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Neural Computation

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https://www.readbyqxmd.com/read/27870617/analytical-calculation-of-mutual-information-between-weakly-coupled-poisson-spiking-neurons-in-models-of-dynamically-gated-communication
#1
Jonathan Cannon
Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870616/comparison-of-different-generalizations-of-clustering-coefficient-and-local-efficiency-for-weighted-undirected-graphs
#2
Yu Wang, Eshwar Ghumare, Rik Vandenberghe, Patrick Dupont
Binary undirected graphs are well established, but when these graphs are constructed, often a threshold is applied to a parameter describing the connection between two nodes. Therefore, the use of weighted graphs is more appropriate. In this work, we focus on weighted undirected graphs. This implies that we have to incorporate edge weights in the graph measures, which require generalizations of common graph metrics. After reviewing existing generalizations of the clustering coefficient and the local efficiency, we proposed new generalizations for these graph measures...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870615/energy-model-of-neuron-activation
#3
Yuriy Romanyshyn, Andriy Smerdov, Svitlana Petrytska
On the basis of the neurophysiological strength-duration (amplitude-duration) curve of neuron activation (which relates the threshold amplitude of a rectangular current pulse of neuron activation to the pulse duration), as well as with the use of activation energy constraint (the threshold curve corresponds to the energy threshold of neuron activation by a rectangular current pulse), an energy model of neuron activation by a single current pulse has been constructed. The constructed model of activation, which determines its spectral properties, is a bandpass filter...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870614/active-inference-a-process-theory
#4
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo
This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870613/orientation-histogram-based-center-surround-interaction-an-integration-approach-for-contour-detection
#5
Rongchang Zhao, Min Wu, Xiyao Liu, Beiji Zou, Fangfang Li
Contour is a critical feature for image description and object recognition in many computer vision tasks. However, detection of object contour remains a challenging problem because of disturbances from texture edges. This letter proposes a scheme to handle texture edges by implementing contour integration. The proposed scheme integrates structural segments into contours while inhibiting texture edges with the help of the orientation histogram-based center-surround interaction model. In the model, local edges within surroundings exert a modulatory effect on central contour cues based on the co-occurrence statistics of local edges described by the divergence of orientation histograms in the local region...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870612/the-population-tracking-model-a-simple-scalable-statistical-model-for-neural-population-data
#6
Cian O'Donnell, J Tiago Gonçalves, Nick Whiteley, Carlos Portera-Cailliau, Terrence J Sejnowski
Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870611/exponentially-long-orbits-in-hopfield-neural-networks
#7
Samuel P Muscinelli, Wulfram Gerstner, Johanni Brea
We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For [Formula: see text] units, these sequences have length [Formula: see text]; that is, they cover the full state-space. We present a mathematical proof that maximal-length orbits exist for all [Formula: see text], and we provide a method to construct both the sequence and the weight matrix that allow its production. The orbit is relatively robust to dynamical noise, and perturbations of the optimal weights reveal other periodic orbits that are not maximal but typically still very long...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870610/neural-circuits-trained-with-standard-reinforcement-learning-can-accumulate-probabilistic-information-during-decision-making
#8
Nils Kurzawa, Christopher Summerfield, Rafal Bogacz
Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27870609/time-series-decomposition-into-oscillation-components-and-phase-estimation
#9
Takeru Matsuda, Fumiyasu Komaki
Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764599/multiple-diffusion-models-to-compare-saccadic-and-manual-responses-for-inhibition-of-return
#10
William Joseph MacInnes
Cuing a location in space produces a short-lived advantage in reaction time to targets at that location. This early advantage, however, switches to a reaction time cost and has been termed inhibition of return (IOR). IOR behaves differently for different response modalities, suggesting that it may not be a unified effect. This letter presents new data from two experiments testing the gradient of IOR with random, continuous cue-target Euclidean distance and cue-target onset asynchrony. This data were then used to train multiple diffusion models of saccadic and manual reaction time for these cuing experiments...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764597/on-decoding-grid-cell-population-codes-using-approximate-belief-propagation
#11
Yongseok Yoo, Woori Kim
