An emergent temporal basis set robustly supports cerebellar time-series learning.
An emergent temporal basis set robustly supports cerebellar time-series learning.
复制标题
紧急时间基础集有力地支持小脑时间序列学习。
DOI：
10.1152/jn.00312.2022
复制
发表时间：
2023
影响因子：
2.5
通讯作者：
Person,AbigailL
中科院分区：
文献类型：
--
作者：
Gilmer,JesseI;Farries,MichaelA;Kilpatrick,Zachary;Delis,Ioannis;Cohen,JeremyD;Person,AbigailL
小脑被认为是一个“学习机器”，对于运动协调和其他行为的时间间隔估计至关重要。理论研究表明，小脑的输入接受结构，颗粒细胞层（GCL），执行模式分离的输入，促进浦肯野细胞（P细胞）的学习。然而，输入重新格式化和学习之间的关系仍然存在争议，强调模式分离功能从稀疏到去相关的作用。我们采取了一种新的方法，通过训练小脑皮层的极简模型来从时变输入中学习复杂的时间序列数据，这在运动过程中是典型的。该模型从这些输入中稳健地产生时间基集，并且所得到的GCL输出支持比单独的苔藓纤维更好地学习时间复杂的目标函数。学习在中等阈值水平下进行了优化，支持相对密集的颗粒细胞活动，但GCL群体活动中驱动学习的关键统计特征与以前在分类任务中看到的不同。这些发现推进可检验的假说的机制的时间基组的形成和预测，适度密集的人口活动优化learning.NEW &值得注意的是，在运动过程中，苔藓纤维输入到小脑中继随时间变化的信息与强大的内在关系正在进行的运动。这种苔藓纤维信号是否足以支持浦肯野信号和学习？在一个模型中，我们展示了GCL如何极大地提高了浦肯野学习的复杂，时间动态信号相对于苔藓纤维单独。学习优化的GCL群体活动是中等密度的，它保留了固有的输入方差，同时也进行模式分离。
The cerebellum is considered a “learning machine” essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum’s input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.NEW & NOTEWORTHYDuring movement, mossy fiber inputs to the cerebellum relay time-varying information with strong intrinsic relationships to ongoing movement. Are such mossy fibers signals sufficient to support Purkinje signals and learning? In a model, we show how the GCL greatly improves Purkinje learning of complex, temporally dynamic signals relative to mossy fibers alone. Learning-optimized GCL population activity was moderately dense, which retained intrinsic input variance while also performing pattern separation.