Cellular automata can classify data by inducing trajectory phase coexistence.
Phys Rev E
; 108(1-1): 014126, 2023 Jul.
Article
en En
| MEDLINE
| ID: mdl-37583190
We show that cellular automata can classify data by inducing a form of dynamical phase coexistence. We use Monte Carlo methods to search for general two-dimensional deterministic automata that classify images on the basis of activity, the number of state changes that occur in a trajectory initiated from the image. When the number of time steps of the automaton is a trainable parameter, the search scheme identifies automata that generate a population of dynamical trajectories displaying high or low activity, depending on initial conditions. Automata of this nature behave as nonlinear activation functions with an output that is effectively binary, resembling an emergent version of a spiking neuron.
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01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Phys Rev E
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos