Matching recall and storage in sequence learning with spiking neural networks.
J Neurosci
; 33(23): 9565-75, 2013 Jun 05.
Article
en En
| MEDLINE
| ID: mdl-23739954
Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Recuerdo Mental
/
Potenciales de Acción
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Neurosci
Año:
2013
Tipo del documento:
Article
País de afiliación:
Suiza