Spiking Neuron Implementation Using a Novel Floating Memcapacitor Emulator

Abstract

Memcapacitors (MCs) are promising candidates for the future design of low-power integrated neuromorphic computing systems, with particular emphasis on dynamical spiking neuron models that exhibit rich temporal behaviors. We present a novel floating flux-controlled MC that is designed using only three current feedback amplifiers, one analog multiplier, one capacitor and one resistor. Compared with existing floating MC emulators, our proposed design has a simpler structure without the need for DC biasing voltage sources, and can operate at higher working frequencies, and therefore enabling rapid prototyping of applied MC circuits for experimental verification of large-scale MC arrays. The consistency of the theoretical analysis, simulation and experimental results confirms the correctness and practicability of this new memcapacitor emulator. To further demonstrate a potential use of our MC, in this work, we apply the MC as the first parameterizable leaky integrator for spiking neuron through simulation and experiments. The intrinsic tunable capacitance of the MC can bring about novel short-term memory dynamics to neuronal circuits by dynamically modifying the membrane time constant on-the-fly, which ultimately resembles long-term potentiation, and can thus offer longer term memory. Our results highlight the potential for integrating heterogeneous spiking neural networks with richer temporal dynamics that rely on MC-based circuits to further the capability of neuromorphic computing.