nils-herrmann/clean_scirepeval huggingface dataset:
Self-Organizing Map Formation: Foundations of Neural Computation

From the Publisher: 
This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation. 
The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. 
Contributors 
 
 Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille,
Predictive learning of temporal sequences in recurrent neocortical circuits.

When a spike is initiated near the soma of a cortical pyramidal neuron, it may back-propagate up dendrites toward distal synapses, where strong depolarization can trigger spike-timing dependent Hebbian plasticity at recently activated synapses. We show that (a) these mechanisms can implement a temporal-difference algorithm for sequence learning, and (b) a population of recurrently connected neurons with this form of synaptic plasticity can learn to predict spatiotemporal input patterns. Using biophysical simulations, we demonstrate that a network of cortical neurons can develop direction selectivity similar to that observed in complex cells in alert monkey visual cortex as a consequence of learning to predict moving stimuli.
Thalamocortical Assemblies: How Ion Channels, Single Neurons and Large-Scale Networks Organize Sleep Oscillations

PART I - INTRODUCTION 1.1 Brain rhythmicities 1.2 Early views on brain rhythmicity 1.3 Origins of brain rhythmicity 1.4 Identification of the key neuronal structures 1.5 Thalamocortical assemblies PART II - BIOPHYSICAL MODELS OF THE MEMBRANE POTENTIAL AND IONIC CURRENTS 2.1 Ionic bases of neuronal excitability 2.2 Calcium-dependent ion channels 2.3 Markov models of voltage-dependent ion channels PART III - ELECTROPHYSIOLOGICAL PROPERTIES OF THALAMIC RELAY NEURONS 3.1 The bursting properties of thalamic relay neurons 3.2 Oscillatory properties of thalamic relay cells 3.3 Intrinsic waxing-and-waning oscillations in thalamic relay cells 3.4 Dendritic T-current in thalamic relay cells PART IV - ELECTROPHYSIOLOGICAL PROPERTIES OF THALAMIC RETICULAR NEURONS 4.1 The rebound burst of thalamic reticular cells 4.2 Intrinsic oscillations in RE cells 4.3 Dendritic T-current in thalamic reticular cells PART V - BIOPHYSICAL MODELS OF SYNAPTIC INTERACTIONS 5.1 Transmitter release 5.2 Models for different types of postsynaptic receptors 5.3 Models of synaptic transmission including extracellular diffusion of transmitter PART VI - SPINDLE OSCILLATIONS IN THALAMIC CIRCUITS 6.1 Experimental characterization of sleep spindle oscillations 6.2 Models of rhythmicity in the isolated reticular nucleus 6.3 Models of rhythmicity arising from thalamic relay-reticular interactions 6.4 Why does the RE nucleus oscillate in vivo but not in vitro? 6.5 Network model of spindle oscillations in ferret thalamic slices 6.6 Intrathalamic augmenting responses PART VII - SPINDLE OSCILLATIONS IN THE THALAMOCORTICAL SYSTEM 7.1 Experimental characterization of spindle oscillations in the thalamocortical system 7.2 A thalamocortical network model of spindle oscillations 7.3 The large-scale synchrony of spindle oscillations during natural sleep 7.4 Thalamocortical augmenting responses PART VIII - THALAMOCORTICAL MECHANISMS FOR SPIKE-AND-WAVE EPILEPTIC SEIZURES 8.1 Experimental characterization of paroxysmal oscillations 8.2 Modeling the genesis of paroxysmal discharges in the thalamus 8.3 Model of spike-and-wave oscillations in the thalamocortical system PART IX - A PHYSIOLOGICAL ROLE FOR SLEEP OSCILLATIONS? 9.1 Impact of thalamic inputs on neocortical neurons 9.2 Oscillations during natural sleep and wakefulness 9.3 A possible function for sleep oscillations 9.4 A computational theory of sleep A - IONIC BASES OF THE MEMBRANE POTENTIAL B - OPTIMIZED ALGORITHMS FOR SIMULATING SYNAPTIC CURRENTS C - DATA AVAILABLE ON THE INTERNET
MEASURING THE VARIABILITY OF EVENT-RELATED BOLD SIGNAL

Most current analysis methods for functional magnetic resonance imaging (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, different approaches are needed. Here we used infomax Independent Component Analysis (ICA) to detect and visualize variations in single-trial HRs in event-related fMRI data. ICA decomposition of the resulting BOLD data produced independent components with variable stimulus-locked HRs active in primary visual (V1) and medial temporal (MT/V5) cortices respectively. Contrary to expectation, in four of six subjects the HR of the V1 component contained two positive peaks in response to short-stimulus bursts, while nearly identical component maps were associated with single-peaked HRs in long-stimulus sessions from the same subject. Thus, ICA combined with single-trial visualization can reveal dramatic and unforeseen task-related HR variation not apparent to researchers analyzing the data with fixed HR
DECORRELATION OF ODOR REPRESENTATIONS IN THE LOCUST ANTENNAL LOBE

