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首页> 外文期刊>Journal of Computational Neuroscience >A multivariate population density model of the dLGN/PGN relay
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A multivariate population density model of the dLGN/PGN relay

机译：dLGN / PGN中继的多元人口密度模型

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Using a population density approach we study the dynamics of two interacting collections of integrate-and-fire-or-burst (IFB) neurons representing thalamocortical (TC) cells from the dorsal lateral geniculate nucleus (dLGN) and thalamic reticular (RE) cells from the perigeniculate nucleus (PGN). Each population of neurons is described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. The state variables of each neuron are the membrane potential and the inactivation gating variable of the low-threshold Ca~(2+) current I_T The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. The population density model is validated by comparing its response to time-varying retinal input to Monte Carlo simulations of the corresponding IFB network composed of 100 to 1000 cells per population. In the absence of retinal input, the population density model exhibits rhythmic bursting similar to the 7 to 14 Hz oscillations associated with slow wave sleep that require feedback inhibition from RE to TC cells. When the TC and RE cell potassium leakage conductances are adjusted to represent cholingergic neuromodulation and arousal of the network, rhythmic bursting of the probability density model may either persists or be eliminated depending on the number of excitatory (TC to RE) or inhibitory (RE to TC) connections made by each presynaptic cell. When the probability den- sity model is stimulated with constant retinal input (10-100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. In this context of dLGN/PGN network modeling, we find the population density approach using 2,500 mesh points and resolving membrane voltage to 0.7 mV is over 30 times more efficient than 1000-cell Monte Carlo simulations.
机译：使用种群密度方法，我们研究了两个相互作用的集合即射或爆发（IFB）神经元的动力学，这些神经元代表来自背外侧膝状核（dLGN）和丘脑网状（RE）细胞的丘脑皮层（TC）细胞周围核（PGN）。用多元概率密度函数描述每个神经元种群，该函数满足具有适当定义的概率通量和边界条件的守恒方程。每个神经元的状态变量是膜电位和低阈值Ca〜（2+）电流I_T的失活门控变量。通过突触后神经元膜电位的瞬时跳跃来模拟种群的突触耦合和外部兴奋性驱动。 。人口密度模型通过将其对随时间变化的视网膜输入的响应与相应的IFB网络（由每个人口100到1000个细胞组成）的蒙特卡罗模拟进行比较来验证。在没有视网膜输入的情况下，人口密度模型表现出节律性爆发，类似于与慢波睡眠相关的7至14 Hz振荡，需要抑制从RE到TC细胞的反馈。当调节TC和RE细胞的钾泄漏电导以代表胆碱能神经调节和网络唤醒时，概率密度模型的节律性爆发可能会持续存在或被消除，具体取决于兴奋性（TC至RE）或抑制性（RE至TC）由每个突触前细胞建立的连接。当恒定的视网膜输入（10-100尖峰/秒）刺激概率密度模型时，根据细胞参数和网络连接性，可以观察到广泛的响应。其中包括异步突发和强音尖峰，类似睡眠纺锤的有节奏的突发脉冲以及种群发射速率的振荡，由于其幅度，频率或存在强壮尖峰，这些现象与睡眠纺锤是有区别的。在dLGN / PGN网络建模的背景下，我们发现使用2500网格点的人口密度方法和将膜电压解析为0.7 mV的效率比1000单元蒙特卡洛模拟的效率高30倍以上。

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