Hyper-Flexible Neural Networks: Rapidly Switching between Logic Operations in a Compact 4-Neuron Circuit

Abstract

Biological neural circuits at various levels exhibit rapid adaptability to diverse environmental stimuli. Such fast response times imply that adaptation cannot rely solely on synaptic plasticity, which operates on a much slower timescale. Instead, circuits must be inherently hyper-flexible and receptive to switches in functionalities without changes in network structure. This biological flexibility is a fruitful mechanism for constructing artificial reconfigurable circuits, whether they are spiking or non-spiking. In this study, we demonstrate that a 4-neuron circuit can rapidly and controllably switch between 24 unique logical functions while maintaining the same set of synaptic weights. Moreover, we show that this reconfigurability works for several different underlying neuronal architectures and strikingly can be applied to a network composed of any sigmoid-shaped activation function. We conclude with proof-of-concept applications showing that we can perform standard tasks such as a full-adder, as well as event-based conditional computing, such as detecting unexpected motion.