Computational identification of receptive fields.
Annu Rev Neurosci
; 36: 103-20, 2013 Jul 08.
Article
em En
| MEDLINE
| ID: mdl-23841838
ABSTRACT
Natural stimuli elicit robust responses of neurons throughout sensory pathways, and therefore their use provides unique opportunities for understanding sensory coding. This review describes statistical methods that can be used to characterize neural feature selectivity, focusing on the case of natural stimuli. First, we discuss how such classic methods as reverse correlation/spike-triggered average and spike-triggered covariance can be generalized for use with natural stimuli to find the multiple relevant stimulus features that affect the responses of a given neuron. Second, ways to characterize neural feature selectivity while assuming that the neural responses exhibit a certain type of invariance, such as position invariance for visual neurons, are discussed. Finally, we discuss methods that do not require one to make an assumption of invariance and instead can determine the type of invariance by analyzing relationships between the multiple stimulus features that affect the neural responses.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Campos Visuais
/
Modelos Neurológicos
/
Neurônios
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article