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CATNIP Lab is a computational and statistical neuroscience group. Our goal is to obtain an effective systems-level description of relevant neural dynamics in the context of cognitive functions and dysfunctions. To arrive at a model of neural computation tightly tied to biology and experimental observations, we work closely with experimental and clinical collaborators. We develop probabilistic methods for analyzing spatiotemporal neural and non-neural time series to infer neural dynamics models. To facilitate the scientific inference process, we develop real-time machine learning and control methods and design next-generation experiments.
See research page for details.
{% comment %} CATNIP Lab is a computational and statistical neuroscience group located in Stony Brook University. We design statistical models and machine learning methods specialized for analyzing neural data. We aim to understand how information and computations are represented and implemented in the brain, both at a single-neuron and systems level. We collaborate with experimental labs on important problems in neuroscience, such as sensory coding and perceptual decision-making. See research page for details.
[//]: ### Collaborators
[//]: * Alex Huk (University of Texas at Austin) [//]: * Jacob Yates (University of Texas at Austin) [//]: * Yuriy Bobkov (Whitney Marine Biology Lab) [//]: * Evan Archer (Columbia University) [//]: * Lars Büsing (Columbia University) [//]: * Jonathan Pillow (Princeton University) [//]: * Anqi Wu (Princeton University) [//]: * Steven Van Vaerenbergh (University of Cantabria) [//]: * Sohan Seth (University of Edinburgh)
We are located on the 5th floor of Life Sciences Building (Room 550) at Stony Brook University, New York. {% endcomment %}