David Alexander Klindt

The paper identifies interpretable and diverse visual features from neural population codes in macaque datasets and shows their causal role in behavior.

The paper introduces latent processing units (LPUs) to explain how neural networks perform reliable computations despite variability, using six theorems to unify geometric and dynamical perspectives.

The paper identifies a retinal ganglion cell type that detects visual context changes by responding selectively to chromatic contrast transitions.

This paper frames the challenge of interpreting cryo-EM latent spaces as an Independent Component Analysis problem, aiming to develop identifiable models for conformational landscapes.

The paper shows that grid cell population activity forms a 2-D toroidal state space during 1-D behavior, using UMAP and persistent homology to analyze topology.

This study improves neural system identification by incorporating efficient coding principles, showing better performance and biologically plausible filters in retinal neuron response prediction.

This paper develops a domain adaptation framework to remove inter-experimental variability in systems neuroscience data while preserving biological signals, showing improved performance over existing methods.

The paper uses a UV-green camera to study mouse visual environments and finds that natural scene statistics shape color discrimination and retinal processing in the upper visual field.

The paper develops a biophysically constrained computational model of inner retinal circuits that performs well on new stimuli and matches connectomics data.

The paper develops a method to image the entire inner plexiform layer of the retina at once, revealing response speed gradients in bipolar cells and isolating experimental batch effects.