Year: 2020
Type: article
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.
Source: Nature Communications
Authors: Benjamin Cramer, David Stöckel, Markus Kreft, Michael Wibral, Johannes Schemmel, Karlheinz Meier, Viola Priesemann
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,380
Awards: The Human Brain Project, G1124005961, G177484282, G1953887145, G2569219052, G3207356259, Brain-inspired multiscale computation in neuromorphic hybrid systems, G393177772, Human Brain Project Specific Grant Agreement 1, G434337789, G4937468798, G5193558347, G5567694422, G5702163051, G570796765, Human Brain Project Specific Grant Agreement 2, Novel Brain-Inspired Learning Paradigms for Large-Scale Neuronal Networks, G6133111771, G6607577419
OpenAlex ID: W2974181993