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Learning-induced synchronization of a globally coupled excitable map system.

Y Hayakawa¹, Y Sawada

¹Research Institute of Electrical Communication, Tohoku University, Sendai, Japan.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics

|October 14, 2000

Summary

This study introduces a pulse-coupled neural network model that exhibits collective chaos. The Hebbian learning algorithm enables the network to synchronize and reproduce complex temporal signals.

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Area of Science:

Computational neuroscience
Complex systems

Background:

Neural networks often use simple units, but complex collective behaviors emerge in networks.
Understanding how networks learn and process temporal information is crucial.

Purpose of the Study:

To propose a novel pulse-coupled neural network model using excitable maps.
To investigate the network's ability to synchronize and learn temporal signals.

Main Methods:

Developed a pulse-coupled neural network model with one-dimensional excitable maps.
Implemented a Hebbian learning algorithm for synaptic plasticity.
Analyzed network dynamics, synchronization patterns, and signal reproduction capabilities.

Main Results:

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The network exhibits collective chaos in active states.

Hebbian learning induces synchronization, forming clusters with power-law size distribution.

The network successfully reproduces applied stationary and periodic signals with high temporal resolution.

Conclusions:

The proposed network model acts as a temporal association device operating in chaotic states.
The model demonstrates learning capabilities beyond its inherent time scales.
The findings suggest potential applications in temporal information processing.