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Computation in dynamically bounded asymmetric systems.

Ueli Rutishauser1, Jean-Jacques Slotine2, Rodney Douglas3

  • 1Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America; Departments of Neurosurgery, Neurology and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.

Plos Computational Biology
|January 25, 2015
PubMed
Summary
This summary is machine-generated.

Asymmetrical recurrent neural networks with linear threshold neurons exhibit unique dynamics for computation. This instability and divergence allow networks to efficiently find solutions, mimicking biological systems.

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Area of Science:

  • Computational neuroscience
  • Dynamical systems theory
  • Artificial intelligence

Background:

  • Recurrent network computation traditionally focused on symmetric connections and saturating neurons.
  • Previous models often explained computation through convergence to attractors.

Purpose of the Study:

  • To analyze the computational properties of asymmetrical networks of linear threshold neurons.
  • To explore the role of unstable 'expansion' dynamics in solution finding.

Main Methods:

  • Analysis of asymmetrical networks with linear threshold neurons.
  • Investigating dynamics driven by input, including expansion and divergence.
  • Examining the role of positive feedback and cross-inhibition.

Main Results:

  • Asymmetry in networks leads to computationally useful, unstable 'expansion' dynamics.
  • Negative divergence steers the system towards a reduced solution space.
  • Stable contraction on a solution manifold is achieved after initial expansion.

Conclusions:

  • Asymmetrical networks offer novel computational capabilities beyond traditional models.
  • The proposed dynamics, common in biological networks, can drive spontaneous computation.
  • These principles may explain spontaneous entropy modification in living systems.