Odor stimulation evokes complex stimulus-specific spatio-temporal patterns of activity in the projection neurons (PNs) of the locust antennal lobe (AL). These patterns, consisting of fast (20-30Hz) oscillations superimposed upon a slower temporal structure of alternating depolarizing and hyperpolarizing epochs, result from intrinsic dynamic network properties activated by the presentation of odorants. These dynamics have been shown to optimize odor representations and thus contribute to odor discrimination. In zebrafish the slow temporal patterning in mitral cells appears to play a major role in the decorrelation of odor representations [ 5505:835, 2001]. Here we tested with a computational model the hypothesis that locust AL intrinsic dynamics can serve a similar function. Upon odor presentation, fast (about 20 Hz) field potential oscillations emerged and were maintained by interactions between excitatory PNs and inhibitory local neurons. When similar odors were presented, the response patterns of PNs strongly overlapped at first. After a few hundred msec, however, the overlap between similar odor representations was reduced and the initial clusters of activated PNs disappeared; cross-correlations between PN activity patterns were reduced by 30-50%. The correlations increased again following the brief activity increase occurring after stimulus termination. Thus, our model predicts that intrinsic dynamic properties emerging from interactions of PNs and LNs can amplify small differences between similar odors, thus improving odor discriminability. Supported by: NIH, HHMI and NIDCD
Imaging brain dynamics using independent component analysis

The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.
Awareness during drowsiness: dynamics and electrophysiological correlates.

During drowsy periods, performance on tasks requiring continuous attention becomes intermittent. Previously, we have reported that during drowsy periods of intermittent performance, 7 of 10 participants performing an auditory detection task exhibited episodes of non-responding lasting about 18 s (Makeig & Jung, 1996). Further, the time patterns of these episodes were repeated precisely in subsequent sessions. The 18-s cycles were accompanied by counterbalanced power changes within two frequency bands in the vertex EEG (near 4 Hz and circa 40 Hz). In the present experiment, performance patterns and concurrent EEG spectra were examined in four participants performing a continuous visuomotor compensatory tracking task in 15-20 minute bouts during a 42-hour sleep deprivation study. During periods of good performance, participants made compensatory trackball movements about twice per second, attempting to keep a target disk near a central ring. Autocorrelations of time series representing the distance of the target disk from the ring centre showed that during periods of poor performance marked near-18-s cycles in performance again appeared. There were phases of poor or absent performance accompanied by an increase in EEG power that was largest at 3-4 Hz. These studies show that in drowsy humans, opening and closing of the gates of behavioural awareness is marked not by the appearance of (12-14 Hz) sleep spindles, but by prominent EEG amplitude changes in the low theta band. Further, both EEG and behavioural changes during drowsiness often exhibit stereotyped 18-s cycles.
A Comparison of Image Processing Techniques for Visual Speech Recognition Applications

We examine eight different techniques for developing visual representations in machine vision tasks. In particular we compare different versions of principal component and independent component analysis in combination with stepwise regression methods for variable selection. We found that local methods, based on the statistics of image patches, consistently outperformed global methods based on the statistics of entire images. This result is consistent with previous work on emotion and facial expression recognition. In addition, the use of a stepwise regression technique for selecting variables and regions of interest substantially boosted performance.
The Spectral Independent Components of Natural Scenes

We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.
Precision of the pacemaker nucleus in a weakly electric fish: network versus cellular influences.

We investigated the relative influence of cellular and network properties on the extreme spike timing precision observed in the medullary pacemaker nucleus (Pn) of the weakly electric fish Apteronotus leptorhynchus. Of all known biological rhythms, the electric organ discharge of this and related species is the most temporally precise, with a coefficient of variation (CV = standard deviation/mean period) of 2 x 10(-4) and standard deviation (SD) of 0.12-1.0 micros. The timing of the electric organ discharge is commanded by neurons of the Pn, individual cells of which we show in an in vitro preparation to have only a slightly lesser degree of precision. Among the 100-150 Pn neurons, dye injection into a pacemaker cell resulted in dye coupling in one to five other pacemaker cells and one to three relay cells, consistent with previous results. Relay cell fills, however, showed profuse dendrites and contacts never seen before: relay cell dendrites dye-coupled to one to seven pacemaker and one to seven relay cells. Moderate (0.1-10 nA) intracellular current injection had no effect on a neuron's spiking period, and only slightly modulated its spike amplitude, but could reset the spike phase. In contrast, massive hyperpolarizing current injections (15-25 nA) could force the cell to skip spikes. The relative timing of subthreshold and full spikes suggested that at least some pacemaker cells are likely to be intrinsic oscillators. The relative amplitudes of the subthreshold and full spikes gave a lower bound to the gap junctional coupling coefficient of 0.01-0.08. Three drugs, called gap junction blockers for their mode of action in other preparations, caused immediate and substantial reduction in frequency, altered the phase lag between pairs of neurons, and later caused the spike amplitude to drop, without altering the spike timing precision. Thus we conclude that the high precision of the normal Pn rhythm does not require maximal gap junction conductances between neurons that have ordinary cellular precision. Rather, the spiking precision can be explained as an intrinsic cellular property while the gap junctions act to frequency- and phase-lock the network oscillations.
Motion integration and postdiction in visual awareness.

