Evolving Cellular Automata Based Associative Memory for Pattern Recognition

Ganguly, Niloy; Das, Arijit; Maji, Pradipta; Sikdar, Biplab K.; Chaudhuri, P.P.

doi:10.1007/3-540-45307-5_11

Cited by 17 publications

(14 citation statements)

References 4 publications

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“…The model is a neurobiologically inspired cellular automaton, in which dynamics concerns the whole, which is simultaneously updated -instead of sequentially updating a small neighborhood at each time step. This automaton (or Little dynamics) strategy has already revealed efficient in modeling several aspects of associative memory (Ganguly et al, 2003;Cortes et al, 2004). Interesting enough, concerning this property, neural automata often exhibit more interesting behavior than their Hopfield-like, sequentially-updated counterparts, in spite of the fact that any two successive states are stronger correlated in the sequential case.…”

Section: Introductionmentioning

confidence: 99%

Chaotic hopping between attractors in neural networks

2007

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We present a neurobiologically-inspired stochastic cellular automaton whose state jumps with time between the attractors corresponding to a series of stored patterns. The jumping varies from regular to chaotic as the model parameters are modified. The resulting irregular behavior, which mimics the state of attention in which a systems shows a great adaptability to changing stimulus, is a consequence in the model of shorttime presynaptic noise which induces synaptic depression. We discuss results from both a mean-field analysis and Monte Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Chaotic hopping between attractors in neural networks

2007

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“…LCA structure Cellular Automata were invented by Ulam and Neumann at the end of the Forties [8]. A CA is a D-dimensional grid (lattice) composed of agents called cells.…”

Section: Learning Cellular Automatamentioning

confidence: 99%

A Quantum Inspired Learning Cellular Automaton for Solving the Travelling Salesman Problem

Draa

Meshoul

2010

2010 12th International Conference on Computer Modelling and Simulation

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Cellular automata (CA) have been shown to be suitable for modelling and simulating complex adaptive systems. To achieve adaptation to the exterior environment, Learning Cellular Automata (LCA) have been proposed as an extension of traditional CA, which exhibit only local adaptation, in order to allow a global adaptation of the automaton to its environment. In this paper, a new variant of LCA is described. It relies on two main ideas: The first one is to enhance the learning capabilities of the cellular automaton by using new operations inspired from the Reynolds's Boids model and the second one is to foster diversity of cells' states within the cellular automaton by adopting a quantum representation of the CA cells. Through solving the Travelling Salesman Problem, we show the effectiveness of the proposed model in implementing a LCA. Therefore a better simulation of complex adaptive systems can be envisaged.

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“…Cellular automata have also been used for the implementation of a Dictionary Search Processor [63], memory controllers [64], and the generation of test patterns for Built-In SelfTest (BIST) of VLSI chips [65,66]. An overview of application-specific cellular automata is given in [67].…”

Section: Cellular Automata Architecturesmentioning

confidence: 99%