Literature DB >> 12633465

Second order phase transition in neural rate coding: binary encoding is optimal for rapid signal transmission.

Matthias Bethge1, David Rotermund, Klaus Pawelzik.   

Abstract

Here, we derive optimal tuning functions for minimum mean square reconstruction from neural rate responses subjected to Poisson noise. The shape of these tuning functions strongly depends on the length T of the time window within which action potentials (spikes) are counted in order to estimate the underlying firing rate. A phase transition towards pure binary encoding occurs if the maximum mean spike count becomes smaller than approximately three. For a particular function class, we prove the existence of a second-order phase transition. The analytically derived critical decoding time window length is in precise agreement with numerical results. Our analysis reveals that binary rate encoding should dominate in the brain wherever time is the critical constraint.

Mesh:

Year:  2003        PMID: 12633465     DOI: 10.1103/PhysRevLett.90.088104

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  7 in total

1.  Role of homeostasis in learning sparse representations.

Authors:  Laurent U Perrinet
Journal:  Neural Comput       Date:  2010-07       Impact factor: 2.026

2.  A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.

Authors:  Martin Rehn; Friedrich T Sommer
Journal:  J Comput Neurosci       Date:  2007-04       Impact factor: 1.621

3.  Zipf's law in short-time timbral codings of speech, music, and environmental sound signals.

Authors:  Martín Haro; Joan Serrà; Perfecto Herrera; Alvaro Corral
Journal:  PLoS One       Date:  2012-03-29       Impact factor: 3.240

4.  Frequency-invariant representation of interaural time differences in mammals.

Authors:  Hannes Lüling; Ida Siveke; Benedikt Grothe; Christian Leibold
Journal:  PLoS Comput Biol       Date:  2011-03-17       Impact factor: 4.475

5.  Optimum neural tuning curves for information efficiency with rate coding and finite-time window.

Authors:  Fang Han; Zhijie Wang; Hong Fan; Xiaojuan Sun
Journal:  Front Comput Neurosci       Date:  2015-06-03       Impact factor: 2.380

6.  Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency.

Authors:  Fang Han; Zhijie Wang; Hong Fan
Journal:  Front Comput Neurosci       Date:  2017-02-21       Impact factor: 2.380

7.  An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features.

Authors:  Laurent U Perrinet
Journal:  Vision (Basel)       Date:  2019-09-16
  7 in total

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