TY - JOUR
T1 - Measuring Fisher Information Accurately in Correlated Neural Populations
AU - Kanitscheider, Ingmar
AU - Coen-Cagli, Ruben
AU - Kohn, Adam
AU - Pouget, Alexandre
N1 - Funding Information:
AP was supported by a grant from the Swiss National Science Foundation (31003A_143707) and a grant from the Simons Foundation. RCC was supported by a fellowship from the Swiss National Science Foundation (PAIBA3- 145045). AK was supported by a grant from the NIH (EY016774). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1004218
DO - 10.1371/journal.pcbi.1004218
M3 - Article
C2 - 26030735
AN - SCOPUS:84953259398
VL - 11
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 6
M1 - e1004218
ER -