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 language | English (US) |
|---|---|
| Article number | e1004218 |
| Journal | PLoS Computational Biology |
| Volume | 11 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2015 |
Fingerprint
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 journal › Article
}
TY - JOUR
T1 - Measuring Fisher Information Accurately in Correlated Neural Populations
AU - Kanitscheider, Ingmar
AU - Coen Cagli, Ruben
AU - Kohn, Adam
AU - Pouget, Alexandre
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.
UR - http://www.scopus.com/inward/record.url?scp=84953259398&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953259398&partnerID=8YFLogxK
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 -