Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland.
J Neurophysiol. 2020 Dec 1;124(6):2022-2051. doi: 10.1152/jn.00449.2020. Epub 2020 Oct 28.
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.
小脑类似于前馈的、具有三层神经元的网络，其中“隐藏层”由浦肯野细胞（P 细胞）组成，输出层由小脑深部核团（DCN）神经元组成。在这种类比中，每个 DCN 神经元的输出都是一个预测，与实际观察结果进行比较，从而产生源自下橄榄核的误差信号。有效的学习需要误差信号到达 DCN 神经元以及投射到它们的 P 细胞。然而，小脑学习的基本规则被违反了：橄榄核到 DCN 的投射很弱，尤其是在成年期。相反，一个来自橄榄核的非常强的信号被发送到 P 细胞，产生复杂的尖峰。奇怪的是，P 细胞被分组到小的群体中，汇聚到单个 DCN 神经元。为什么 P 细胞以这种方式组织，每个群体的成员标准是什么？在这里，我应用机器学习的基本数学方法，并考虑到形成一个群体的 P 细胞表现出一种特殊属性的事实：它们可以同步它们的复杂尖峰，这反过来又抑制了它们投射到的 DCN 神经元的活动。因此，复杂的尖峰不仅可以作为 P 细胞的教学信号，而且通过复杂的尖峰同步，一个 P 细胞群体可以作为产生错误输出的 DCN 神经元的替代教师。将 P 细胞分组为共享对错误的偏好的小群体似乎满足了有效学习的一个关键要求：将错误信息提供给负责错误的输出层神经元（DCN），以及对其有贡献的隐藏层神经元（P 细胞）。这种群体编码可能解释了学习过程中几种显著的行为特征，包括多个时间尺度、防止擦除以及记忆的自发恢复。
