Chaos and Neural Networks

Lábos, E.

doi:10.1007/978-1-4757-9631-5_23

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…In this paper, we attempt to study learning-induced synchronization of a developing neural network, whose size ranges from tens to hundreds of neurons. Since we focus on the robust properties of collective neuronal dynamics which are not sensitive to details of neurons (Usery and Reid, 1999;Tresch and Kiehn, 2002;Zhigulin et al, 2003), we apply a one-dimensional map with excitable dynamics representing the neuronal activities of the network, instead of a biological model of action potential (AP) for nerve cells (Labos, 1986;Hayakawa and Sawada, 2000). In our model, each neuron integrates all the inputs from other neurons and fires whenever the membrane potential reaches a threshold.…”

Section: Introductionmentioning

confidence: 99%

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Chao

Chen

2005

J Comput Neurosci

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Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.

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Section: Introductionmentioning

confidence: 99%

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Chao

Chen

2005

J Comput Neurosci

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“…In recent years, many neuronal models have been proposed to simulate real neural systems, [1] in which simple or complex chaotic phenomena exist. [2] Experimental evidence demonstrates that synchronous neural oscillations contain many cortical processes, [3,4] and ensembles of neurons can synchronize to accomplish critical functional goals. [5,6] Many cells are linked to each other by specialized intercellular pathways known as gap junctions.…”

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confidence: 99%

Chaotic Synchronization of Two Electrical Coupled Neurons with Unknown Parameters Based on Adaptive Control

Wang¹,

Ren²

2011

Chinese Phys. Lett.

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Chaotic synchronization of two electrical coupled FitzHugh-Nagumo (FHN) neurons with unknown parameters via adaptive control is investigated. Based on the Lyapunov stability theory, an adaptive controller and a parameter update law are designed, which can achieve the synchronization of the two gap junction coupled FHN neurons when the individual neuron is chaotic, without considering the coupling strength. Moreover, the unknown parameters are identified successfully and the controller is robust to the random noise. The numerical simulation results confirm the effectiveness of the designed controller.

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Signs of Dynamic Processes in Organized Neural Tissue: Extracting Order from Chaotic Data

Bullock¹

1989

Springer Series in Brain Dynamics

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Chaos and Neural Networks

Cited by 7 publications

References 9 publications

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Chaotic Synchronization of Two Electrical Coupled Neurons with Unknown Parameters Based on Adaptive Control

Signs of Dynamic Processes in Organized Neural Tissue: Extracting Order from Chaotic Data

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