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实验发现, 在觉醒时学习导致了突触强度的净增强, 并伴随着神经元从强直性发放向阵发性发放的转变; 在睡眠时突触强度净减弱到基准值, 并且神经元的阵发性发放重新转变为强直性发放. 本文提出了一种突触可塑性模型, 并在由Hindmarsh-Rose神经元组成的神经网络中, 实现了觉醒-睡眠周期中这种突触强度的变化和神经元发放动力学的转变. 通过数值模拟和理论分析进一步发现, 神经网络的平均突触权值在长时间的觉醒或睡眠后可以达到一个稳定值, 该稳定值取决于模型中一些特定参数的比值. 特别地, 当平均突触权值达到稳定值时, 突触权值的分布呈现出稳定的真实神经系统中观察到的对数正态分布, 并且这种分布的波动性与突触可塑性模型中噪声的波动性呈正相关. 本文提出的突触可塑性模型及其动力学的研究结果可为觉醒-睡眠周期中突触可塑性及其神经元发放的生理机制研究提供理论参考, 并在睡眠障碍治疗或干预措施的开发中具有潜在的应用价值.It has been found experimentally that learning during wakefulness leads to a net enhancement of synaptic strength, accompanied by the neural dynamical transition from tonic to bursting firing, while the net synaptic strength decreases to a baseline level during sleep, accompanied by the transition from bursting to tonic firing. In this paper, we establish a model of synaptic plasticity, which can realize synaptic strength changes and neural dynamical transitions in wakefulness-sleep cycle by using the coupled Hindmarsh-Rose neurons. Through numerical simulation and theoretical analysis, it is further found that the average synaptic weight of the neural network can reach a stable value during either prolonged wakefulness or prolonged sleep, which depends on the ratio of some specific parameters in the model. Particularly, the synaptic weight exhibits a stable log-normal distribution observed in a real neural system, when the average synaptic weight reaches a stable value. Moreover, the fluctuation of this weight distribution is positively correlated with the fluctuation of noise in the synaptic plasticity model. The provided model of the synaptic plasticity and its dynamics results can provide a theoretical reference for studying the physiological mechanism of synaptic plasticity and neuronal firings during the wakefulness-sleep cycle, and they are expected to have potential applications in the development of therapeutic interventions for sleep disorders.
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Keywords:
- neural networks/
- synaptic plasticity/
- neural dynamics/
- synchronization
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