Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

Wolfgang Maass
2014 Proceedings of the IEEE  
This paper discusses biologically inspired machine learning methods based on theories about how the brain exploits noise to carry out computations, such as probabilistic inference through sampling. 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
more » ... its 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.
doi:10.1109/jproc.2014.2310593 fatcat:54mgt3scqje5flvjqnad45okfi