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 sponta-
neously 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 transforma-
tions 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 plas-
ticity may be a more rapid and powerful modulator of
network activity than generally appreciated.
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