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NMDA 驱动的前额叶皮层神经元不规则放电的动力学基础。

Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons.

作者信息

Durstewitz Daniel, Gabriel Thomas

机构信息

Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, UK.

出版信息

Cereb Cortex. 2007 Apr;17(4):894-908. doi: 10.1093/cercor/bhk044. Epub 2006 Jun 1.

Abstract

Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. Here we report that highly irregular firing can be induced through a combination of NMDA- and dopamine D1 receptor agonists applied to adult PFC neurons in vitro. The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, while neurons with clearly mono- or bimodal distributions fired much more regularly. Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. The nonlinearity of NMDA conductances was crucial for inducing this regime. NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.

摘要

慢N-甲基-D-天冬氨酸(NMDA)突触电流被认为对工作记忆期间前额叶皮层(PFC)中观察到的持续升高的放电率有很大贡献。在持续活动期间,许多神经元的放电高度不规则。在此我们报告,通过在体外将NMDA和多巴胺D1受体激动剂应用于成年PFC神经元,可以诱导出高度不规则的放电。最高的峰峰间隔(ISI)变异性出现在一种过渡状态,此时阈下膜电位分布从单峰变为双峰,而具有明显单峰或双峰分布的神经元放电则更为规则。不规则ISI序列中的可预测性显著高于由噪声驱动的线性过程所预期的,这表明它可能最好通过复杂(可能是混沌的)非线性确定性过程来描述。因此,在体外观察到的现象可以在纯确定性生物物理模型神经元中重现。这些模型中的高放电不规则性出现在一个混沌的、接近分岔的状态,其特征是膜电位分布从单峰变为双峰,并且具有与体外观察到的类似的ISI返回图。NMDA电导的非线性对于诱导这种状态至关重要。NMDA诱导的不规则动力学可能对工作记忆和神经编码期间的计算过程具有重要意义。

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