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Thomas Euler

5 accepted papers

2025

A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations

NeurIPS 2025poster

Visual processing starts in the outer retina where photoreceptors transform light into electrochemical signals. These signals are modulated by inhibition from horizontal cells and sent to the inner retina via excitatory bipolar cells. The outer retina is thought to play an important role in contrast…

Cited by 0SourceScholar
2024

Most discriminative stimuli for functional cell type clustering

ICLR 2024poster

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towar…

2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

NeurIPS 2021spotlight

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the…

2020

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

NeurIPS 2020spotlight

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnosti…

2017

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

NeurIPS 2017poster

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”. Learning deep convolutional feature spaces that are sh…