Identifying Interpretable Visual Features in Artificial and Biological Neural Systems

no code implementations • 17 Oct 2023 • David Klindt, Sophia Sanborn, Francisco Acosta, Frédéric Poitevin, Nina Miolane

Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features.

Disentanglement

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

1 code implementation • NeurIPS 2021 • Dominic Gonschorek, Larissa Höfling, Klaudia Szatko, Katrin Franke, Timm Schubert, Benjamin Dunn, Philipp Berens, David Klindt, Thomas Euler

Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience.

Domain Adaptation

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

1 code implementation • NeurIPS 2020 • Cornelius Schröder, David Klindt, Sarah Strauss, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens

Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms.

Blocking

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

1 code implementation • ICLR 2021 • David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan Paiton

We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos.

Disentanglement

Neural system identification for large populations separating “what” and “where”

1 code implementation • NeurIPS 2017 • David Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge

Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”.