ArticleCognitive neurodynamics2024
Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization activities.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A memristor-based circuit design of avoidance learning with time delay and its application.Cognitive neurodynamics · 2024Article
- Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.Cognitive neurodynamics · 2024Article
- Memristive patch attention neural network for facial expression recognition and edge computing.Cognitive neurodynamics · 2024Article
- Enhancing in-situ updates of quantized memristor neural networks: a Siamese network learning approach.Cognitive neurodynamics · 2024Article
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5 authors.
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Abstract
Synaptic plasticity makes memristors particularly suitable for simulating the connection synapses between neurons that describe magnetic induction coupling. By applying a memristor to the synaptic coupling between two map-based neuron models, a memristor-coupled dual-neuron mapping (MCDN) model is proposed in this article. The MCDN model has a line fixed point set associated with the memristor initial state, which is always unstable for the model parameters and memristor initial state of interest. Complex spiking/bursting firing patterns and their transitions are disclosed using some dynamical analysis means. The numerical results show that these spiking/bursting firings are significantly relied on the memristor initial state, demonstrating the coexistence of firing patterns. Moreover, the initial effects of complete synchronization are explored for the homogeneous MCDN model, and it is clarified that in addition to being related to the coupling strength, the synchronization activities are extremely dependent on the initial states of the memristor and neurons. Finally, these numerical results are confirmed by the FPGA-based hardware experiments.
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