EMBEDDED NEURAL CONTROLLERS BASED ON SPIKING NEURON MODELS

Abstract: This paper demonstrates, that input patterns can be encoded in the synaptic weights by local Hebbian delay-learning of spiking neurons (SN), where, after learning, the firing time of an output neuron reflects the distance of the evaluated pattern to its learned input pattern thus realizing a kind of RBF behavior. Furthermore, the paper shows, that temporal spike-time coding and Hebbian learning is a viable means for unsupervised computation in a network of SNs, as the network is capable of clustering realistic data. Then, two versions- with and without embedded micro-controllers- of a SNN are implemented for the aforementioned task