Implementation of a Minimal Recurrent Spiking Neural Network in a Solid-State Device

We study the minimal recurrent spiking neural network of a single neuron with an autaptic synapse. We implement the neural system in the solid state with a recently introduced ultracompact neuron (UCN) model, which is based on the memristive properties of a thyristor. The UCN is supplemented by a self-synaptic, autaptic, connection, where we control the feedback. Both excitatory and inhibitory cases are considered. We explore the systematic behavior as a function of autaptic intensity and feedback time delay. We realize a tunable dynamic memory, showing graded persistent activity, where short excitatory and inhibitory pulses allow the firing rate to be controlled. We finally reproduce recent experimentally observed behavior of a biological autapse measured in vivo, finding excellent qualitative agreement. Our work opens the way into the field of solid-state neuroscience, with the UCN as an accessible platform to implement and experimentally study the dynamic behavior of spiking neural networks.

Automated summary

This study investigates the minimal recurrent spiking neural network of a single neuron with an autaptic synapse, implemented in solid state using an ultracompact neuron model based on the memristive properties of a thyristor. By controlling feedback, they explore the systematic behavior and dynamic memory of the network, replicating experimentally observed behavior of biological autapse. This work lays the foundation for solid-state neuroscience research using the ultracompact neuron as a platform.