Neural circuits display complex activity patterns both spontaneously and when responding
to a stimulus or generating a motor output. How are these two forms of activity related?
We develop a procedure called FORCE learning for modifying synaptic strengths either
external to or within a model neural network to change chaotic spontaneous activity
into a wide variety of desired activity patterns. FORCE learning works even though
the networks we train are spontaneously chaotic and we leave feedback loops intact
and unclamped during learning. Using this approach, we construct networks that produce
a wide variety of complex output patterns, input-output transformations that require
memory, multiple outputs that can be switched by control inputs, and motor patterns
matching human motion capture data. Our results reproduce data on premovement activity
in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid
and powerful modulator of network activity than generally appreciated.