KIP publications

 
year 2013
author(s) Mihai A. Petrovici*, Johannes Bill*, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
title Stochastic inference with deterministic spiking neurons
KIP-Nummer HD-KIP 13-119
KIP-Gruppe(n) F9
document type Paper
Keywords stochastic inference, neural sampling, leaky integrate-and-fire neurons, high-conductance state
source 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
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