Ecker, Alexander

Authors Nellen NS, Turishcheva P, Vystrčilová M, Sridhar S, Gollisch T, Tolias AS, Ecker AS Journal Arxiv Citation arXiv:2506.03293. Abstract Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from

Authors Vystrčilová M, Sridhar S, Burg MF, Khani MH, Karamanlis D, Schreyer HM, Ramakrishna V, Krüppel S, Zapp SJ, Mietsch M, Gollisch T,Ecker AS Journal BioRxiv Citation bioRxiv 2025.04.09.647910. Abstract The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, or motion. However, it

Authors Schneider AC, Neuhaus V, Ehrlich DA, Makkeh A, Ecker AS, Priesemann V, Wibral M Journal Arxiv Citation arXiv:2412.02482. Abstract In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local

Authors Sridhar S, Vystrčilová M, Khani MH, Karamanlis D, Schreyer HM, Ramakrishna V, Krüppel S, Zapp SJ, Mietsch M, Ecker A, Gollisch T Journal BioRxiv Citation bioRxiv 2024.03.05.583449. Abstract Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields.

Authors Vystrčilová M, Sridhar S, Burg MF, Gollisch T, Ecker AS Journal BioRxiv Citation bioRxiv 2024.03.06.583740. Abstract The diverse nature of visual environments demands that the retina, the first stage of the visual system, encodes a vast range of stimuli with various statistics. The retina adapts its computations to some

Authors Burg MF, Zenkel T, Vystrčilová M, Oesterle J, Höfling L, Willeke KF, Lause J, Müller S, Fahey PG, Ding Z, Restivo K, Sridhar S, Gollisch T, Berens P, Tolias AS, Euler T, Bethge M, Ecker AS Journal ArXiv Citation arXiv:2401.05342. Abstract Identifying cell types and understanding their functional properties