2019/09/30 by Benjamin Cramer, David Stöckel, Markus Kreft +4
Computer Science · Engineering · Mathematics · #Advanced Memory and Neural Computing #Artificial neural network #Computation #Criticality #Ferroelectric and Negative Capacitance Devices #Models of neural computation #Neural Networks and Reservoir Computing #Neuromorphic engineering #Set (abstract data type) #Spiking neural network #State (computer science) #Task (project management) #cs.ET #stat.CO
paper · pdf · doi:10.1038/s41467-020-16548-3
openalex created_date 2019/09/26 · arxiv created 2020/02/11 · openalex publication_date 2020/06/05 · arxiv updated 2020/11/05 · openalex updated_date 2026/08/05
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.