KIP publications

year 2016
author(s) Mihai A. Petrovici*, Johannes Bill*, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
title Stochastic inference with spiking neurons in the high-conductance state
KIP-Nummer HD-KIP 16-81
KIP-Gruppe(n) F9
document type Paper
Keywords (shown) Bayesian inference, spiking neurons, high-conductance state, activation function, autocorrelation propagation
source Physical Review E 94, 042312 (2016)
doi 10.1103/PhysRevE.94.042312
Abstract (en)

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.

  author   = {Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier},
  title    = {Stochastic inference with spiking neurons in the high-conductance state},
  journal  = {Physical Review E},
  year     = {2016},
  volume   = {94},
  number   = {4},
  pages    = {},
  month    = {October},
  doi      = {10.1103/PhysRevE.94.042312},
  url      = {}
URL Online article
URL arXiv
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