Noise as a resource for computation and learning in networks of spiking neurons

W. Maass

Abstract:

We are used to viewing noise as a nuisance in computing systems. This is a pity, since noise will be abundantly available in energy efficient future nanoscale devices and circuits. I propose here to learn from the way the brain deals with noise, and apparently even benefits from it. Recent theoretical results have provided insight into how this can be achieved: how noise enables networks of spiking neurons to carry out probabilistic inference through sampling and also enables creative problem solving. In addition noise supports the self organization of networks of spiking neurons, and learning from rewards. I will sketch here the main ideas and some consequences of these results. I will also describe why these results are paving the way for a qualitative jump in the computational capability and learning performance of neuromorphic networks of spiking neurons with noise, and for other future computing systems that are able to treat noise as a resource. Index Terms-noise, spiking neurons, neural networks, computational power, stochastic computing, self-organization, neuromorphic hardware.

Reference: W. Maass. Noise as a resource for computation and learning in networks of spiking neurons. Special Issue of the Proc. of the IEEE on "Engineering Intelligent Electronic Systems based on Computational Neuroscience", 102(5):860-880, 2014.