On the maximization of information flow between spiking neurons.
Journal Article (Journal Article)
A feedforward spiking network represents a nonlinear transformation that maps a set of input spikes to a set of output spikes. This mapping transforms the joint probability distribution of incoming spikes into a joint distribution of output spikes. We present an algorithm for synaptic adaptation that aims to maximize the entropy of this output distribution, thereby creating a model for the joint distribution of the incoming point processes. The learning rule that is derived depends on the precise pre- and postsynaptic spike timings. When trained on correlated spike trains, the network learns to extract independent spike trains, thereby uncovering the underlying statistical structure and creating a more efficient representation of the incoming spike trains.
Full Text
Duke Authors
Cited Authors
- Parra, LC; Beck, JM; Bell, AJ
Published Date
- November 2009
Published In
Volume / Issue
- 21 / 11
Start / End Page
- 2991 - 3009
PubMed ID
- 19635018
International Standard Serial Number (ISSN)
- 0899-7667
Digital Object Identifier (DOI)
- 10.1162/neco.2009.04-06-184
Language
- eng
Conference Location
- United States