Control of criticality and computation in spiking neuromorphic networks with plasticity.

ABSTRACT: The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a plastic spiking network on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, only the complex tasks profit from criticality, whereas sim

SUBMITTER: Cramer B 

PROVIDER: S-EPMC7275091 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature