Defining computation and connectivity in neuronal population activity underlying motor learning

神经网络结构限制了大脑的活动动力学。具体来说，由先前动作的结果引导的运动学习导致运动皮层网络及其活动的适应。为了在细胞水平上理解这些机制，需要在学习熟练动作的过程中，在毫秒级的时间尺度上同时记录数百个局部神经元。我们已经成功地建立了一种方法，使用最近开发的高密度电极硅探针结合基于机器学习的运动学分析和细胞类型特异性光遗传学调制，同时记录小鼠运动区域上的数千个神经元。2022）和我们在皮质微电路方面的经验（Peng等人，2019年，2022年），我们的目标是确定神经元微电路的核心变化，这些变化是运动学习和执行的基础。我们将开发新的方法来提取反映局部突触水平上的塑性变化的活动签名，并对这些签名如何约束神经元群体活动的整体维度进行建模。研究结果将提供对运动回路学习的微回路水平的理解，并为研究高密度电生理记录中的神经网络结构奠定基础。

Neural network structure constrains the activity dynamics of the brain. Specifically, learning of movements guided by the outcome of previous actions leads to adaptations in the motor cortical network and its activity. To understand these mechanisms on the cellular level would require simultaneous recordings from hundreds of local neurons at millisecond timescale in vivo during learning of a skilled movement. We have successfully established an approach to simultaneously record thousands of neurons across motor regions in mice, using recently developed high-density electrode silicon-probes in combination with machine-learning based kinematic analysis and cell-type specific optogenetic modulation.Motivated by recent work that link structure of population activity to the underlying synaptic connectivity (Dahmen et al., 2022) and our experience in cortical microcircuits (Peng et al., 2019, 2022), we aim to identify core changes in neuronal microcircuits that underlie motor learning and execution. We will develop novel approaches to extract activity signatures reflecting plastic changes on the local synaptic level and model how these constrain the overall dimensionality of neuronal population activity. The results will provide a microcircuit level understanding of learning in motor circuits and lay the groundwork to study neural network architecture in high-density electrophysiological recordings.