Control of criticality and computation in spiking neuromorphic networks with plasticity

Benjamin Cramer (), David Stöckel, Markus Kreft, Michael Wibral, Johannes Schemmel, Karlheinz Meier and Viola Priesemann ()
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Benjamin Cramer: Heidelberg University
David Stöckel: Heidelberg University
Markus Kreft: Heidelberg University
Michael Wibral: Georg-August University
Johannes Schemmel: Heidelberg University
Karlheinz Meier: Heidelberg University
Viola Priesemann: Max-Planck-Institute for Dynamics and Self-Organization

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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 simple tasks suffer. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16548-3

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DOI: 10.1038/s41467-020-16548-3

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