Neuromorphic self-organizing map design for classification of bioelectric-timescale signals

The Self-Organizing Map (SOM) is a recurrent neural network topology that realizes competitive learning for the unsupervised classification of data. In this paper, we investigate the design of a spiking neural network-based SOM for the classification of bioelectric-timescale signals. We present novel insights into the architectural design space, inherent trade-offs, and the critical requirements for designing and configuring neurons, synapses and learning rules to achieve stable and accurate behaviour. We perform this exploration using high-level architectural simulations and, additionally, through the full-custom implementation of components.