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Driving neural oscillations with correlated spatial input and topographic feedback

Axel Hutt 1 Connie Sutherland 2 André Longtin 2
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We consider how oscillatory activity in networks of excitable systems depends on spatial correlations of random inputs and the spatial range of feedback coupling. Analysis of a neural field model with topographic delayed recurrent feedback reveals how oscillations in certain frequency bands, including the gamma band, are enhanced by increases in the input correlation length. Further, the enhancement is maximal when this length exceeds the feedback coupling range. Suppression of oscillatory power occurs concomitantly in other bands. These effects depend solely on the ratio of input and feedback length scales. The precise positions of these bands are determined by the synaptic constants and the delays. The results agree with numerical simulations of the model and of a network of stochastic spiking neurons, and are expected for any noise-driven excitable element networks.
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https://hal.inria.fr/inria-00332994
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Submitted on : Wednesday, October 22, 2008 - 11:07:48 AM
Last modification on : Thursday, September 2, 2021 - 8:14:03 AM

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Axel Hutt, Connie Sutherland, André Longtin. Driving neural oscillations with correlated spatial input and topographic feedback. Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, American Physical Society, 2008, 78 (2), pp.021911-1--021911-7. ⟨10.1103/PhysRevE.78.021911⟩. ⟨inria-00332994⟩

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