| Jahr | 2013 |
| Autor(en) | Mihai A. Petrovici*, Johannes Bill*, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier |
| Titel | Stochastic inference with deterministic spiking neurons |
| KIP-Nummer | HD-KIP 13-119 |
| KIP-Gruppe(n) | F9 |
| Dokumentart | Paper |
| Keywords (angezeigt) | stochastic inference, neural sampling, leaky integrate-and-fire neurons, high-conductance state |
| Quelle | arXiv:1311.3211 |
| Abstract (en) | The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic leaky integrate-and-fire neurons embedded in a spiking noisy environment can attain the correct firing statistics in order to sample from a well-defined target distribution. We provide an analytical derivation of the activation function on the single cell level; for recurrent networks, we examine convergence towards stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level. |
| bibtex | @article{petrovici2013stochastic,
author = {Mihai A. Petrovici*, Johannes Bill*, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier},
title = {Stochastic inference with deterministic spiking neurons},
journal = {arXiv},
year = {2013},
volume = {},
pages = {},
url = {http://arxiv.org/abs/1311.3211}
} |
| Datei | Full Paper |
| URL | Online Article |
