# Most discriminative stimuli for functional cell type clustering

arXiv: https://arxiv.org/abs/2401.05342 · HTML: https://mlsanity.com/paper/2401.05342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Do Adversarially Robust ImageNet Models Transfer Better?](https://mlsanity.com/paper/2007.08489/) (2020)
- [Deep Learning Models of the Retinal Response to Natural Scenes](https://mlsanity.com/paper/1702.01825/) (2017)
- [Neural system identification for large populations separating "what" and "where"](https://mlsanity.com/paper/1711.02653/) (2017)
- [The Sensorium competition on predicting large-scale mouse primary visual cortex activity](https://mlsanity.com/paper/2206.08666/) (2022)
- [The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos](https://mlsanity.com/paper/2305.19654/) (2023)
- [Diverse feature visualizations reveal invariances in early layers of deep neural networks](https://mlsanity.com/paper/1807.10589/) (2018)

## Built on by (most cited first, 2 of 2)

- [Discriminating image representations with principal distortions](https://mlsanity.com/paper/2410.15433/) (2024)
- [Know Thyself by Knowing Others: Learning Neuron Identity from Population Context](https://mlsanity.com/paper/2512.01199/) (2025)

## Abstract (arXiv)

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 towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.