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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

Chaotic synchronization using a network of neural oscillators.

V Srinivasa Chakravarthy¹, Neelima Gupte, S Yogesh

¹Department of Biotechnology, Indian Institute of Technology, Chennai, India. schakra@iitm.ac.in

International Journal of Neural Systems

|May 3, 2008

Summary

This study demonstrates synchronization in high-dimensional chaotic neural networks using a subset of state vector components. This research advances secure communications through complex system synchronization.

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Published on: August 14, 2015

Area of Science:

Artificial Intelligence
Complex Systems
Neuroscience

Background:

Synchronization of chaotic systems is crucial for applications like secure communications.
High-dimensional chaotic neural networks, such as the Complex Hopfield Network (CHN), exhibit complex behaviors.

Purpose of the Study:

To demonstrate synchronization in a high-dimensional chaotic neural network.
To investigate synchronization using only a subset of state vector components.

Main Methods:

Utilized a Complex Hopfield Network (CHN), an extension of the Hopfield Network.
Employed simulations to analyze the synchronizability of two identical CHNs.

Main Results:

Achieved synchronization between two identical high-dimensional CHNs.
Showed that synchronization is possible by communicating only a subset of state vector components.

Conclusions:

Synchronization of high-dimensional chaotic neural networks is feasible.
Subset communication is an effective method for synchronizing complex chaotic systems.