Modeling multi-area dynamics during motor control

阐明行为的神经基础是神经科学的一个基本目标。实现这一目标的进展是 复杂的事实是，大多数行为产生于一些分布式和相互作用之间的相互作用， 连接的大脑区域项目4使用与实验数据紧密联系的模型来解决这个问题， 序列运动行为的背景。许多复杂的运动行为可以被分解成一系列的 可以重新排列以产生各种其他行为的定型成分或“图案”。创建 从现有的图案序列，运动系统必须产生所需的神经活动，同时监测 运动的进展，以便适当的时间之间的过渡不同的主题。这个建模项目是 旨在开发和测试一个模型的互动网络代表运动皮层，运动丘脑， 基底神经节的输入和输出结构（即，纹状体和GPi/SNr），可以自主产生一个 各种各样的图案，把它们灵活地串成序列，并监测正在进行的活动，以确保 基元之间的转换发生在它们应该发生的时候。在这个模型中，大脑皮层和丘脑之间的回路 一个单一的皮质-丘脑网络，用于执行多个行为基序。关键是，这种皮质-丘脑 神经网络不是一个固定的实体，但可以通过基底神经节的抑制输出来修改艾德。The motif that the 皮质-丘脑网络在任何给定时间产生的能量是由运动丘脑中的哪组神经元决定的 在那个时候不被GPi/SNr活性抑制。不同的图案将通过改变图案来选择 GPi/SNr的活性，从而改变运动丘脑的抑制模式。因此， 模型中的GPi/SNr是为了保持当前基序，并驱动转换到序列中的下一个基序。 GPi/SNr的模型将用于研究和提出它们在一段时间内维持活动的机制。 motif并在motif之间切换。纹状体将被建模为皮质活动的监测器，其作用是阻止 当一个主题已经结束并且下一个主题可以开始时进行挖掘。当一个合适的机会已经确定艾德， 纹状体中的短暂活动将触发系统通过其投射从一个基序切换到另一个基序 GPi/SNr。这个建模项目与这个小组提案的总体目标紧密匹配， 运动系统对行为产生的定量理解。因为模型将 在多个区域（运动皮层，运动丘脑，GPi/SNr和纹状体）的神经群体的活动，它将 提供了许多预测，这些预测将使用内部其他项目产生的实验数据进行测试。 这组提案。这些测试的结果将用于完善模型，此外，预测 该模型将指导新的实验方法。从实验中导出约束的循环， 模型的预测将继续下去，直到我们达到一个有启发性的和生物学上合理的电路水平。 解释了我们在实验中观察到的现象，并为我们提供了新的见解， 生成电动机序列。

Elucidating the neural basis of behavior is a fundamental goal of neuroscience. Progress towards this goal is complicated by the fact that most behaviors arise from interactions between a number of distributed and intercon- nected brain regions. Project 4 uses models that are tightly linked to experimental data to address this issue in the context of sequential motor behaviors. Many complex motor behaviors can be decomposed into sequences of stereotyped components or `motifs' that can be rearranged to produce a wide variety of other behaviors. To create a sequence from existing motifs, the motor system must generate the required neural activity while monitoring movement progress in order to time transitions between different motifs appropriately. This modeling project is aimed at developing and testing a model of interacting networks representing motor cortex, motor thalamus and input and output structures of the basal ganglia (i.e., striatum and GPi/SNr) that can autonomously generate a wide variety of motifs, string them together flexibly into sequences, and monitor ongoing activity to assure that transitions between motifs occur when they should. In this model, the loop between cortex and thalamus creates a single cortico-thalamic network for the execution of multiple behavioral motifs. Critically, this cortico-thalamic network is not a fixed entity but can be modified by the inhibitory output of the basal ganglia. The motif that the cortico-thalamic network produces at any given time is determined by which set of neurons in the motor thalamus is not being inhibited by GPi/SNr activity at that time. Different motifs will be selected by changing the pattern of activity in the GPi/SNr, thereby modifying the pattern of inhibition in the motor thalamus. Thus, the role of the GPi/SNr in the model is to maintain the current motif and to drive transitions to the next motif in a sequence. Models of the GPi/SNr will be used to study and propose mechanisms by which they sustain activity during a motif and switch it between motifs. Striatum will be modeled as a monitor of cortical activity with the role of deter- mining when one motif has ended and the next can begin. When an appropriate opportunity has been identified, transient activity in the striatum will trigger the system to switch from one motif to another through its projections to the GPi/SNr. This modeling project is tightly matched to the overall goal of this group proposal, a detailed, quantitative understanding of the production of behavior by the motor system. Because the model relates the activities of neural populations in multiple regions (motor cortex, motor thalamus, GPi/SNr and striatum), it will provide many predictions that will be tested using the experimental data produced by the other projects within this group proposal. The results of these tests will be used to refine the model and, in addition, predictions of the model will guide new experimental approaches. The cycle of deriving constraints from experiments and predictions from models will continue until we arrive at an illuminating and biologically plausible circuit-level de- scription that accounts for what we observe in experiments and lends new insight into how flexible sequential motor sequences are generated.