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Cellular automata can classify data by inducing trajectory phase coexistence
Stephen Whitelam1, Isaac Tamblyn2,3
1Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA.
Physical Review. E
|August 16, 2023
Summary
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.
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.
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