History-dependent multiple-time-scale dynamics in a single-neuron model
History-dependent multiple-time-scale dynamics in a single-neuron model
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DOI：
10.1523/jneurosci.0763-05.2005
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发表时间：
2005-07-13
影响因子：
5.3
通讯作者：
Brenner, N
中科院分区：
文献类型：
--
作者：
Gilboa, G;Chen, R;Brenner, N
动力学中历史依赖的特征时间尺度已经在神经系统组织的几个层次上被观察到。这种动态可以为计算和存储提供强大的手段。在单个神经元的水平上，包括离子通道动力学在内的几种微观机制可以支持多时间尺度的动力学。时间上复杂的通道动力学如何引起神经元的动力学特性尚不清楚。在这里，我们构建了一个模型，该模型捕获了这两个组织级别之间的连接的一些特征。模型神经元在几个方面表现出历史依赖的多时间尺度动态：首先，在刺激后，恢复时间尺度与刺激持续时间呈幂律标度关系；其次，响应持续刺激的神经活动的时间模式随着时间的推移而调节；最后，刺激阶跃变化后适应的特征时间尺度取决于前一个刺激的持续时间。所有这些效应都是通过实验观察到的，目前的单神经元模型无法解释。这里提出的模型神经元由离子通道的集合组成，这些离子通道可以在一个大的退化无活性状态池中漫游，因此在分子水平上表现出多时间尺度的动力学。通道失活率取决于最近的神经活动，而神经活动又取决于神经反应函数对活跃通道比例的调节。这种结构产生了一个模型，它强有力地展示了非指数历史依赖动力学，在定性上与实验结果一致。
History-dependent characteristic time scales in dynamics have been observed at several levels of organization in neural systems. Such dynamics can provide powerful means for computation and memory. At the level of the single neuron, several microscopic mechanisms, including ion channel kinetics, can support multiple-time-scale dynamics. How the temporally complex channel kinetics gives rise to dynamical properties of the neuron is not well understood. Here, we construct a model that captures some features of the connection between these two levels of organization. The model neuron exhibits history-dependent multiple-time-scale dynamics in several effects: first, after stimulation, the recovery time scale is related to the stimulation duration by a power-law scaling; second, temporal patterns of neural activity in response to ongoing stimulation are modulated over time; finally, the characteristic time scale for adaptation after a step change in stimulus depends on the duration of the preceding stimulus. All these effects have been observed experimentally and are not explained by current single-neuron models. The model neuron here presented is composed of an ensemble of ion channels that can wander in a large pool of degenerate inactive states and thus exhibits multiple-time-scale dynamics at the molecular level. Channel inactivation rate depends on recent neural activity, which in turn depends through modulations of the neural response function on the fraction of active channels. This construction produces a model that robustly exhibits nonexponential history-dependent dynamics, in qualitative agreement with experimental results.