Name
Abstract | ||
|---|---|---|
We propose a new interpretation of spiking neurons as Bayesian integra- tors accumulating evidence over time about events in the external world or the body, and communicating to other neurons their certainties about these events. In this model, spikes signal the occurrence of new infor- mation, i.e. what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic rep- resentation of probabilities. We proceed to develop a theory of Bayesian inference in spiking neural networks, recurrent interactions implement- ing a variant of belief propagation. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-1-4614-7320-6_568-1 | NIPS |
Keywords | Field | DocType |
bayesian inference,belief propagation,spiking neural network | Bayesian inference,Computer science,Random neural network,Integrator,Artificial intelligence,Poisson distribution,Spiking neural network,Machine learning,Bayesian probability,Belief propagation | Conference |
Citations | PageRank | References |
11 | 1.48 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sophie Denève | 1 | 172 | 17.55 |