analyzing and explaining electrophysiological data
analyzing and explaining human behavior
Making sense of data is possibly the biggest problem in Neuroscience and beyond. We build algorithms to analyze data. We also use theory as well as computational and neural modeling to understand how information is processed in the nervous system, explaining data obtained in collaboration with electrophysiologists and in psychophysical experiments. Lastly, we constrain and develop new technologies aimed at obtaining data about brains. Our conceptual work addresses information processing in the nervous system from two angles: (1) By analyzing and explaining electrophysiological data, we study what neurons do. (2) By analyzing and explaining human behavior, we study what all these neurons do together. Much of our work looks at these questions from a normative or causal viewpoint, asking what problems the nervous system should be solving. This often means taking a Bayesian approach. Bayesian decision theory is the systematic way of calculating how the nervous system may make good decisions in the presence of uncertainty. Causal inference from observational data promises to be a key enabler for progress in science.
For love of neuroscience: The Neuromatch movement Konrad Paul Kording Neuron, 2021 (Article)
Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study Jonah Meyerhoff, Tony Liu, Konrad P Kording, Lyle H Ungar, Susan M Kaiser, Chris J Karr, David C Mohr Journal of Medical Internet Research, 2021 (Article)