LAUSR

Analog circuits have proven to be a reliable medium for neuromorphic architectures in silicon, capable of emulating parts of the brain’s computational processes. Information in the brain is shared between neurons in the form of spikes, where a neuron’s soma emits a voltage signal after integrating multiple synaptic input currents. To preserve the quality of this information in a rate-based protocol, it is important that a post-synaptic neuron is able to adapt its firing rate to some desired or expected rate, especially in a noisy environment. This paper presents an analog Izhikevich neuron circuit and a proof-of-concept tuning algorithm that adjusts the neuron’s spike rate using proportional feedback control. The neuron circuit is implemented using discrete surface-mount components on a custom printed circuit board, and its output spike pattern is modified by adjusting one of the neuron’s four voltage parameters. Adjusting the voltage parameters gives a neuromorphic system designer the ability to calibrate these silicon neurons in the presence of noise or component imperfections, ensuring a baseline configuration where all neurons exhibit the same, expected spike response given the same input.