Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures

TL;DR

This paper introduces a biologically-inspired method using inhibition and adaptation mechanisms in spiking neural networks to effectively compress and classify high-dynamic-range biosignals, addressing the challenge of encoding such signals without saturation.

Contribution

It proposes a novel combination of spike-frequency adaptation, feed-forward inhibition, and recurrent inhibition to handle high dynamic range biosignals in SNNs, validated through gesture classification tasks.

Findings

Effective compression of high-dynamic-range biosignals in SNNs.

Successful gesture classification from surface EMG recordings.

Validation through in silico simulations demonstrating improved encoding.

Abstract

Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals. As in biology, to minimize power consumption, the silicon neurons' circuits are configured to fire with a limited dynamic range and with maximum firing rates restricted to a few tens or hundreds of Herz. However, biosignals can have a very large dynamic range, so encoding them into spikes without saturating the neuron outputs represents an open challenge. In this work, we present a biologically-inspired strategy for compressing this high-dynamic range in SNN architectures, using three adaptation mechanisms ubiquitous in the brain: spike-frequency adaptation at the single neuron level, feed-forward inhibitory connections from neurons belonging to the input layer, and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons in the output layer. We apply this strategy to input biosignals encoded using both an asynchronous delta modulation method and an energy-based pulse-frequency modulation method. We validate this approach in silico, simulating a simple network applied to a gesture classification task from surface EMG recordings.