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Updated: Aug 8, 2025
08:32
External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures
Published on: May 7, 2017
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A memristive neuron and its adaptability to external electric field
Feifei Yang1, Ying Xu2, Jun Ma1
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Chaos (Woodbury, N.Y.)
|March 1, 2023
Summary
Memristors integrated into neural circuits enable external control via electric fields. This study reveals how memristive energy influences neuron firing modes, offering insights into neural network behavior.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Materials Science
Background:
- Memristors offer enhanced controllability in neural circuits under external physical stimuli.
- Memristive current in flux-controlled memristors can model electromagnetic induction effects on neurons.
- Charge-controlled memristors can be used to investigate external electric field impacts on neural circuits.
Purpose of the Study:
- To incorporate a charge-controlled memristor into a neural circuit to analyze external electric field effects.
- To develop an energy function to understand firing mode dependence on capacitive, inductive, and memristive energy channels.
- To explore the influence of memristive energy on neuron firing patterns and collective network behaviors.
Main Methods:
- A charge-controlled memristor was integrated into a neural circuit model.
- Field energy in each component was calculated, leading to a dimensionless energy function (H).
- The impact of energy distribution across capacitive, inductive, and memristive channels on firing modes was analyzed.
Main Results:
- Memristive channel energy (HM) dominated Hamilton energy (H), inducing chaotic/periodic firing modes.
- Magnetic field energy (HL) dominance led to bursting and spiking behaviors.
- Stochastic resonance was observed under noisy electric field disturbances; negative Hamilton energy occurred under strong fields.
Conclusions:
- The memristive neuron model accurately reflects biophysical neuron properties.
- Memristive current modification controls firing modes through energy accommodation.
- The model facilitates research into collective behaviors and self-organization in networks influenced by energy flow and noise.
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