Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 20 (1), 118-45

Bayesian Spiking Neurons II: Learning

Affiliations

Bayesian Spiking Neurons II: Learning

Sophie Deneve. Neural Comput.

Abstract

In the companion letter in this issue ("Bayesian Spiking Neurons I: Inference"), we showed that the dynamics of spiking neurons can be interpreted as a form of Bayesian integration, accumulating evidence over time about events in the external world or the body. We proceed to develop a theory of Bayesian learning in spiking neural networks, where the neurons learn to recognize temporal dynamics of their synaptic inputs. Meanwhile, successive layers of neurons learn hierarchical causal models for the sensory input. The corresponding learning rule is local, spike-time dependent, and highly nonlinear. This approach provides a principled description of spiking and plasticity rules maximizing information transfer, while limiting the number of costly spikes, between successive layers of neurons.

Similar articles

See all similar articles

Cited by 19 articles

See all "Cited by" articles

Publication types

LinkOut - more resources

Feedback