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Title: Mutual Information and Information Gating in Synfire Chains

Abstract

Here, coherent neuronal activity is believed to underlie the transfer and processing of information in the brain. Coherent activity in the form of synchronous firing and oscillations has been measured in many brain regions and has been correlated with enhanced feature processing and other sensory and cognitive functions. In the theoretical context, synfire chains and the transfer of transient activity packets in feedforward networks have been appealed to in order to describe coherent spiking and information transfer. Recently, it has been demonstrated that the classical synfire chain architecture, with the addition of suitably timed gating currents, can support the graded transfer of mean firing rates in feedforward networks (called synfire-gated synfire chains—SGSCs). Here we study information propagation in SGSCs by examining mutual information as a function of layer number in a feedforward network. We explore the effects of gating and noise on information transfer in synfire chains and demonstrate that asymptotically, two main regions exist in parameter space where information may be propagated and its propagation is controlled by pulse-gating: a large region where binary codes may be propagated, and a smaller region near a cusp in parameter space that supports graded propagation across many layers.

Authors:
 [1];  [2]; ORCiD logo [3];  [2]
  1. Univ. of Arizona, Tucson, AZ (United States)
  2. Peking Univ., Beijing (China)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of California, Davis, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1419762
Report Number(s):
LA-UR-17-31404
Journal ID: ISSN 1099-4300; ENTRFG
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Entropy
Additional Journal Information:
Journal Volume: 20; Journal Issue: 2; Journal ID: ISSN 1099-4300
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science; Computer Science; Mathematics; pulse gating; channel capacity; neural coding; feedforward networks; neural information propagation

Citation Formats

Xiao, Zhuocheng, Wang, Binxu, Sornborger, Andrew Tyler, and Tao, Louis. Mutual Information and Information Gating in Synfire Chains. United States: N. p., 2018. Web. doi:10.3390/e20020102.
Xiao, Zhuocheng, Wang, Binxu, Sornborger, Andrew Tyler, & Tao, Louis. Mutual Information and Information Gating in Synfire Chains. United States. doi:10.3390/e20020102.
Xiao, Zhuocheng, Wang, Binxu, Sornborger, Andrew Tyler, and Tao, Louis. 2018. "Mutual Information and Information Gating in Synfire Chains". United States. doi:10.3390/e20020102. https://www.osti.gov/servlets/purl/1419762.
@article{osti_1419762,
title = {Mutual Information and Information Gating in Synfire Chains},
author = {Xiao, Zhuocheng and Wang, Binxu and Sornborger, Andrew Tyler and Tao, Louis},
abstractNote = {Here, coherent neuronal activity is believed to underlie the transfer and processing of information in the brain. Coherent activity in the form of synchronous firing and oscillations has been measured in many brain regions and has been correlated with enhanced feature processing and other sensory and cognitive functions. In the theoretical context, synfire chains and the transfer of transient activity packets in feedforward networks have been appealed to in order to describe coherent spiking and information transfer. Recently, it has been demonstrated that the classical synfire chain architecture, with the addition of suitably timed gating currents, can support the graded transfer of mean firing rates in feedforward networks (called synfire-gated synfire chains—SGSCs). Here we study information propagation in SGSCs by examining mutual information as a function of layer number in a feedforward network. We explore the effects of gating and noise on information transfer in synfire chains and demonstrate that asymptotically, two main regions exist in parameter space where information may be propagated and its propagation is controlled by pulse-gating: a large region where binary codes may be propagated, and a smaller region near a cusp in parameter space that supports graded propagation across many layers.},
doi = {10.3390/e20020102},
journal = {Entropy},
number = 2,
volume = 20,
place = {United States},
year = 2018,
month = 2
}

Journal Article:
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  • Commonly used dependence measures, such as linear correlation, cross-correlogram or Kendall's Tau, cannot capture the complete dependence structure in data unless the structure is restricted to linear, periodic or monotonic. Mutual information (MI) has been frequently utilized for capturing the complete dependence structure including nonlinear dependence. Recently, several methods have been proposed for the MI estimation, such as kernel density estimators (KDE), k-nearest neighbors (KNN), Edgeworth approximation of differential entropy, and adaptive partitioning of the XY plane. However, outstanding gaps in the current literature have precluded the ability to effectively automate these methods, which, in turn, have caused limited adoptionsmore » by the application communities. This study attempts to address a key gap in the literature, specifically, the evaluation of the above methods to choose the best method, particularly in terms of their robustness for short and noisy data, based on comparisons with the theoretical MI estimates, which can be computed analytically, as well with linear correlation and Kendall's Tau. Here we consider smaller data sizes, such as 50, 100, and 1 000, where this study considers 50 and 100 data points as very short and 1 000 as short. We consider a broader class of functions, specifically linear, quadratic, periodic and chaotic, contaminated with artificial noise with varying noise-to-signal ratios. The case studies presented here are motivated by domain consideration in the earth sciences where the data are short and noisy. Our results indicate KDE as the best choice for very short data at relatively high noise-to-signal levels whereas the performance of KNN is the best for short data at relatively low noise levels as well as for short data consistently across noise levels. In addition, the optimal smoothing parameter of a Gaussian kernel appears to be the best choice for KDE while three nearest neighbors appear optimal for KNN. Thus, in situations where the approximate data sizes are known in advance, and exploratory data analysis and/or domain knowledge can be used to provide a priori insights on the noise-to-signal ratios, the results in the paper point to a way forward for automating the process of MI estimation.« less