The cerebro-cerebellar-basal-gangliar network for visuomotor learning

摘要 视觉学习对人类和非人类灵长类动物的生活至关重要。视觉运动协会， 将任意符号分配给特定动作(就像刹车动作的红灯)，是一种很好的- 研究了视觉学习的形式。这一提议验证了大脑完成视觉运动的假设 使用解剖学定义的闭环网的联想学习，包括前额叶皮质， 基底节和小脑。在我们的前期工作中，我们制定了一个任务，研究如何 猴子学会了将两个新的分形符号中的一个与右手运动联系起来，而另一个符号 用左手的动作。每个实验都是从猴子对两个过度训练的符号做出反应开始的 他们已经见过几十万次了。在任意时刻，我们将符号变成两个分形符 猴子从未见过的符号。猴子需要40到70次试验才能学会这些新的联系。在……里面 我们的初步结果我们已经发现，小脑中外侧半球的浦肯野细胞跟踪 猴子一边学习，一边找出所需的联想。神经元发出信号表示 事先的决定。当先前的决定正确时，一半的神经元做出更多的反应；其他的神经元做出更多的反应 当先前的决定是错误的时候。这两种类型的神经元活动之间的差异提供了 一种认知错误信号，当猴子在机会水平上表现时，它是最大的，并逐渐 随着猴子学习这项任务，它变得与零没有什么不同。神经元并不能预测 即将做出的决定。尽管神经元在符号切换时活动发生了戏剧性的变化，但 运动的运动学根本不会改变。这项提案以这一发现为起点， 四个目标：1)使用病毒跨突触束追踪来发现皮质和基底节区域 投射到小脑视觉运动关联区。2)从网络的四个节点记录为 解剖学定义(小脑中外侧半球、齿状核、基底节、前额叶皮质)， 同时，使用多个单个神经元的记录，看看这些区域是否也有关于 视觉运动关联的过程3)使每个节点失活，看看它们的失活如何影响猴子的 学习新联系的能力，以及失活是否影响另一侧神经元的活动 节点。4)开发计算方法来分析同时记录的神经活动的活动 在小脑中外侧皮质的所有四个网络节点(目标2)中，关于以下参数 先前的结果和动作、手、符号，以及先前认知错误信号的强度和时代。 我们将使用降维技术来回答诸如手或符号是否可以 从网络活动中解码。我们将模拟小脑简单的棘波认知错误信号可能 通过网络传播，并用于促进视觉运动关联学习和处理 小脑、基底节和大脑皮层的信号

ABSTRACT Visual learning is critical to the lives of human and non-human primates. Visuomotor association, the assignment of an arbitrary symbol to a particular movement (like a red light to a braking movement), is a well- studied form of visual learning. This proposal tests the hypothesis that the brain accomplishes visuomotor associative learning using an anatomically defined closed-loop network, including the prefrontal cortex, the basal ganglia, and the cerebellum. In our preliminary work we have developed a task that studies how monkeys learn to associate one of two novel fractal symbols with a right hand movement, and the other symbol with a left hand movement. Every experiment begins with the monkeys responding to two overtrained symbols that they have seen hundreds of thousands of times. At an arbitrary time we change the symbols to two fractal symbols that the monkey has never seen. It takes the monkey 40 to 70 trials to learn the new associations. In our preliminary results we have discovered that Purkinje cells in the midlateral cerebellar hemisphere track the monkeys’ learning as they as they figure out the required associations. The neurons signal the result of the prior decision. Half of the neurons respond more when the prior decision was correct; the others respond more when the prior decision was wrong. The difference between the activity of these two types of neurons provides a cognitive error signal that is maximal when the monkeys are performing at a chance level, and gradually becomes not different from zero as the monkeys learn the task. The neurons do not predict the result of the impending decision. Although the neurons change their activity dramatically at the symbol switch, the kinematics of the movements do not change at all. This proposal takes this discovery as the starting point for four aims: 1) to use viral transynaptic tract tracing to discover the cortical and basal ganglia regions that project to the cerebellar visuomotor association area. 2) to record from the four nodes of the network as anatomically defined (midlateral cerebellar hemisphere, dentate nucleus, basal ganglia, prefrontal cortex), simultaneously, using multiple single neuron recordings, to see if these areas also have information about the process of visuomotor association 3) to inactivate each node, to see how their inactivation affects the monkey’s ability to learn new associations, and whether the inactivation affects the activity of the neurons at the other nodes. 4) to develop computational methods to analyze the activity of neural activity recorded simultaneously in all four nodes of the network (Aim 2) in the midlateral cerebellar cortex with regard to parameters such as prior outcome and movement, hand, symbol, and the intensity and epoch of the prior cognitive error signal. We will use dimensional reduction techniques to answer questions like whether hand or symbol can be decoded from network activity. We will model how the cerebellum simple spike cognitive error signal might propagate through the network and be used to facilitate visuomotor association learning and the processing of signals in the cerebellum, basal ganglia and cerebral cortex