Stochastic Spiking Neural Networks

One ongoing challenge in brain-inspired (neuromorphic) computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. This research topic investigates learning rules that uses modulated membrane-based synaptic plasticity for learning deep representations in brain-inspired (neuromorphic), stochastic computing hardware. Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. We introduced the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic unreliability as a means to stochasticity for sampling.