English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  How pairwise correlations affect the redundancy in large populations of neurons

Macke, J., Opper, M., & Bethge, M. (2008). How pairwise correlations affect the redundancy in large populations of neurons. Frontiers in Computational Neuroscience, 2008(Conference Abstract: Bernstein Symposium 2008).

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0003-8B87-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A165-5
Genre: Meeting Abstract

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Macke, J1, 2, Author              
Opper, M, Author
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Simultaneously recorded neurons often exhibit correlations in their spiking activity. These correlations shape the statistical structure of the population activity, and can lead to substantial redundancy across neurons. Knowing the amount of redundancy in neural responses is critical for our understanding of the neural code. Here, we study the effect of pairwise correlations on the statistical structure of population activity. We model correlated activity as arising from common Gaussian inputs into simple threshold neurons. In population models with exchangeable correlation structure, one can analytically calculate the distribution of synchronous events across the whole population, and the joint entropy (and thus the redundancy) of the neural responses. We investigate the scaling of the redundancy as the population size is increased, and characterize its phase transitions for increasing correlation strengths. We compare the asymptotic redundancy in our models to the corresponding maximum- and minimum entropy models. Although this model must exhibit more redundancy than the maximum entropy model, we find that its joint entropy increases linearly with population size.

Details

show
hide
Language(s):
 Dates: 2008-11
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3389/conf.neuro.10.2008.01.086
 Degree: -

Event

show
hide
Title: Bernstein Symposium 2008
Place of Event: München, Germany
Start-/End Date: 2008-10-08 - 2008-10-10

Legal Case

show

Project information

show

Source 1

show
hide
Title: Frontiers in Computational Neuroscience
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 2008 (Conference Abstract: Bernstein Symposium 2008) Sequence Number: - Start / End Page: - Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188