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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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Information processing capacity of dynamical systems.

Joni Dambre1, David Verstraeten, Benjamin Schrauwen

  • 1Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium. Joni.Dambre@ugent.be

Scientific Reports
|July 21, 2012
PubMed
Summary
This summary is machine-generated.

We developed a method to quantify how dynamical systems process information from external signals, defining their computational capacity. This capacity is limited by the system

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Area of Science:

  • Dynamical Systems Theory
  • Information Theory
  • Computational Neuroscience

Background:

  • Dynamical systems process time-dependent external signals.
  • Understanding information processing modes is crucial for system analysis.

Purpose of the Study:

  • To quantify information processing modes in dynamical systems.
  • To define and bound the computational capacity of these systems.

Main Methods:

  • Developed a theoretical framework combining machine learning, system modeling, stochastic processes, and functional analysis.
  • Quantified information processing modes and computational capacity.
  • Applied numerical simulations to diverse systems.

Main Results:

  • Defined computational capacity based on linearly independent state variables and fading memory.
  • Demonstrated capacity equals the number of linearly independent functions of stimuli computable by the system.
  • Revealed universal trade-offs between computation non-linearity and short-term memory.

Conclusions:

  • The proposed theory provides a unified approach to understanding information processing in dynamical systems.
  • Computational capacity is a fundamental property bounded by system complexity.
  • Trade-offs exist between non-linear computation and memory in information processing.