Cerebellar processing of noisy inputs

摘要/项目摘要 小脑被认为是大脑感觉运动处理的关键部分。基本特征 小脑的学习机制已经被很好地描述了。这种学习是改善饲料的服务- 前向预测，这对小脑的大部分贡献运动的准确性。这项建议 是关于小脑如何处理噪音输入的，并基于大量的初步数据， 一种由小脑实现的新计算，使其对噪声输入的反应更具适应性。 我们开始训练小脑输出一个目标大小的反应，以一个特定的模式，其苔藓 光纤输入通过系统地改变探测苔藓纤维输入的不同性，我们看到小脑 不随相异度的增加而减小响应幅度。这样做是不适应的，因为 先前没有经验表明这些较小的幅度是正确的。相反，小脑减少了 当相异性增加时，它做出响应的可能性增加，但当它做出响应时，它几乎总是 产生正确的振幅。我们提出了丰富的初步数据，这种适应性行为不是 这归因于响应系统本质上是全有或全无的。同样的数据表明， 计算是在小脑进行的，而且事实上，很大程度上是在小脑皮层中完成的。的答复 浦肯野细胞是小脑皮层的唯一输出，也是全有或全无的，并通过一个又一个的试验来跟踪行为。 试用在三个具体的目标，我们建议确定这种自适应计算的机制。我们将 第一个完整的记录研究，确定在广泛的条件下， 小脑皮质的处理与小脑深核的处理。第二，我们将使用 小脑皮层神经元的记录，以确定使反应不确定的机制， 随机输入特别是，我们将确定是否有重大差异的投入， 浦肯野细胞的反应与非反应试验，我们将确定是否新发现的 浦肯野细胞和篮状细胞之间的连接在放大或阈值化这些差异中起作用 使反应随机化。最后，我们将使用可逆灭活技术的组合， 浦肯野细胞的记录，以检验新发现的小脑深核侧支的假设， 神经元实现一种形式的传出复制反馈，即使对于 这些输入是如此不同，以至于它们很少引起回应。完成这些研究将确定 小脑的一种新的计算适应机制与应对噪音或不确定性有关 输入。这些机制可能是神经病理学病因学的一个未被充分认识的类别，因为 大多数实验方法试图消除尽可能多的噪音或可变性， 可能因此，这些研究代表了一种新的和创新的方法来研究大脑的机制， 功能和功能障碍。

Abstract/Project Summary The cerebellum is known to be a key part of the brain's sensory-motor processing. The basic features of cerebellar learning mechanisms are well characterized. This learning is the service of improving feed- forward predictions, which for much of the cerebellum contribute to the accuracy of movements. This proposal is about how the cerebellum copes with noisy inputs and is based on extensive preliminary data demonstrating a novel computation implemented by the cerebellum that makes its responses to noisy inputs more adaptive. We begin by training the cerebellum to output a response of a target size to a particular pattern of its mossy fiber inputs. By systematically varying the dissimilarity of probe mossy fiber inputs we see that the cerebellum does not decrease response amplitude as the dissimilarity increases. Doing so would be non-adaptive, as there is no prior experience that these smaller amplitudes are correct. Instead, the cerebellum decreases the likelihood of making a response as the dissimilarity increases, but when it does respond it almost always produces the correct amplitude. We present abundant preliminary data that this adaptive behavior is not attributable to the response system being inherently all-or-none. The same data demonstrate that this computation is cerebellar and is, indeed, largely accomplished in the cerebellar cortex. The responses of Purkinje cells, the sole output of the cerebellar cortex, are also all-or-none and track the behavior on a trial-by- trial basis. In three specific aims, we propose to identify the mechanisms of this adaptive computation. We will first complete recording studies that identify over a wide range of conditions the relative contributions of processing in the cerebellar cortex versus processing in the cerebellar deep nuclei. Second, we will use recordings from cerebellar cortex neurons to determine the mechanisms that make responding to uncertain inputs stochastic. In particular, we will determine whether there are significant differences in inputs to the Purkinje cells on response versus non-response trials and we will determine whether newly discovered connectivity between Purkinje cells and basket cells play a role in amplifying or thresholding these differences to make responding stochastic. Finally, we will use a combination of reversible inactivation techniques and recordings of Purkinje cells to test the hypothesis that newly discovered collaterals of deep cerebellar nucleus neurons implement a form of efference copy feedback that enforce the proper response amplitude even for inputs that are so different that they rarely elicit a response. Completion of these studies will identify the mechanisms of a novel computational adaptation of the cerebellum related to coping with noisy or uncertain inputs. Such mechanisms may be an under-appreciated category of etiology of neural pathologies, owing largely to the fact that most experimental approaches seek to eliminate as much noise or variability as possible. As such, these studies represent a novel and innovative approach to the mechanisms of brain function and dysfunction.