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.

翻译：识别细胞类型并理解其功能特性对于揭示感知和认知的机制至关重要。在视网膜中，功能性类型可以通过精心挑选的刺激来识别，但这需要专业领域知识，并且偏向于已知的细胞类型。在视觉皮层中，尚不清楚存在哪些功能类型以及如何识别它们。因此，为了无偏地识别视网膜和视觉皮层中的功能性细胞类型，需要新的方法。在此，我们提出一种基于优化的聚类方法，利用深度预测模型，通过最具判别性的刺激（MDS）获得神经元的功能聚类。我们的方法在刺激优化与聚类重新分配之间交替进行，类似于期望最大化算法。该算法在小鼠视网膜、狨猴视网膜以及猕猴视觉区域V4中恢复了功能聚类。这表明我们的方法能够成功地在不同物种、视觉系统阶段和记录技术中找到判别性刺激。由此产生的最具判别性的刺激可用于快速且即时地分配功能性细胞类型，而无需训练复杂的预测模型或展示大型自然场景数据集，从而为先前受限于实验时间的实验铺平了道路。关键的是，MDS是可解释的：它们将最明确识别特定类型神经元的独特刺激模式可视化。