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Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization
Bocheng Bao1, Jingting Hu1, Han Bao1
1School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People's Republic of China.
Cognitive Neurodynamics
|May 3, 2024
Summary
Memristors simulate neural synapses in a new model, revealing complex firing patterns dependent on initial states. These findings, confirmed by hardware experiments, advance neuromorphic computing.
Area of Science:
- Computational Neuroscience
- Materials Science
- Electrical Engineering
Background:
- Synaptic plasticity is crucial for neural computation.
- Memristors exhibit plasticity, making them suitable for simulating synapses.
- Existing neuron models lack memristor-based synaptic coupling.
Purpose of the Study:
- To propose a memristor-coupled dual-neuron mapping (MCDN) model.
- To investigate complex firing patterns and their dependence on memristor initial states.
- To analyze the impact of initial states on complete synchronization in the MCDN model.
Main Methods:
- Developing the MCDN model using memristors for synaptic coupling.
- Employing dynamical analysis to study firing patterns and stability.
- Conducting numerical simulations and FPGA-based hardware experiments.
Main Results:
- The MCDN model exhibits complex spiking/bursting firing patterns.
- Firing patterns are significantly influenced by the memristor's initial state, showing pattern coexistence.
- Complete synchronization is highly dependent on memristor and neuron initial states, not just coupling strength.
Conclusions:
- The MCDN model effectively simulates synaptic plasticity using memristors.
- Memristor initial states play a critical role in determining neuronal firing patterns and synchronization.
- FPGA validation confirms the model's practical applicability in neuromorphic systems.
