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The Journal of Neuroscience, May 23, 2007, 27(21):5744-5756; doi:10.1523/JNEUROSCI.3985-06.2007

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Behavioral/Systems/Cognitive
Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters

Sophie Denève,¹ Jean-René Duhamel,² and Alexandre Pouget³

¹Group for Neural Theory, Département d'Etude Cognitives, Ecole Normale Supérieure, Collège de France, Centre National de la Recherche Scientifique (CNRS), 75005 Paris, France, ²Institut des Sciences Cognitives, CNRS, 69500 Bron, France, and ³Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627

Correspondence should be addressed to Sophie Denève, Group for Neural Theory, Collège de France, 3, rue d'Ulm, 75005 Paris, France. Email: sophie.deneve{at}ens.fr

Several behavioral experiments suggest that the nervous system uses an internal model of the dynamics of the body to implement a close approximation to a Kalman filter. This filter can be used to perform a variety of tasks nearly optimally, such as predicting the sensory consequence of motor action, integrating sensory and body posture signals, and computing motor commands. We propose that the neural implementation of this Kalman filter involves recurrent basis function networks with attractor dynamics, a kind of architecture that can be readily mapped onto cortical circuits. In such networks, the tuning curves to variables such as arm velocity are remarkably noninvariant in the sense that the amplitude and width of the tuning curves of a given neuron can vary greatly depending on other variables such as the position of the arm or the reliability of the sensory feedback. This property could explain some puzzling properties of tuning curves in the motor and premotor cortex, and it leads to several new predictions.

Key words: sensorimotor integration; Kalman filter; motor control; population code; line attractors; basis functions

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Received April 25, 2006; revised Feb. 21, 2007; accepted Feb. 22, 2007.

Correspondence should be addressed to Sophie Denève, Group for Neural Theory, Collège de France, 3, rue d'Ulm, 75005 Paris, France. Email: sophie.deneve{at}ens.fr

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