A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model

Authors:
Yoshikazu Suemitsu;Shigetoshi Nara
Affiliations:
Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan;Department of Electrical and Electronic Engineering, Okayama University, Okayama, 700-8530, Japan
Venue:
Neural Computation
Year:
2004

Citing 2
Cited 2

Memory search using complex dynamics in a recurrent neural network model

Neural Networks
Dynamical cell assembly hypothesis—theoretical possibility of spatio-temporal coding in the cortex

Neural Networks - 1996 Special issue: four major hypotheses in neuroscience

Tracking a Moving Target Using Chaotic Dynamics in a Recurrent Neural Network Model

Neural Information Processing
Chaotic dynamics in quasi-layered recurrent neural network model and application to complex control via simple rule

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.