Antidromic spikes drive Hebbian learning in an artificial dendritic tree

Hebbian learning using the timing between pre-synaptic and post-synaptic sp iking allows a network of silicon neuromorphs to learn and playback complex spatiotemporal input patterns. Learning occurred dynamically and in a stim ulus dependent manner by potentiating active synapses that contributed to p ost-synaptic spike production and depressing active synapses that were anti -causal. Active synapses that were neither causal nor anticausal remained a t their pre-activated efficacy. The network used to evaluate hebbian synapt ic plasticity was fully connected with each neuromorph making a prescribed number of connections to the dendrites of all the other neuromorphs. To ena ble learning of spatiotemporal spiking activity, efferents from each neurom orph had to make connections along the entire length of their target dendri tes so as to produce a temporally distributed response. Upon repetitive pre sentation of an input pattern those synapses that had appropriate causal ti ming were strengthened while those that were anti-causal were depressed.