be changed by salient events in neighboring locations in the visual scene. We propose a model for the neural substrate underlying this phenomenon.
Information transfer in entrained cortical neurons

Cortical interneurons connected by gap junctions can provide a synchronized inhibitory drive that can entrain pyramidal cells. This was studied in a single-compartment Hodgkin-Huxley-type model neuron that was entrained by periodic inhibitory inputs with low jitter in the input spike times (i.e. high precision), and a variable but large number of presynaptic spikes on each cycle. During entrainment the Shannon entropy of the output spike times was reduced sharply compared with its value outside entrainment. Surprisingly, however, the information transfer as measured by the mutual information between the number of inhibitory inputs in a cycle and the phase lag of the subsequent output spike was significantly increased during entrainment. This increase was due to the reduced contribution of the internal correlations to the output variability. These theoretical predictions were supported by experimental recordings from the rat neocortex and hippocampus in vitro.
Model of Thalamocortical Slow-Wave Sleep Oscillations and Transitions to Activated States

During natural slow-wave sleep (SWS) in nonanesthetized cats, silent (down) states alternate with active (up) states; the down states are absent during rapid-eye-movement sleep and waking. Oscillations (<1 Hz) in SWS and transformation to an activated awake state were investigated with intracellular recordings in vivo and with computational models of the corticothalamic system. Occasional summation of the miniature EPSPs during the hyperpolarized (silent) phase of SWS oscillation activated the persistent sodium current and depolarized the membrane of cortical pyramidal (PY) cells sufficiently for spike generation. In the model, this triggered the active phase, which was maintained by lateral PY–PY excitation and persistent sodium current. Progressive depression of the excitatory interconnections and activation of Ca2+-dependent K+ current led to termination of the 20–25 Hz activity after 500–1000 msec. Including thalamocortical (TC) and thalamic reticular neurons in the model increased the duration of the active epochs up to 1–1.5 sec and introduced waning spindle sequences. An increase in acetylcholine activity, which is associated with activated states, was modeled by the reduction in the K+ leak current in PY and TC cells and by a decrease in intracortical PY–PY synaptic conductances. These changes eliminated the hyperpolarizing phases of network activity and transformed cortical neurons to tonic firing at 15–20 Hz. During the transition from SWS to the activated state, the input resistance of cortical neurons gradually increased and, in a fully activated state, reached the same or even higher values as during silent phases of SWS oscillations. The model describes many essential features of SWS and activated states in the thalamocortical system as well as the transition between them.
Slow Feature Analysis: Unsupervised Learning of Invariances

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decor-related features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.
Information transfer in entrained cortical neurons.

Cortical interneurons connected by gap junctions can provide a synchronized inhibitory drive that can entrain pyramidal cells. This was studied in a single-compartment Hodgkin-Huxley-type model neuron that was entrained by periodic inhibitory inputs with low jitter in the input spike times (i.e. high precision), and a variable but large number of presynaptic spikes on each cycle. During entrainment the Shannon entropy of the output spike times was reduced sharply compared with its value outside entrainment. Surprisingly, however, the information transfer as measured by the mutual information between the number of inhibitory inputs in a cycle and the phase lag of the subsequent output spike was significantly increased during entrainment. This increase was due to the reduced contribution of the internal correlations to the output variability. These theoretical predictions were supported by experimental recordings from the rat neocortex and hippocampus in vitro.
Synchronization as a mechanism for attentional modulation

References Neuronal responses can often be written as a product of two stimulus features: firing rate=f(x)g(y). For instance, contrast (y) modulates the gain of the orientation tuning curve (x) (McAdams & Maunsell, 1999). Attention may modulate neuronal response properties in a similar way. When attention is shifted to the receptive field of a neuron, the firing of the neuronmaybecome 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 (2001)). Here we explore the hypothesis that synchrony modulates the gain of neuronal responses.