A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models
A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models
复制标题
DOI：
10.1101/870345
复制
发表时间：
2019-12
影响因子：
2.5
通讯作者：
M. Carlu;O. Chehab;L. Dalla Porta;D. Depannemaecker;C. Héricé;M. Jedynak;E. Köksal Ersöz;P. Muratore;S. Souihel;C. Capone;Y. Zerlaut;A. Destexhe;M. di Volo
中科院分区：
文献类型：
--
作者：
M. Carlu;O. Chehab;L. Dalla Porta;D. Depannemaecker;C. Héricé;M. Jedynak;E. Köksal Ersöz;P. Muratore;S. Souihel;C. Capone;Y. Zerlaut;A. Destexhe;M. di Volo
我们提出了一个平均场的形式主义能够预测集体动态的大型网络的电导为基础的相互作用尖峰神经元。我们将这种形式主义应用于几个神经元模型，从最简单的自适应指数积分和火灾模型到更复杂的Hodgkin-Huxley和Morris-Lecar模型。我们表明，由此产生的平均场模型能够预测正确的自发活动的兴奋性和抑制性神经元在异步不规则的制度，典型的皮质动力学。此外，它是可能的定量预测的人口对外部刺激的形式的外部穗列车。因此，这种平均场形式主义提供了一个范例，以弥合人口动态和个体细胞生理学的微观复杂性之间的规模。人口模型是研究神经网络动力学和在宏观尺度上模拟大脑的强大数学工具。我们提出了一个平均场模型，能够定量预测的时间动态的网络复杂的尖峰神经元模型，从Integrate-and-Fire到Hodgkin-Huxley，从而将人口模型神经元电生理学。这打开了一个角度，从电生理记录生成生物现实的平均场模型。
We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology. NEW & NOTEWORTHY Population models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.