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Neuromorphic processors that implement Spiking Neural Networks (SNNs) usingmixed-signal analog/digital circuits represent a promising technology forclosed-loop real-time processing of biosignals. As in biology, to minimizepower consumption, the silicon neurons' circuits are configured to fire with alimited dynamic range and with maximum firing rates restricted to a few tens orhundreds of Herz.  However, biosignals can have a very large dynamic range, so encoding theminto spikes without saturating the neuron outputs represents an open challenge.  In this work, we present a biologically-inspired strategy for compressingthis high-dynamic range in SNN architectures, using three adaptation mechanismsubiquitous 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 neuronsin the output layer.  We apply this strategy to input biosignals encoded using both an asynchronousdelta modulation method and an energy-based pulse-frequency modulation method.  We validate this approach in silico, simulating a simple network applied to agesture classification task from surface EMG recordings.

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