Generating Coherent Patterns of Activity from Chaotic Neural Networks

Department of Neuroscience, Department of Physiology and Cellular Biophysics, Columbia University College of Physicians and Surgeons, New York, NY 10032-2695, USA.
Neuron (Impact Factor: 15.05). 09/2009; 63(4):544-57. DOI: 10.1016/j.neuron.2009.07.018
Source: PubMed


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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Available from: David Sussillo, Mar 28, 2014
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    • "With the development of neural simulators, neural modeling contributes to the advance in computer science research including the fields of artificial intelligence, computer vision, robotics, and data mining. The computational capability of brain-style computing is actively being investigated by several projects (Eliasmith et al., 2012; Furber et al., 2014) and new algorithms inspired by the principle of neural computation are being developed (Gütig and Sompolinsky, 2006; Sussillo and Abbott, 2009; Schmidhuber, 2014). "
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