Learning and inference with correlated neural variability.

ABSTRACT: The abundance of both input and process noises in the brain suggests that stochasticity is an integral part of neural computing, but how spiking neural networks (SNN) can learn general tasks under correlated variability remain unclear. In this work, we propose a stochastic neural computing (SNC) theory to implement gradient-based learning in SNN in the noise-driven regime using a moment closure approach. This leads to a new class of deep learning architecture called the moment neural network (MNN), which naturally generalizes rate-based neural networks to second-order statistical moments. Once trained, the parameters of the MNN can be directly used to recover the corresponding SNN without further fine-tuning. The trained model captures realistic firing statistics of biological neurons, inc

SUBMITTER: Qi Y 

PROVIDER: S-EPMC12512019 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature