Konrad Kording seeks to understand the brain as a computational device. He sees considerable limitations in the standard way with which neuroscience studies the brain and how to mine neural data for causal relations. Deep learning provides an alternative way of thinking about brains that focuses on cost functions, optimization algorithms, and specialized structures. Working towards a deep learning-based view of the brain, the Kording lab broadly uses data analysis methods, including machine learning, to ask fundamental questions.
Large NIH TR01 grant to study neural recording onto DNA
PIK Professor, UPenn
Co-leader (past) of Cosyne, CCN, NSF neural data science, and NIPS big brain workshop
(past) Senior management team of Rehabilitation Institute of Chicago (Hospital)
J.I. Glaser, R.H. Chowdhury, M.G. Perich, L.E. Miller and K.P. Kording, "Machine learning for neural decoding," arXiv preprint.
I. Vilares and K.P. Kording, "Dopaminergic Medication Increases Reliance on Current Information in Parkinson's Disease," Nature Human Behaviour 1 (8), 0129.
S. Saeb, L. Lonini, A. Jayaraman, D.C. Mohr and K.P. Kording, "The need to approximate the use-case in clinical machine learning," GigaScience 6 (5), 1-9.
E. Jonas and K.P. Kording, "Could a neuroscientist understand a microprocessor?," PLoS computational biology 13 (1), e1005268.
J.I. Glaser, M.G. Perich, P. Ramkumar, L.E. Miller and K.P. Kording, "Population Coding Of Conditional Probability Distributions In Dorsal Premotor Cortex," bioRxiv, 137026.
Associate Fellow Learning in Machines & Brains
University of PennsylvaniaBioengineering and Neuroscience
Physics ETH Zurich
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