Defining computation and connectivity in neuronal population activity underlying motor learning

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.