Stochastic inference with spiking neurons in the high-conductance state

TL;DR

This paper demonstrates how high-conductance states in spiking neuron models enable stochastic inference, linking deterministic neuron responses to probabilistic computation at the network level.

Contribution

It provides an analytical derivation of the neural activation function in high-conductance states and shows how recurrent networks perform Bayesian inference through sampling.

Findings

Neural activation functions derived analytically for high-conductance states.

Recurrent networks can perform sample-based Bayesian inference.

High-conductance states facilitate stochastic inference in deterministic neuron models.

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.