Please welcome our guest speaker and FRF Candidate, Laureline Logiaco, Columbia University.
Title: Neural network mechanisms of flexible, robust & efficient cognitive motor control
Abstract: One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales in order to efficiently shape structured behaviors. I will present research elucidating how these complex computations are performed in the mammalian brain, with an emphasis on autonomous motor control. After briefly mentioning research on the mechanisms underlying high-level planning, I will focus on the efficient interface of these high-level control commands with motor cortical dynamics to drive muscles. I will notably take advantage of the fact that the anatomy of the circuits underlying the latter computation is better known. Specifically, I will show how these architectural constraints lead to a principled understanding of how the combination of hardwired circuits and strategically positioned plastic connections located within loops can create a form of efficient modularity. I will show that this modular architecture can balance two different objectives: first, supporting the flexible recombination of an extensible library of re-usable motor primitives; and second, promoting the efficient use of neural resources by taking advantage of shared connections between modules. I will finally show that these insights are relevant for designing artificial neural networks able to flexibly and robustly compose hierarchical continuous behaviors from a library of motor primitives.