基于STDP耦合神经元集群同步活动
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摘要
文章基于依赖峰电位发放时间可塑性(spike timing dependent plasticity,STDP),提出在外部周期刺激和噪声共同作用下全局耦合神经振子集群的相位演化模型,导出了描述神经振子集群整体活动的平均数密度的演化方程。数值模拟结果表明,神经振子集群同步活动的大小取决于STDP耦合机制、外部刺激强度和刺激频率。基于STDP耦合机制的神经振子集群的同步活动要大于无STDP耦合机制的。刺激对神经振子集群同步活动的影响取决于刺激强度和刺激频率。当系统特征频率大约为刺激频率的3倍时,神经振子集群的数密度呈现出周期性振荡行为;在相同的刺激频率下,神经振子集群的同步程度与刺激强度有关,刺激强度在一定范围内越强,同步程度越高。
Based on the spike timing dependent plasticity(STDP), a dynamical evolution model of globally coupled neuronal oscillator population in the presence of external periodic stimulus and noise is proposed, and the evolution equation of average number density describing the overall activity of neuronal population is derived. Numerical simulations indicate that the synchronous activity of neuronal oscillator population depends on the STDP and the external stimulus intensity and frequency. The synchronous activity of neuronal oscillator population under STDP is larger than that without STDP. The effect of stimulus on the synchronous activity of neuronal oscillator population depends on the stimulus intensity and frequency. As the characteristic frequency is about three times bigger than the stimulus frequency, the neuronal population exhibits periodic oscillation behavior; as the characteristic frequency is the same as the stimulus frequency, the synchronization degree of the neuronal population is related to the stimulus intensity, and in a certain range, the stronger the stimulus, the higher the degree of synchronization.
Based on the spike timing dependent plasticity(STDP), a dynamical evolution model of globally coupled neuronal oscillator population in the presence of external periodic stimulus and noise is proposed, and the evolution equation of average number density describing the overall activity of neuronal population is derived. Numerical simulations indicate that the synchronous activity of neuronal oscillator population depends on the STDP and the external stimulus intensity and frequency. The synchronous activity of neuronal oscillator population under STDP is larger than that without STDP. The effect of stimulus on the synchronous activity of neuronal oscillator population depends on the stimulus intensity and frequency. As the characteristic frequency is about three times bigger than the stimulus frequency, the neuronal population exhibits periodic oscillation behavior; as the characteristic frequency is the same as the stimulus frequency, the synchronization degree of the neuronal population is related to the stimulus intensity, and in a certain range, the stronger the stimulus, the higher the degree of synchronization.
引文
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[11] BREAKSPEAR M,HEITMANN S,DAFFERTSHOFER A.General models of cortical oscillations:neurobiological implications of the Kuramoto model[J].Frontiers in Human Neuroscience,2010,4:Article190.
[2] BUZSAKI G.Rhythms of the brain[M].Oxford:Oxford University Press,2006:132-137.
[3] KURAMOTO Y.Collective synchronization of pulse-coupled oscillators and excitable units[J].Physica D:Nonlinear Phenomena,1991,50(1):15-30.
[4] JIAO X F,WANG R B.Synchronization in neuronal population with the variable coupling strength in the presence of external stimulus[J].Applied Physics Letters,2006,88:203901.
[5] SELIGER P,YOUN S C.Plasticity and learning in network of coupled phase oscillators[J].Physical Review E,2002,65:041906.
[6] FELDMAN D E.The spike-timing dependence of plasticity[J].Neuron,2012,75(4):556-571.
[7] TASS P,MAJTANIK M.Long-term anti-kindling effects of desynchronizing brain stimulation:a theoretical study[J].Biological Cybernetics,2006,94(1):58-66.
[8] NIYOGI R K,ENGLISH L Q.Learning-rate-dependent clustering and self-development in a network of coupled phase oscillators[J].Physical Review E,2009,80:066213.
[9] DAN Y,POO M M.Spike timing-dependent plasticity of neural circuits[J].Neuron,2004,44(1):23-30.
[10] MAISTRENKO Y L,LYSYANSKY B,HAUPTMANN C,et al.Multistability in the Kuramoto model with synaptic plasticity[J].Physical Review E,2007,75:066207.
[11] BREAKSPEAR M,HEITMANN S,DAFFERTSHOFER A.General models of cortical oscillations:neurobiological implications of the Kuramoto model[J].Frontiers in Human Neuroscience,2010,4:Article190.