Most discriminative stimuli for functional cell type clustering

TL;DR

This paper introduces an optimization-based clustering method using deep predictive models to identify functional neuron types through Most Discriminative Stimuli, enabling fast, unbiased, and interpretable classification across species and brain regions.

Contribution

It presents a novel approach combining stimulus optimization and clustering to discover functional neuron types without prior biases or extensive training.

Findings

Successfully recovers functional clusters in multiple species and brain areas.

Generates interpretable stimuli that distinguish neuron types.

Operates efficiently without large natural scene datasets.

Abstract

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.