Control of criticality and computation in spiking neuromorphic networks with plasticity

TL;DR

This study investigates how the critical state in spiking neuromorphic networks affects computational performance, revealing that criticality benefits only complex, memory-intensive tasks rather than all types of computation.

Contribution

The paper demonstrates that optimal network performance depends on task complexity, challenging the assumption that criticality universally enhances neural computation.

Findings

Criticality can be adjusted by input strength in neuromorphic networks.

Information capacity peaks at criticality, but task performance varies with complexity.

Complex tasks benefit from criticality, simple tasks do not.

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 spiking network with synaptic plasticity 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, this is not the case for performance on specific tasks: Only the complex, memory-intensive tasks profit from criticality, whereas the simple tasks suffer from it. 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 to achieve optimal performance.