Date(s) - 10/27/2014
Our ultimate understanding of the brain as a complex system is reflected in our ability to predict its dynamics in the normal state and eventually control these dynamics in the pathological state. Interest in reverse engineering the brain – the process of identifying its building blocks and revealing how they’re networked together – has been surging, primarily due to striking advances in neural interface technology intended to measure and manipulate brain dynamics at exceedingly high temporal and spatial resolutions, and to characterize the ever changing interplay between the brain’s structure and function.
In this talk, I will discuss our recent efforts to characterize the neural ensemble correlates of somatosensory and motor coding in the brain, and demonstrate how inferring the connectivity between constituents of an ensemble may be key to rapid learning of neural decoders that translate thoughts of neurologically impaired subjects into reaching and grasping behavior of natural or artificial limbs. Complementing this view is our framework to reverse engineer the thalamocortical pathway in order to facilitate perceptual learning of artificial sensory feedback such as touch and proprioception through electrical or optogenetic stimulation. I will conclude by shedding some light on key questions that have to be addressed for brain-machine interfaces to have a long lasting impact on basic and clinical neuroscience.