Stimulus-dependent variability and noise correlations in cortical MT neurons.
Proc Natl Acad Sci U S A
; 110(32): 13162-7, 2013 Aug 06.
Article
en En
| MEDLINE
| ID: mdl-23878209
Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Lóbulo Temporal
/
Algoritmos
/
Potenciales de Acción
/
Modelos Neurológicos
/
Neuronas
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2013
Tipo del documento:
Article
País de afiliación:
España
Pais de publicación:
Estados Unidos