Adaptive Behaviour Research Group, Department of Psychology, University of Sheffield, UK.
Neural Netw. 2009 Oct;22(8):1174-88. doi: 10.1016/j.neunet.2009.07.018. Epub 2009 Jul 19.
The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit's connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells.
纹状体是基底神经节的主要输入结构，对运动控制和学习都至关重要。它接收来自整个新皮层、海马体、杏仁核和丘脑的会聚输入，是大脑中多巴胺的主要受体。在纹状体内部，存在一个 GABA 能微电路，它作用于这些输入，由占主导地位的中等棘突投射神经元（MSNs）和快速放电中间神经元（FSIs）组成。由于非分层结构阻碍了重复的典型微电路的识别，因此在理解其执行的计算方面几乎没有取得进展。我们在这里开始识别纹状体中潜在的动态定义的计算元素。我们构建了一个新的纹状体微电路连接性的三维模型，并使用我们的多巴胺调制 MSN 和 FSIs 神经元模型来实例化这个模型。引入了一种新的 FSIs 之间的缝隙连接模型，并对其进行了实验数据的调整。我们引入了一种新的多尖峰串分析方法，并将其应用于模型的输出，以在多个时间尺度上找到同步神经元的群组。我们发现，在现实的体内背景输入下，小群同步的 MSNs 会自发出现，这与实验观察结果一致，而且同步的神经元群组数量和时间尺度强烈依赖于模拟的多巴胺浓度。我们还表明，来自 FSIs 的前馈抑制出人意料地增加了 MSNs 的发射率。这种只有在没有多巴胺的情况下才会自发形成的小细胞集合，可能会导致人类和动物在多巴胺细胞丧失后出现运动控制问题。
