<p>Understanding the input-output function of principal cortical neurons and their role in network dynamics is a key milestone in decoding how information is represented and processed in the cortex. Pyramidal neurons act as complex computational units, integrating the activity of thousands of synaptic inputs and transforming them into output patterns. These computations are primarily carried out within an elaborate dendritic tree, which receives extensive synaptic input and converts it into a neural code. However, the nature of dendritic computations in vivo during behaviorally relevant tasks remains unclear.</p><p>In this talk, I will present our recent findings on the dendritic mechanisms used by layer 5 pyramidal tract (PT) neurons to encode motor information in vivo during various motor tasks. Using two-photon calcium imaging in head-fixed mice, along with a custom experimental and analytical pipeline, we achieved unprecedented resolution in correlating dendritic structure with function. I will discuss how different types of PT neurons process and represent motor information, how these properties are shaped during learning, and the role of thalamocortical inputs in modulating both learning and representation.</p>

<p>We can store and retrieve specific patterns of activity in network models through synaptic plasticity mechanisms. When the synapses between cells in these models are bounded, then encoding a new pattern necessarily implies the partial erasure of previously stored ones. This overwriting or “palimpsest” property of networks with biologically constrained synapses has been studied intensively over the past 30 years. Most theoretical studies have focused on mechanisms for improving the memory capacity in such networks, which is starkly degraded through overwriting. However, there is another property of these memory systems which has not yet been fully explored. Namely, in the context of sensorydriven activity, ongoing learning can lead to the overwriting of some fraction of the synapses. This in turn leads to changes in the output of the network at any two distinct points in time, even if the input patterns have remained unchanged. This effect is reminiscent of the phenomenon of representational drift (RD), which has by now been wellestablished in the hippocampus, and other cortical areas. Recent experimental work has brought to light a number of puzzling findings regarding RD, which seem to defy simple explanation. These include the discovery that repetition rate can both reduce drift (in piriform cortex) and increase it (in hippocampus). In hippocampal place cells, RD has been shown to have differential effects on overall firing rates and spatial tuning. This suggests that there may be distinct underlying mechanisms. I will discuss how all of these findings are, in fact, consistent with the changes in activity observed in networks which store patterns through Hebbian plasticity. The fundamental assumption in such models is that memory storage is ongoing, and occurs between experimental sessions. The array of distinct and sometimes seemingly contradictory findings can be accounted for by differences in learning rates and correlations between input patterns.</p>