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Hindmarsh-Rose neuron model with memristors.

Usha K¹, Subha P A²

¹Department of Physics, University of Calicut, Kerala 673635, India.

|January 15, 2019

Summary

This summary is machine-generated.

This study explores how electromagnetic fields affect Hindmarsh-Rose neurons using memristors. Researchers observed diverse neural dynamics, including synchrony and desynchrony, offering insights for neural network hardware.

Keywords:

Amplitude death Anti-phase oscillations Electrical and chemical synapse Hindmarsh-Rose model Memristors Near death rare spikes Synchronization

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Area of Science:

Computational Neuroscience
Neuroscience
Artificial Intelligence

Background:

The Hindmarsh-Rose neuron model is a foundational tool for studying neural dynamics.
Electromagnetic fields, arising from ion exchange, can influence neuronal behavior.
Memristors offer a novel approach to modeling complex relationships in neural systems.

Purpose of the Study:

To investigate the impact of time-varying electromagnetic fields on single and coupled Hindmarsh-Rose neurons.
To model the interaction between electromagnetic fields and neuronal membrane potential using memristors.
To analyze the rich dynamical behaviors emerging from neuronal coupling under electromagnetic influence.

Main Methods:

Utilized the Hindmarsh-Rose neuron model.
Incorporated memristors to represent the magnetic flux-membrane potential relationship.
Performed bifurcation analysis by adjusting electromagnetic field modulation intensity.
Employed electrical and chemical synapses for neuronal coupling.
Analyzed transverse Lyapunov exponents to identify transitions between synchrony and desynchrony.

Main Results:

Observed diverse dynamical behaviors including synchrony, desynchrony, amplitude death, anti-phase oscillations, and coexistence of resting and spiking states.
Identified transitions from desynchrony to synchrony using transverse Lyapunov exponents.
Demonstrated the influence of electromagnetic fields on complex neural network dynamics.

Conclusions:

Memristor-based analysis of neural networks provides a valuable framework for biological system modeling.
Memristors can be effectively implemented as synapses in artificial neural network hardware.
The study highlights the potential of memristive elements in understanding and replicating complex neural functions.