Wu Tsai Neurosciences Institute Seminar Series Presents
Mark Harnett, PhD
Associate Professor, Department of Brain and Cognitive Sciences at The McGovern Institute for Brain Research, MIT.
Host: Cosmos Wang
The biological substrates of computation in the cortex remain elusive. While the remarkable recent progress of artificial neural systems has generated important insights into cortical function, these networks employ highly simplified model neurons. Real cortical neurons receive thousands of synaptic inputs distributed across extensive dendritic arbors that exhibit highly nonlinear properties, providing opportunities for subcellular operations before final integration and output at the axon. Neuronal units endowed with nonlinear dendritic processing could provide their respective networks with increased power, flexibility, and/or efficiency. However, the actual contributions of dendrites to cortical computations underlying behavior are unclear: dendritic mechanisms may not be recruited during relevant in vivo circuit activity or may only serve to compensate for other biological constraints. In this talk, I will discuss my lab’s recent progress in evaluating the biophysical substrates, engagement, and utility of dendritic processing in the mammalian cortex. I will present a multidisciplinary synapses-to-systems analysis of the mouse retrosplenial cortex that aims to connect biophysical processes at the level of single cells to the computations carried out by neuron populations during ethologically relevant navigational behavior. I will also describe how this avenue of research has led my lab to explore new mechanisms for adult cortical plasticity. Finally, I will discuss our systematic effort to understand how biophysical properties (ion channels and dendritic morphology) shape cortical neuron input-output functions across the phylogenetic tree of mammals, from Etruscan shrews to humans. We hope to use the results from these lines of inquiry to understand general principles of cortical design and function, and to facilitate the development of new artificial neural networks with enhanced capabilities.