Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Abstract: In this paper, we present a novel spiking neural network model designed to perform frequency decomposition of spike trains. Our model emulates neural microcircuits theorized in the somatosensory cortex, rendering it a biologically plausible candidate for decoding the spike trains observed in tactile peripheral nerves. We demonstrate the capacity of simple neurons and synapses to replicate the phase-locked loop (PLL) and explore the emergent properties when considering multiple spiking phase-locked loops (sPLLs) with diverse oscillations. We illustrate how these sPLLs can decode textures using the spectral features elicited in peripheral nerves. Leveraging our model's frequency decomposition abilities, we improve state-of-the-art performances on a Multifrequency Spike Train (MST) dataset. This work offers valuable insights into neural processing and presents a practical framework for enhancing artificial neural network capabilities in complex pattern recognition tasks.