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Cellular automata classify data by creating dynamical phase coexistence. This research identifies automata that act like nonlinear activation functions, mimicking spiking neurons for binary output.

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Area of Science:

  • Computational science
  • Complex systems

Background:

  • Cellular automata (CA) are discrete dynamical systems with simple rules.
  • Image classification is a fundamental task in machine learning and computer vision.

Purpose of the Study:

  • To explore the potential of cellular automata for data classification.
  • To investigate the use of dynamical phase coexistence for image recognition.

Main Methods:

  • Utilized Monte Carlo methods to search for suitable two-dimensional deterministic cellular automata.
  • Defined image classification based on 'activity'—the number of state changes in a CA trajectory.
  • Treated the number of time steps as a trainable parameter.

Main Results:

  • Identified cellular automata capable of classifying images based on initial conditions.
  • Discovered automata that generate distinct populations of high and low activity dynamical trajectories.
  • Observed that these automata function as nonlinear activation functions with binary output.

Conclusions:

  • Cellular automata can perform data classification through induced dynamical phase coexistence.
  • The identified automata exhibit emergent properties analogous to spiking neurons.
  • This work presents a novel approach to computation and machine learning using cellular automata.