A biomimetic neural encoder for spiking neural network

TLDR

Spiking neural networks aim to bridge artificial and biological neural networks by using biologically plausible neurons for faster, lower‑energy, event‑driven inference, but hardware encoders that convert stimuli into spike trains are lacking, making conventional transducers inadequate for neuromorphic computing. This work demonstrates a biomimetic device that encodes analog signals into stochastic spike trains using a dual‑gated MoS₂ field‑effect transistor. The dual‑gated MoS₂ FET implements rate‑based, spike‑timing‑based, and spike‑count‑based encoding algorithms to convert input signals into spike trains. The device achieves dynamic range and encoding precision, consumes only 1–5 pJ per spike, and can encode MNIST images in ≈200 timesteps, enabling a trained SNN to reach over 91 % accuracy.

Abstract

Abstract Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing. Here, we demonstrate a biomimetic device based on a dual gated MoS 2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1–5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN.