La Camera Giancarlo, Giugliano Michele, Senn Walter, Fusi Stefano
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, 49 Convent Dr, Rm 1B80, Bethesda, MD 20892, USA.
Biol Cybern. 2008 Nov;99(4-5):279-301. doi: 10.1007/s00422-008-0272-7. Epub 2008 Nov 5.
The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In particular, under such an assumption, it is possible to determine and study all the equilibrium points of the network dynamics when the neuronal response to noisy, in vivo-like, synaptic currents is known. The response function can be computed analytically for simple integrate-and-fire neuron models and it can be measured directly in experiments in vitro. Here we review theoretical and experimental results about the neural response to noisy inputs with stationary statistics. These response functions are important to characterize the collective neural dynamics that are proposed to be the neural substrate of working memory, decision making and other cognitive functions. Applications to the case of time-varying inputs are reviewed in a companion paper (Giugliano et al. in Biol Cybern, 2008). We conclude that modified integrate-and-fire neuron models are good enough to reproduce faithfully many of the relevant dynamical aspects of the neuronal response measured in experiments on real neurons in vitro.
如果假设对于大量行为相似的神经元，突触输入的统计特性是相同的（平均场方法），那么对相互作用神经元集体动力学若干方面的研究就可以得到极大简化。特别是，在这样的假设下，当已知神经元对类似体内噪声突触电流的响应时，就有可能确定并研究网络动力学的所有平衡点。对于简单的积分发放神经元模型，响应函数可以通过解析计算得到，并且可以在体外实验中直接测量。在这里，我们回顾关于具有平稳统计特性的噪声输入的神经响应的理论和实验结果。这些响应函数对于表征被认为是工作记忆、决策和其他认知功能的神经基础的集体神经动力学非常重要。在一篇配套论文中（朱利亚诺等人，《生物控制论》，2008年）回顾了时变输入情况的应用。我们得出结论，改进的积分发放神经元模型足以忠实地再现体外真实神经元实验中测量到的神经元响应的许多相关动力学方面。
