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.Comment: 5 pages, 7 figures, to be published in IEEE BioCAS 2023 Proceeding