The population trackingmodel

A simple, scalable statistical model for neural population data

Cian O'Donnell, J. Tiago Goncalves, Nick Whiteley, Carlos Portera-Cailliau, Terrence J. Sejnowski

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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 (~2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semiindependent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate.

Original languageEnglish (US)
Pages (from-to)50-93
Number of pages44
JournalNeural Computation
Volume29
Issue number1
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

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Statistical Models
Population
Neurons
Statistical Model
Neuron

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

Cite this

The population trackingmodel : A simple, scalable statistical model for neural population data. / O'Donnell, Cian; Goncalves, J. Tiago; Whiteley, Nick; Portera-Cailliau, Carlos; Sejnowski, Terrence J.

In: Neural Computation, Vol. 29, No. 1, 01.01.2017, p. 50-93.

Research output: Contribution to journalArticle

O'Donnell, Cian ; Goncalves, J. Tiago ; Whiteley, Nick ; Portera-Cailliau, Carlos ; Sejnowski, Terrence J. / The population trackingmodel : A simple, scalable statistical model for neural population data. In: Neural Computation. 2017 ; Vol. 29, No. 1. pp. 50-93.
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