Modular Modeling of Analog Organic Neuromorphic Circuits: Toward Prototyping of Hardware-Level Spiking Neural Networks

Abstract

This work proposes a novel modeling approach for analog organic circuits using very simple to customize circuit topology and parameters of individual p- and n-type organic field effect transistors (OFETs). Aided with the combination of primitive elements (OFETs, capacitors, resistors), the convoluted behavior of analog organic neuromorphic circuits (ONCs) and even other general analog organic circuits, can be predicted. The organic log-domain integrator (oLDI) synaptic circuit, the organic differential-pair integrator (oDPI) synaptic circuit, and the organic Axon-Hillock (oAH) somatic circuit are designed and serve as the modular circuit primitives of more complicated ONCs. We first validate our modeling approach by comparing the simulated oDPI and oAH circuit responses to their experimental measurements. Thereafter, the summation effects of the excitatory and inhibitory oDPI circuits in prototyped ONCs are investigated. We also predict the dynamic power dissipation of modular ONCs and show an average power consumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.1 \mu \text {J}$ </tex-math></inline-formula> per spike for the oAH soma at a ~1 Hz spiking frequency. Furthermore, we compare our modeling approach with other two representative organic circuit models and prove that our approach outperforms the other two in terms of accuracy and convergence speed.