Bogacz Rafal, Martin Moraud Eduardo, Abdi Azzedine, Magill Peter J, Baufreton Jérôme
Medical Research Council Brain Network Dynamics Unit, Department of Pharmacology, University of Oxford, Oxford, United Kingdom.
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2016 Jul 7;12(7):e1005004. doi: 10.1371/journal.pcbi.1005004. eCollection 2016 Jul.
The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes' equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called 'prototypic' and 'arkypallidal' neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions.
外侧苍白球(GPe)是基底神经节回路中的一个关键核团,该回路被认为与动作选择有关。一类计算模型假定,在动作选择过程中,基底神经节会针对给定情境下所有可用动作计算出应该选择它们的概率。这些模型表明,GPe和丘脑底核(STN)神经元网络会计算贝叶斯方程中的归一化项。为了执行这种计算,GPe需要向STN发送与STN神经元活动的特定函数相等的反馈。然而,该函数的复杂形式使得单个GPe神经元,甚至单一类型的GPe细胞都不太可能计算它。在此,我们展示了如何在一个包含两种γ-氨基丁酸能GPe投射神经元(即所谓的“原型”和“原苍白球”神经元)的网络中计算该函数,这两种神经元在体内具有不同的反应特性和不同的连接方式。我们将模型预测结果与实验报道的这两类GPe神经元群体的连接性和输入-输出函数(f-I曲线)进行了比较。我们表明,这两种不同类型的细胞共同满足了计算最佳动作选择所需函数的必要条件。我们得出结论,凭借其独特的反应特性和连接方式,原苍白球和原型GPe神经元网络构成了一个能够支持动作后验概率计算的神经基质。