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Updated: Jun 16, 2025
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
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Coupling dynamics of locally active memristor based neurons
Yujiao Dong1, Rongrong Guo1, Yan Liang1
1Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Chaos (Woodbury, N.Y.)
|August 15, 2024
Summary
Coupling identical third-order memristive neurons reveals complex brain-like dynamics. Resistive coupling induces novel states, and network simulations generate exotic spatiotemporal patterns like chimera states.
Area of Science:
- Neuroscience
- Complex Systems
- Nonlinear Dynamics
Background:
- Brain-like dynamics necessitate higher-order complexity.
- Memristive neurons offer a platform for neuromorphic computing.
- Understanding coupled neuron behavior is crucial for network function.
Purpose of the Study:
- Investigate coupling effects on identical third-order memristive neurons.
- Explore dynamics under varying operating regimes and coupling strengths.
- Analyze emergent spatiotemporal patterns in a larger network.
Main Methods:
- Simulated dynamics of single and coupled third-order memristive neurons.
- Applied resistive coupling and analyzed behavior across different bias conditions.
- Utilized Hopf bifurcation theory to explain synchronization phenomena.
- Constructed a 100-neuron ring network with resistive synapses.
Main Results:
- Uncoupled neurons exhibit resting, periodic spiking, or chaotic states.
- Resistive coupling of resting neurons induces inhomogeneous states (static/dynamic Smale paradox).
- Coupled spiking/chaotic neurons show oscillation death, asynchronization, or synchronization.
- The 100-neuron network generated chimera death, amplitude chimeras, solitary states, and asynchronization.
Conclusions:
- Third-order memristive neuron coupling produces diverse and complex dynamics.
- Symmetry breaking in networks leads to exotic spatiotemporal patterns.
- Findings contribute to understanding neuromorphic networks via nonlinear circuit theory.
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