Computation in Dynamically Bounded Asymmetric Systems
Author(s)Rutishauser, Ueli; Douglas, Rodney J.; Slotine, Jean-Jacques E.
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Previous explanations of computations performed by recurrent networks have focused on symmetrically connected saturating neurons and their convergence toward attractors. Here we analyze the behavior of asymmetrical connected networks of linear threshold neurons, whose positive response is unbounded. We show that, for a wide range of parameters, this asymmetry brings interesting and computationally useful dynamical properties. When driven by input, the network explores potential solutions through highly unstable ‘expansion’ dynamics. This expansion is steered and constrained by negative divergence of the dynamics, which ensures that the dimensionality of the solution space continues to reduce until an acceptable solution manifold is reached. Then the system contracts stably on this manifold towards its final solution trajectory. The unstable positive feedback and cross inhibition that underlie expansion and divergence are common motifs in molecular and neuronal networks. Therefore we propose that very simple organizational constraints that combine these motifs can lead to spontaneous computation and so to the spontaneous modification of entropy that is characteristic of living systems.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Nonlinear Systems Laboratory
PLOS Computational Biology
Public Library of Science
Rutishauser, Ueli, Jean-Jacques Slotine, and Rodney Douglas. “Computation in Dynamically Bounded Asymmetric Systems.” Edited by Olaf Sporns. PLoS Comput Biol 11, no. 1 (January 24, 2015): e1004039.
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