Stochastic inference with spiking neurons in the high-conductance state

Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, and Karlheinz Meier
Phys. Rev. E 94, 042312 – Published 20 October 2016

Abstract

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

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  • Received 11 November 2013
  • Revised 2 July 2016

DOI:https://doi.org/10.1103/PhysRevE.94.042312

©2016 American Physical Society

Physics Subject Headings (PhySH)

NetworksPhysics of Living Systems

Authors & Affiliations

Mihai A. Petrovici1,*, Johannes Bill1,2, Ilja Bytschok1, Johannes Schemmel1, and Karlheinz Meier1

  • 1Kirchhoff Institute for Physics, University of Heidelberg, Heidelberg, Germany
  • 2Institute for Theoretical Computer Science, Graz University of Technology, Styria, Austria

  • *Corresponding author: mpedro@kip.uni-heidelberg.de

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Issue

Vol. 94, Iss. 4 — October 2016

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