Relating Superconducting Optoelectronic Networks to Classical Neurodynamics

TL;DR

This paper extends a phenomenological model of superconducting optoelectronic neural circuits to simplify spike treatment, improving simulation efficiency and conceptual clarity while maintaining accuracy in representing neural dynamics.

Contribution

The authors develop an extended phenomenological model that simplifies spike processing in superconducting optoelectronic neurons, enhancing simulation speed and understanding of neural operations.

Findings

The extended model accurately captures dendritic responses to single and multiple spikes.

It identifies parameter regions where the model performs well or poorly.

The governing equations closely resemble those used in classical neuroscience for leaky-integrator neurons.

Abstract

The circuits comprising superconducting optoelectronic synapses, dendrites, and neurons are described by numerically cumbersome and formally opaque coupled differential equations. Reference 1 showed that a phenomenological model of superconducting loop neurons eliminates the need to solve the Josephson circuit equations that describe synapses and dendrites. The initial goal of the model was to decrease the time required for simulations, yet an additional benefit of the model was increased transparency of the underlying neural circuit operations and conceptual clarity regarding the connection of loop neurons to other physical systems. Whereas the original model simplified the treatment of the Josephson-junction dynamics, essentially by only considering low-pass versions of the dendritic outputs, the model resorted to an awkward treatment of spikes generated by semiconductor transmitter circuits that required explicitly checking for threshold crossings and distinct treatment of time steps wherein somatic threshold is reached. Here we extend that model to simplify the treatment of spikes coming from somas, again making use of the fact that in neural systems the downstream recipients of spike events almost always perform low-pass filtering. We provide comparisons between the first and second phenomenological models, quantifying the accuracy of the additional approximations. We identify regions of circuit parameter space in which the extended model works well and regions where it works poorly. For some circuit parameters it is possible to represent the downstream dendritic response to a single spike as well as coincidences or sequences of spikes, indicating the model is not simply a reduction to rate coding. The governing equations are shown to be nearly identical to those ubiquitous in the neuroscience literature for modeling leaky-integrator dendrites and neurons.