An organic synaptic circuit: toward flexible and biocompatible organic neuromorphic processing. Issue 3 (1st September 2022)

• Record Type: Journal Article
• Authors: Mirshojaeian Hosseini, Mohammad Javad; Yang, Yi; Prendergast, Aidan J; Donati, Elisa; Faezipour, Miad; Indiveri, Giacomo; Nawrocki, Robert A
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 3(2022)
• Publication Date: 2022-09-01
• Subjects: spiking neuromorphics -- organic neuromorphics -- flexible organic synaptic circuit -- differential-pair integrator synapse -- biologically plausible time constant; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac830c
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: In the nervous system synapses play a critical role in computation. In neuromorphic systems, biologically inspired hardware implementations of spiking neural networks, electronic synaptic circuits pass signals between silicon neurons by integrating pre-synaptic voltage pulses and converting them into post-synaptic currents, which are scaled by the synaptic weight parameter. The overwhelming majority of neuromorphic systems are implemented using inorganic, mainly silicon, technology. As such, they are physically rigid, require expensive fabrication equipment and high fabrication temperatures, are limited to small-area fabrication, and are difficult to interface with biological tissue. Organic electronics are based on electronic properties of carbon-based molecules and polymers and offer benefits including physical flexibility, low cost, low temperature, and large-area fabrication, as well as biocompatibility, all unavailable to inorganic electronics. Here, we demonstrate an organic differential-pair integrator synaptic circuit, a biologically realistic synapse model, implemented using physically flexible complementary organic electronics. The synapse is shown to convert input voltage spikes into output current traces with biologically realistic time scales. We characterize circuit's responses based on various synaptic parameters, including gain and weighting voltages, time-constant, synaptic capacitance, and circuit response due to inputs of different frequencies. Time constants comparable to those of biological synapses and the neurons are critical in processing real-world sensory signals such as speech, or bio-signals measured from the body. For processing even slower signals, e.g., on behavioral time scales, we demonstrate time constants in excess of two seconds, while biologically plausible time constants are achieved by deploying smaller synaptic capacitors. We measure the circuit synaptic response to input voltage spikes and present the circuit response properties using custom-made circuit simulations, which are in good agreement with the measured behavior.