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Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization

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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.

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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.