Stochastic inference with deterministic spiking neurons
This work addresses a foundational problem in computational neuroscience by linking deterministic neuron models to stochastic inference, potentially impacting neural network modeling and brain-inspired computing.
The paper tackled the discrepancy between deterministic neuron models and stochastic network dynamics by showing that deterministic leaky integrate-and-fire neurons in a noisy environment can achieve correct firing statistics to sample from target distributions, with analytical derivations and simulations demonstrating Bayesian inference in mixed graphical models.
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.