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

Neural spikes can encode a rich set of information, ranging from the perceived intensity of light sources to the likelihood associated with decisions made in the cortex. Among these capabilities, previous studies demonstrated that spikes can also encode in their activity multiple frequencies at the same time, such as those generated by skin vibrations during textures scanning. However, the mechanism responsible for decoding spikes containing multiple frequencies is yet to be uncovered. In this paper, we introduce a novel spiking neural network model tailored for frequency decomposition of spike trains. Our model mimics neural microcircuits hypothesized in the somatosensory cortex, making it a biologically plausible candidate for decoding spike trains observed in tactile peripheral nerves. We showcase the ability of simple neurons and synapses to replicate the functionality of a phase-locked loop (PLL) and delve into the emergent properties when multiple spiking phase-locked loops (sPLLs) interact with diverse inputs. Furthermore, we demonstrate how these sPLLs can decode textures by leveraging the spectral features of spike trains generated in peripheral nerves. By harnessing our model's frequency decomposition capabilities, we achieve significant performance enhancements over state-of-the-art approaches on a Multifrequency Spike Train (MST) dataset. Our findings underscore the potential of sPLLs in elucidating the mechanisms behind texture decoding in the brain, while also showcasing their potential to outperform conventional SNNs in handling spike trains with multiple frequencies. We believe this study sheds light into the neuronal mechanisms behind texture decoding, while presenting a practical framework for augmenting the capabilities of artificial neural networks in intricate pattern recognition tasks.