A Hardware-friendly Neuromorphic Spiking Neural Network for Frequency Detection and Fine Texture Decoding

Humans can distinguish fabrics by their textures, even when they are finer than the density of tactile sensors. Evidence suggests that this ability is produced by the nervous system using an active touch strategy. When the finger slides over a texture, the nervous system converts the texture's spatial period into an equivalent spiking frequency. Many studies focused on modeling the biological encoding part that translates the spatial frequency into a temporal spiking frequency, but few explored the decoding part. In this work, we propose a novel approach based on a spiking neural network able to detect the frequency of an input signal. Inspired by biological evidence, our architecture detects the range in which the encoded frequency dwells and could therefore decode the texture's spatial period. The network has been designed to be composed of existing neuromorphic spiking primitives. This property enables a straightforward implementation on integrated silicon circuits, allowing the texture decoding at the edge of the sensor.