  Populations of neurons display an extraordinary diversity in the behaviors
they affect and display. Machine learning techniques have recently emerged that
allow us to create networks of model neurons that display behaviours of similar
complexity. Here, we demonstrate the direct applicability of one such
technique, the FORCE method, to spiking neural networks. We train these
networks to mimic dynamical systems, classify inputs, and store discrete
sequences that correspond to the notes of a song. Finally, we use FORCE
training to create two biologically motivated model circuits. One is inspired
by the zebra-finch and successfully reproduces songbird singing. The second
network is motivated by the hippocampus and is trained to store and replay a
movie scene. FORCE trained networks reproduce behaviors comparable in
complexity to their inspired circuits and yield information not easily
obtainable with other techniques such as behavioral responses to
pharmacological manipulations and spike timing statistics.
