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      Generating Coherent Patterns of Activity from Chaotic Neural Networks

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      Neuron
      Elsevier BV

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          Abstract

          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.

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          Author and article information

          Journal
          Neuron
          Neuron
          Elsevier BV
          08966273
          August 2009
          August 2009
          : 63
          : 4
          : 544-557
          Article
          10.1016/j.neuron.2009.07.018
          2756108
          19709635
          1d44750a-1188-4fd5-a72c-61b434d069d9
          © 2009

          https://www.elsevier.com/tdm/userlicense/1.0/

          https://www.elsevier.com/open-access/userlicense/1.0/


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