An active dendritic tree can mitigate fan-in limitations in superconducting neurons

TL;DR

This paper demonstrates that incorporating an active dendritic tree in superconducting neurons can reduce the number of active synapses needed for firing, enhancing computational efficiency and enabling sparse input spiking.

Contribution

It introduces a model where active dendritic trees based on SQUIDs mitigate fan-in limitations in superconducting neurons, improving their computational capabilities.

Findings

Active dendritic trees reduce required synapse activity for neuron firing.

Dendritic structures enhance the computational power of superconducting neurons.

Sparse input activity can effectively trigger neuron spikes with dendritic support.

Abstract

Superconducting electronic circuits have much to offer with regard to neuromorphic hardware. Superconducting quantum interference devices (SQUIDs) can serve as an active element to perform the thresholding operation of a neuron's soma. However, a SQUID has a response function that is periodic in the applied signal. We show theoretically that if one restricts the total input to a SQUID to maintain a monotonically increasing response, a large fraction of synapses must be active to drive a neuron to threshold. We then demonstrate that an active dendritic tree (also based on SQUIDs) can significantly reduce the fraction of synapses that must be active to drive the neuron to threshold. In this context, the inclusion of a dendritic tree provides the dual benefits of enhancing the computational abilities of each neuron and allowing the neuron to spike with sparse input activity.