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. |
bibtex | @article{petrovici2016stochastic, 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 = {http://journals.aps.org/pre/abstract/10.1103/PhysRevE.94.042312} } |
URL | Online article |
URL | arXiv |