Leaky Integrate-and-Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal-Coding

Spiking neural networks (SNNs) employ discrete spikes that mimic the firing of neurons in biological systems to process and transmit information. This characteristic enables SNNs to effectively capture temporal dynamics and capitalize on the time information inherent in time-varying inputs, such as motion, audio/video streams, and other sequential data. Currently, most hardware implementations of SNNs are designed to use rate-coding, where information is encoded in the rate of spikes. However, it still remains challenging for the hardware implementation of temporal coding in SNNs, which allows for higher input sparsity and exploits additional dimensions such as precise spike timing and relative spike timings. This study presents hardware implementations of SNNs constructed by organic electrochemical transistors (OECTs), processing temporal-coded information. The protic dynamics in response to electrical stimuli enable the emulation of temporal integration, reset, and leaking of membrane potential in a simple leaky integrate-and-fire (LIF) neuron circuit. By utilizing these features, the emulated LIF neuron can be employed to construct SNNs capable of processing temporal-coded information in complex tasks including coincidence detection and dynamic handwriting recognition, exhibiting high performance and good tolerance even when dealing with noisy datasets. Hardware-based spiking neural networks (SNNs) constructed by organic electrochemical transistors (OECTs) are proposed for the processing of temporal-coded information. Artificial leaky integrate-and-fire (LIF) neurons widely used in SNNs are designed and implemented by utilizing the proton dynamics in dielectric electrolytes and are employed in SNNs for solving complex tasks, including coincidence detection and dynamic handwriting recognition, with temporal-coding method.image