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Populations of neurons display an extraordinary diversity in the behaviorsthey affect and display. Machine learning techniques have recently emerged thatallow us to create networks of model neurons that display behaviours of similarcomplexity. Here, we demonstrate the direct applicability of one suchtechnique, the FORCE method, to spiking neural networks. We train thesenetworks to mimic dynamical systems, classify inputs, and store discretesequences that correspond to the notes of a song. Finally, we use FORCEtraining to create two biologically motivated model circuits. One is inspiredby the zebra-finch and successfully reproduces songbird singing. The secondnetwork is motivated by the hippocampus and is trained to store and replay amovie scene. FORCE trained networks reproduce behaviors comparable incomplexity to their inspired circuits and yield information not easilyobtainable with other techniques such as behavioral responses topharmacological manipulations and spike timing statistics.

Supervised Learning in Spiking Neural Networks with FORCE Training