David Klindt
Assistant Professor
Ph.D., University of Tübingen, Germany, 2021
klindt@cshl.edu | 516-367-5510
Faculty ProfileOur research explores how biological systems, such as the brain, learn from sensory data and generalize knowledge to new situations, inspiring the development of more robust artificial intelligence models. By investigating neural representations and leveraging expertise in computational neuroscience and AI, we aim to uncover groundbreaking insights at the intersection of biology and technology.
The research of our group delves into the intersection of biological systems and artificial intelligence (AI), aiming to understand how organisms, particularly the brain, process sensory information and generalize knowledge across different contexts. By investigating how brains learn structured representations from limited and dynamic inputs, the research sheds light on fundamental questions regarding biological learning mechanisms and their potential application to AI models. Leveraging insights from computational neuroscience and AI, we explore the development of more robust AI algorithms that can mimic the efficient learning and inference mechanisms observed in biological systems. This interdisciplinary approach not only advances our understanding of biological perception but also holds promise for the next generation of AI technology, with implications for various fields beyond neuroscience and AI.
All Publications
Towards Interpretable Cryo-EM: Disentangling Latent Spaces of Molecular Conformations
19 Mar 2024 | bioRxiv
Klindt, David, Hyvärinen, Aapo, Levy, Axel, Miolane, Nina, Poitevin, Frédéric
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems
18 Oct 2023
Klindt, David, Sanborn, Sophia, Acosta, Francisco, Poitevin, Frédéric, Miolane, Nina
Efficient coding of natural scenes improves neural system identification
24 Apr 2023 | PLoS Computational Biology | 19(4):e1011037
Qiu, Yongrong, Klindt, David, Szatko, Klaudia, Gonschorek, Dominic, Hoefling, Larissa, Schubert, Timm, Busse, Laura, Bethge, Matthias, Euler, Thomas, Fleming, Roland
Controlling neural network smoothness for neural algorithmic reasoning
7 Dec 2022 | Transactions on Machine Learning Research
Klindt, David
A chromatic feature detector in the retina signals visual context changes
1 Dec 2022 | bioRxiv
Hoefling, Larissa, Szatko, Klaudia, Behrens, Christian, Qiu, Yongrong, Klindt, David, Jessen, Zachary, Schwartz, Gregory, Bethge, Matthias, Berens, Philipp, Franke, Katrin, Ecker, Alexander, Euler, Thomas
Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior
26 Nov 2022 | bioRxiv
Hermansen, Erik, Klindt, David, Dunn, Benjamin
Understanding neural coding on latent manifolds by sharing features and dividing ensembles
6 Oct 2022
Klindt, David
Natural environment statistics in the upper and lower visual field are reflected in mouse retinal specializations
9 Aug 2021 | Current Biology | 31(15):3233-3247.e6
Qiu, Yongrong, Zhao, Zhijian, Klindt, David, Kautzky, Magdalena, Szatko, Klaudia, Schaeffel, Frank, Rifai, Katharina, Franke, Katrin, Busse, Laura, Euler, Thomas
The temporal structure of the inner retina at a single glance
10 Mar 2020 | Scientific Reports | 10(1):4399
Zhao, Zhijian, Klindt, David, Maia Chagas, André, Szatko, Klaudia, Rogerson, Luke, Protti, Dario, Behrens, Christian, Dalkara, Deniz, Schubert, Timm, Bethge, Matthias, Franke, Katrin, Berens, Philipp, Ecker, Alexander, Euler, Thomas