neuronal classification
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What transcriptomics tells us about retinal development, disease and evolution
Classification of neurons, long viewed as a fairly boring enterprise, has emerged as a major bottleneck in analysis of neural circuits. High throughput single cell RNA-seq has provided a new way to improve the situation. We initially applied this method to mouse retina, showing that its five neuronal classes (photoreceptors, three groups of interneurons, and retinal ganglion cells) can be divided into 130 discrete types. We then applied the method to other species including human, macaque, zebrafish and chick. With the atlases in hand, we are now using them to address questions about how retinal cell types diversify, how they differ in their responses to injury and disease, and the extent to which cell classes and types are conserved among vertebrates.
Improving the Neuronal Classification Capacity with Nonlinear Parallel Synapses
Bernstein Conference 2024
Parallel synapses with transmission nonlinearities increase the neuronal classification capacity
COSYNE 2023
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