Learning from biological neurons to compute with electronic noise special

Biological neurons seem able to compute with noise reliably, or even to use noise to achieve probabilistic inference. This paper introduces two neuro-inspired algorithms and their implementation in the Very Large Scale Integration (VLSI). By generalising data variability with noise, the algorithms are able to classify noisy data more reliably. The VLSI implementation further demonstrates the feasibility of utilising electronic noise for stochastic computation. To exploit the intrinsic noise of transistors for computation, two transistors with enhanced and adaptable noise are further developed and modelled. These technologies would allow us to compute with noisy devices just like how the brain computes with noisy neurons.