Neural systems are inherently noisy. One well-studied example of a noise reduction mechanism in the brain is the population code, where representing a variable with multiple neurons allows the encoded variable to be recovered with fewer errors. Studies have assumed ideal observer models for decoding population codes, and the manner in which information in the neural population can be retrieved remains elusive. This letter addresses a mechanism by which realistic neural circuits can recover encoded variables...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764596/lsv-based-tail-inequalities-for-sums-of-random-matrices
#12
Chao Zhang, Lei Du, Dacheng Tao
The techniques of random matrices have played an important role in many machine learning models. In this letter, we present a new method to study the tail inequalities for sums of random matrices. Different from other work (Ahlswede & Winter, 2002; Tropp, 2012; Hsu, Kakade, & Zhang, 2012), our tail results are based on the largest singular value (LSV) and independent of the matrix dimension. Since the LSV operation and the expectation are noncommutative, we introduce a diagonalization method to convert the LSV operation into the trace operation of an infinitely dimensional diagonal matrix...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764594/fast-accurate-localization-of-epileptic-seizure-onset-zones-based-on-detection-of-high-frequency-oscillations-using-improved-wavelet-transform-and-matching-pursuit-methods
#13
Min Wu, Ting Wan, Xiongbo Wan, Yuxiao Du, Jinhua She
This letter describes the improvement of two methods of detecting high-frequency oscillations (HFO) and their use to localize epileptic seizure onset zones (SOZs). The wavelet transform (WT) method was improved by combining the complex Morlet WT with Shannon entropy to enhance the temporal-frequency resolution during HFO detection. And the matching pursuit (MP) method was improved by combining it with an adaptive genetic algorithm to improve the speed and accuracy of the calculations for HFO detection. The HFOs detected by these two methods were used to localize SOZs in five patients...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764593/on-the-mathematical-consequences-of-binning-spike-trains
#14
Bruno Cessac, Arnaud Le Ny, Eva Löcherbach
We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process that is no longer Markov but is instead a variable-length Markov chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764592/selective-interareal-synchronization-through-gamma-frequency-differences-and-slower-rhythm-gamma-phase-reset
#15
Thomas Burwick, Alexandros Bouras
The communication-through-coherence (CTC) hypothesis states that a sending group of neurons will have a particularly strong effect on a receiving group if both groups oscillate in a phase-locked ("coherent") manner (Fries, 2005, 2015). Here, we consider a situation with two visual stimuli, one in the focus of attention and the other distracting, resulting in two sites of excitation at an early cortical area that project to a common site in a next area. Taking a modeler's perspective, we confirm the workings of a mechanism that was proposed by Bosman et al...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764591/improving-the-incoherence-of-a-learned-dictionary-via-rank-shrinkage
#16
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first updated the dictionary using the method of optimal directions (MOD) and then applied a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764590/online-reinforcement-learning-using-a-probability-density-estimation
#17
Alejandro Agostini, Enric Celaya
Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In kinds of tasks, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27764589/a-combinatorial-model-for-dentate-gyrus-sparse-coding
#18
William Severa, Ojas Parekh, Conrad D James, James B Aimone
The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation-similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus's (DG) coding. We suggest a theoretically tractable, combinatorial model for this action. The model provides formal methods for a highly redundant, arbitrarily sparse, and decorrelated output signal...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27626970/variations-on-the-theme-of-synaptic-filtering-a-comparison-of-integrate-and-express-models-of-synaptic-plasticity-for-memory-lifetimes
#19
Terry Elliott
Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binary-strength synapses for associative memory storage, we have previously shown that such a filter-based model outperforms other, nonintegrative, "cascade"-type models of memory storage in most regions of biologically relevant parameter space...
September 14, 2016: Neural Computation
https://www.readbyqxmd.com/read/27626967/an-online-structural-plasticity-rule-for-generating-better-reservoirs
#20
Subhrajit Roy, Arindam Basu
In this letter, we propose a novel neuro-inspired low-resolution online unsupervised learning rule to train the reservoir or liquid of liquid state machines. The liquid is a sparsely interconnected huge recurrent network of spiking neurons. The proposed learning rule is inspired from structural plasticity and trains the liquid through formating and eliminating synaptic connections. Hence, the learning involves rewiring of the reservoir connections similar to structural plasticity observed in biological neural networks...
September 14, 2016: Neural Computation
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