Controlling chaos in a chaotic neural network

The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern

Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network

CMOS current-mode chaotic neurons

This paper presents two nonlinear CMOS current-mode circuits that implement neuron soma equations for chaotic neural networks, and another circuit to realize programmable current-mode synapse using CMOS-compatible BJT's. They have been fabricated in a double-metal, single-poly 1.6 /spl mu/m CMOS technology and their measured performance reached the expected function and specifications. The neuron soma circuits use a novel, highly accurate CMOS circuit strategy to realize piecewise-linear characteristics in the current-mode domain. Their prototypes obtain reduced area and low voltage power supply (down to 3 V) with clock frequency of 500 kHz. As regard to the synapse circuit, it obtains large linearity and continuous, linear, weight adjustment by exploration of the exponential-law operation of CMOS-BJT's. The full accordance observed between theory and measurements supports the development of future analog VLSI chaotic neural networks to emulate biological systems and advanced computation

Hopf Bifurcation and Chaos in Tabu Learning Neuron Models

In this paper, we consider the nonlinear dynamical behaviors of some tabu leaning neuron models. We first consider a tabu learning single neuron model. By choosing the memory decay rate as a bifurcation parameter, we prove that Hopf bifurcation occurs in the neuron. The stability of the bifurcating periodic solutions and the direction of the Hopf bifurcation are determined by applying the normal form theory. We give a numerical example to verify the theoretical analysis. Then, we demonstrate the chaotic behavior in such a neuron with sinusoidal external input, via computer simulations. Finally, we study the chaotic behaviors in tabu learning two-neuron models, with linear and quadratic proximity functions respectively.Comment: 14 pages, 13 figures, Accepted by International Journal of Bifurcation and Chao

Winnerless competition between sensory neurons generates chaos: A possible mechanism for molluscan hunting behavior

© 2002 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.In the presence of prey, the marine mollusk Clione limacina exhibits search behavior, i.e., circular motions whose plane and radius change in a chaotic-like manner. We have formulated a dynamical model of the chaotic hunting behavior of Clione based on physiological in vivo and in vitroexperiments. The model includes a description of the action of the cerebral hunting interneuron on the receptor neurons of the gravity sensory organ, the statocyst. A network of six receptor model neurons with Lotka–Volterra-type dynamics and nonsymmetric inhibitory interactions has no simple static attractors that correspond to winner take all phenomena. Instead, the winnerless competition induced by the hunting neuron displays hyperchaos with two positive Lyapunov exponents. The origin of the chaos is related to the interaction of two clusters of receptor neurons that are described with two heteroclinic loops in phase space. We hypothesize that the chaotic activity of the receptor neurons can drive the complex behavior of Clione observed during hunting.Support for this work came from NIH Grant No. 2R01 NS38022- 05A1. P.V. acknowledges support from MCT BFI2000-0157. M.R. acknowledges support from U.S. Department of Energy Grant No. DE-FG03-96ER14592

Controlling chaos in diluted networks with continuous neurons

Diluted neural networks with continuous neurons and nonmonotonic transfer function are studied, with both fixed and dynamic synapses. A noisy stimulus with periodic variance results in a mechanism for controlling chaos in neural systems with fixed synapses: a proper amount of external perturbation forces the system to behave periodically with the same period as the stimulus.Comment: 11 pages, 8 figure