Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235
Department of Mechanical Engineering, University of Colorado Boulder, Boulder Colorado 80309.
J Neurosci. 2023 Nov 8;43(45):7523-7529. doi: 10.1523/JNEUROSCI.1505-23.2023.
Rapid progress in our understanding of the brain's learning mechanisms has been accomplished over the past decade, particularly with conceptual advances, including representing behavior as a dynamical system, large-scale neural population recordings, and new methods of analysis of neuronal populations. However, motor and cognitive systems have been traditionally studied with different methods and paradigms. Recently, some common principles, evident in both behavior and neural activity, that underlie these different types of learning have become to emerge. Here we review results from motor and cognitive learning, relying on different techniques and studying different systems to understand the mechanisms of learning. Movement is intertwined with cognitive operations, and its dynamics reflect cognitive variables. Training, in either motor or cognitive tasks, involves recruitment of previously unresponsive neurons and reorganization of neural activity in a low dimensional manifold. Mapping of new variables in neural activity can be very rapid, instantiating flexible learning of new tasks. Communication between areas is just as critical a part of learning as are patterns of activity within an area emerging with learning. Common principles across systems provide a map for future research.
在过去的十年中,我们对大脑学习机制的理解取得了迅速的进展,特别是在概念上的进展,包括将行为表示为动力系统、大规模神经群体记录和神经元群体分析的新方法。然而,运动和认知系统传统上是用不同的方法和范式进行研究的。最近,一些共同的原则,在行为和神经活动中都很明显,这些不同类型的学习的基础已经开始出现。在这里,我们回顾了来自运动和认知学习的结果,依赖于不同的技术和研究不同的系统来理解学习的机制。运动与认知操作交织在一起,其动态反映了认知变量。无论是在运动还是认知任务的训练中,都会涉及到以前无反应的神经元的招募,以及在低维流形中重新组织神经活动。在神经活动中映射新变量可以非常迅速,实现新任务的灵活学习。区域之间的通信与区域内活动模式一样,是学习的关键部分。跨系统的共同原则为未来的研究提供了一个蓝图。