Measuring Fisher Information Accurately in Correlated Neural Populations

Ingmar Kanitscheider, Ruben Coen Cagli, Adam Kohn, Alexandre Pouget

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Neural responses are known to be variable. In order to understand how this neural variability constrains behavioral performance, we need to be able to measure the reliability with which a sensory stimulus is encoded in a given population. However, such measures are challenging for two reasons: First, they must take into account noise correlations which can have a large influence on reliability. Second, they need to be as efficient as possible, since the number of trials available in a set of neural recording is usually limited by experimental constraints. Traditionally, cross-validated decoding has been used as a reliability measure, but it only provides a lower bound on reliability and underestimates reliability substantially in small datasets. We show that, if the number of trials per condition is larger than the number of neurons, there is an alternative, direct estimate of reliability which consistently leads to smaller errors and is much faster to compute. The superior performance of the direct estimator is evident both for simulated data and for neuronal population recordings from macaque primary visual cortex. Furthermore we propose generalizations of the direct estimator which measure changes in stimulus encoding across conditions and the impact of correlations on encoding and decoding, typically denoted by Ishuffle and Idiag respectively.

Original languageEnglish (US)
Article numbere1004218
JournalPLoS Computational Biology
Volume11
Issue number6
DOIs
StatePublished - Jun 1 2015

Fingerprint

Fisher Information
Macaca
Visual Cortex
Population
Noise
Neurons
Decoding
Encoding
Estimator
measuring
trial
Neuron
neurons
Lower bound
Alternatives
Estimate
Datasets

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

Cite this

Measuring Fisher Information Accurately in Correlated Neural Populations. / Kanitscheider, Ingmar; Coen Cagli, Ruben; Kohn, Adam; Pouget, Alexandre.

In: PLoS Computational Biology, Vol. 11, No. 6, e1004218, 01.06.2015.

Research output: Contribution to journalArticle

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