Related Experiment Video
Updated: Jun 12, 2025
08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
7.7K
Dynamic Effects Analysis in Fractional Memristor-Based Rulkov Neuron Model.
Mahdieh Ghasemi1, Zeinab Malek Raeissi1, Ali Foroutannia2
1Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur 9319774446, Iran.
Biomimetics (Basel, Switzerland)
|September 27, 2024
Summary
Researchers developed a fractional memristor Rulkov neuron model, enhancing neural function analysis. This new model improves heritable properties, multi-time scale activity, and firing frequency responses, offering better synchronization in neural networks.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Fractional Calculus
Background:
- Complex nervous system modeling relies on mathematical neuron models like Fitzhugh-Nagumo and Hodgkin-Huxley.
- These models' complexity hinders detailed neural function analysis.
- The discrete Rulkov model offers a simpler approach to studying neuronal dynamics.
Purpose of the Study:
- Introduce a novel fractional memristor Rulkov neuron model.
- Investigate dynamic effects and improvements by combining memristors and fractional derivatives.
- Enhance neuron modeling for more accurate biophysical effect estimation.
Main Methods:
- Combined a Rulkov neuron model with a memristor.
- Evaluated system parameters using bifurcation diagrams and the 0-1 chaos test.
- Applied a discrete fractional-order approach to the Rulkov memristor map and analyzed coupled systems.
Main Results:
- The fractional memristor Rulkov model exhibits tonic, periodic, and chaotic firing behaviors.
- Fractional order significantly impacts system dynamics and enhances synchronization.
- Improved generation of heritable properties and multi-time scale activity compared to full-order models.
Conclusions:
- The fractional memristor Rulkov neuron model offers enhanced accuracy and performance.
- Fractional calculus and memristors effectively improve discrete neuron models.
- This combined approach is valuable for modeling biophysical effects in neural networks.
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