In the flash-lag illusion, a flash and a moving object in the same location appear to be offset. A series of psychophysical experiments yields data inconsistent with two previously proposed explanations: motion extrapolation (a predictive model) and latency difference (an online model). We propose an alternative in which visual awareness is neither predictive nor online but is postdictive, so that the percept attributed to the time of the flash is a function of events that happen in the approximately 80 milliseconds after the flash. The results here show how interpolation of the past is the only framework of the three models that provides a unified explanation for the flash-lag phenomenon.
INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS

(1) Computational Neurobiology Laboratory, Howard Hughes Medical Institute The Salk Institute for Biological Studies; (2) Institute for Neural Computation, University of California San Diego, La Jolla CA; (3) Department of Biology, University of California San Diego, La Jolla CA. (4) Naval Health Research Center, San Diego CA; (5) Department of Medicine (Neurology), Duke University (6) Brain Imaging and Analysis Center (BIAC), Duke University; (7) Center for Cognitive Neuroscience, Duke University, Durham, NC {
Cholinergic induction of oscillations in the hippocampal slice in the slow (0.5–2 Hz), theta (5–12 Hz), and gamma (35–70 Hz) bands

Carbachol, a muscarinic receptor agonist, produced three distinct spontaneous oscillations in the CA3 region of rat hippocampal slices. Carbachol concentrations in the 4–13 μM range produced regular synchronized CA3 discharges at 0.5–2 Hz (carbachol‐delta). Higher concentrations (13–60 μM) produced short episodes of 5–10 Hz (carbachol‐theta) oscillations separated by nonsynchronous activity. Concentrations of carbachol ranging from 8–25 μM also produced irregular episodes of high‐frequency discharges (carbachol‐gamma, 35–70 Hz), in isolation or mixed with carbachol‐theta and carbachol‐delta. At carbachol concentrations sufficient to induce carbachol‐theta, low concentrations of APV reversibly transformed carbachol‐theta into carbachol‐delta. Higher concentrations of D,L‐2‐amino‐5‐phosphonopentanoic acid (APV) reversibly and completely blocked carbachol‐theta. A systematic study of the effects of carbachol shows that the frequency of spontaneous oscillations depended nonlinearly on the level of muscarinic activation. Field and intracellular recordings from CA1 and CA3 pyramidal cells and interneurons during carbachol‐induced rhythms revealed that the hippocampal circuitry preserved in the slice was capable of spontaneous activity over the range of frequencies observed in vivo and suggests that the presence of these rhythms could be under neuromodulatory control.
ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation

An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, non-Gaussian densities. The algorithm estimates the density of each class and is able to model class distributions with non-Gaussian structure. The new algorithm can improve classification accuracy compared with standard Gaussian mixture models. When applied to blind source separation in nonstationary environments, the method can switch automatically between classes, which correspond to contexts with different mixing properties. The algorithm can learn efficient codes for images containing both natural scenes and text. This method shows promise for modeling non-Gaussian structure in high-dimensional data and has many potential applications.
Removing electroencephalographic artifacts by blind source separation.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
Pyramidal Neurons Afterhyperpolarization in Cultured Rat Hippocampal Channels Involved in the Generation of the Slow 2 + Ca

, February 1, 2007; 578 (3): 799-818.  Catherine Cook Kaczorowski, John Disterhoft and Nelson Spruston neurons: effects of internal anions Stability and plasticity of intrinsic membrane properties in hippocampal CA1 pyramidal [PDF]  [ , May 1, 2008; 99 (5): 2134-2143.  Modulating L-Type Calcium Channels Neurotensin Enhances GABAergic Activity in Rat Hippocampus CA1 Region by [PDF]  [ , April 15, 2009; 29 (15): 4750-4755.  Elizabeth A. Matthews, John M. Linardakis and John F. Disterhoft Neurons by Aging and Learning The Fast and Slow Afterhyperpolarizations Are Differentially Modulated in Hippocampal [PDF]  [ , December , 2009; 102 (6): 3234-3250. J Neurophysiol Vladislav Volman, Herbert Levine, Eshel Ben-Jacob and Terrence J. Sejnowski Dendritic Conductances Locally Balanced Dendritic Integration by Short-Term Synaptic Plasticity and Active [PDF]  [ , April , 2011; 300 (4): C937-C949.
Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex.

The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABA(A) antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.