A review of methods for identifying stochastic resonance in simulations of single neuron models.
Network
; 26(2): 35-71, 2015.
Article
em En
| MEDLINE
| ID: mdl-25760433
ABSTRACT
Stochastic resonance (SR) is said to be observed when the presence of noise in a nonlinear system enables an output signal from the system to better represent some feature of an input signal than it does in the absence of noise. The effect has been observed in models of individual neurons, and in experiments performed on real neural systems. Despite the ubiquity of biophysical sources of stochastic noise in the nervous system, however, it has not yet been established whether neuronal computation mechanisms involved in performance of specific functions such as perception or learning might exploit such noise as an integral component, such that removal of the noise would diminish performance of these functions. In this paper we revisit the methods used to demonstrate stochastic resonance in models of single neurons. This includes a previously unreported observation in a multicompartmental model of a CA1-pyramidal cell. We also discuss, as a contrast to these classical studies, a form of 'stochastic facilitation', known as inverse stochastic resonance. We draw on the reviewed examples to argue why new approaches to studying 'stochastic facilitation' in neural systems need to be developed.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Processos Estocásticos
/
Modelos Neurológicos
/
Neurônios
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article