October 13, 2017

Colloquium Speaker: John Krakauer


Dr. John Krakauer is currently John C. Malone Professor of Neurology, Neuroscience, and Physical Medicine and Rehabilitation, and Director of the Brain, Learning, Animation, and Movement Lab (www.BLAM-lab.org) at The Johns Hopkins University School of Medicine. His areas of research interest are: (1) Experimental and computational studies of motor control and motor learning in humans (2) Tracking long-term motor skill learning and its relation to higher cognitive processes such as decision-making. (3) Prediction of motor recovery after stroke (4) Mechanisms of spontaneous motor recovery after stroke in humans and in mouse models (5) New neuro-rehabilitation approaches for patients in the first 3 months after stroke.  Dr. Krakauer is also co-founder of the video gaming company Max and Haley, and of the creative engineering Hopkins-based project named KATA. KATA and M&H are both predicated on the idea that animal movement based on real physics is highly pleasurable and that this pleasure is hugely heightened when the animal movement is under the control of our own movements. A simulated dolphin and other cetaceans developed by KATA has led to a therapeutic game, interfaced with an FDA-approved 3D exoskeletal robot, which is being used in an ongoing multi-site rehabilitation trial for early stroke recovery. Dr. Krakauer’s book, “Broken Movement: The Neurobiology of Motor Recovery after Stroke” will be published by the MIT Press in the autumn of 2017.




Colloquium Topic: What Are We Asking When We Ask How The Brain Works

There are ever more compelling tools available for neuroscience research, ranging from selective genetic targeting to optogenetic circuit control to mapping whole connectomes. These approaches are coupled with a deep-seated, often tacit, belief in the reductionist program for understanding the link between the brain and behavior. The aim of this program is causal explanation through neural manipulations that allow testing of necessity and sufficiency claims. We argue, however, that another equally important approach seeks an alternative form of understanding through careful theoretical and experimental decomposition of behavior. Specifically, the detailed analysis of tasks and of the behavior they elicit is best suited for discovering component processes and their underlying algorithms. In most cases, we argue that study of the neural implementation of behavior is best investigated after such behavioral work. Thus, we advocate a more pluralistic notion of neuroscience when it comes to the brain-behavior relationship: behavioral work provides understanding, whereas neural interventions test causality.