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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)860-880
JournalProceedings of the IEEE
Issue number5
Publication statusPublished - 2014

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)


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