Tracking Changing Stimuli in Continuous Attractor Neural Networks
Fung, C. C. Alan
Wong, Michael K.Y.
|Source||Advances in Neural Information Processing Systems. 21 : 22nd Annual Conference on Neural Information Processing Systems 2008, December 8-10, 2008, Vancouver, B.C., Canada , / Edited by D. Koller. La Jolla, Calif. :Neural Information Processing Systems ; Printed from e-media with permission by Curran Associates, Inc, c2009|
|Summary||Continuous attractor neural networks (CANNs) are emerging as promising models for describing the encoding of continuous stimuli in neural systems. Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of neutrally stable states. In this study, we systematically explore how neutral stability of a CANN facilitates its tracking performance, a capacity believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.|
|Rights||The final version of this article has been published in Advances in Neural Information Processing Systems, v. 21, published by The MIT Press|
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