Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate

TL;DR

This paper presents a neuromorphic framework enabling recurrent spiking neural networks to switch seamlessly between analog signal processing and digital computation, supported by theoretical analysis and hardware experiments.

Contribution

It introduces a novel approach for hybrid analog-digital computation in neuromorphic hardware based on principles inspired by cortical microcircuits.

Findings

Framework effectively switches between analog and digital modes

Hardware implementation demonstrates robustness and configurability

Theoretical conditions for switching are formally derived

Abstract

Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of canonical cortical microcircuits, we propose a framework for using recurrent spiking neural networks to seamlessly and robustly switch between analog signal processing and categorical and discrete computation. We provide theoretical analysis and practical neural network design tools to formally determine the conditions for inducing this switch. We demonstrate the robustness of this framework experimentally with hardware soft Winner-Take-All and mixed-feedback recurrent spiking neural networks, implemented by appropriately configuring the analog neuron and synapse circuits of a mixed-signal neuromorphic processor chip.