Deep brain stimulation and lag synchronization in a memristive two-neuron network

•A memristive two-neuron network with deep brain stimulation is presented.•The complex bursting activity is revealed in the 2-D Morris–Lecar model.•The desynchronization mechanism and lag synchronization are investigated.•The network is implemented based on FPGA to explore the physical neurons. In t...

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Veröffentlicht in:Neural networks 2024-12, Vol.180, p.106728, Article 106728
Hauptverfasser: Yu, Xihong, Bao, Han, Xu, Quan, Chen, Mo, Bao, Bocheng
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Sprache:eng
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Zusammenfassung:•A memristive two-neuron network with deep brain stimulation is presented.•The complex bursting activity is revealed in the 2-D Morris–Lecar model.•The desynchronization mechanism and lag synchronization are investigated.•The network is implemented based on FPGA to explore the physical neurons. In the pursuit of potential treatments for neurological disorders and the alleviation of patient suffering, deep brain stimulation (DBS) has been utilized to intervene or investigate pathological neural activities. To explore the exact mechanism of how DBS works, a memristive two-neuron network considering DBS is newly proposed in this work. This network is implemented by coupling two-dimensional Morris–Lecar neuron models and using a memristor synaptic synapse to mimic synaptic plasticity. The complex bursting activities and dynamical effects are revealed numerically through dynamical analysis. By examining the synchronous behavior, the desynchronization mechanism of the memristor synapse is uncovered. The study demonstrates that synaptic connections lead to the appearance of time-lagged or asynchrony in completely synchronized firing activities. Additionally, the memristive two-neuron network is implemented in hardware based on FPGA, and experimental results confirm the abundant neuronal electrical activities and chaotic dynamical behaviors. This work offers insights into the potential mechanisms of DBS intervention in neural networks.
ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2024.106728