Understanding the relationship between structure and function of cortical neurons and circuits is one of the key challenges in neuroscience. For inhibitory neurons, roughly 15 subtypes are well characterized and we know a fair bit about their function. However, the vast majority of neocortical neurons are excitatory. Yet we know little about how differences in the morphology of excitatory neurons relate to their computational properties in vivo. In this project we hypothesize that there is a close correspondence between morphology and function of excitatory neurons: distinct subtypes can be identified not only by their morphological features, but also by how they respond to stimulation with natural stimuli. To test this hypothesis, we build upon recent advances in machine learning and develop a data-driven approach to derive a “bar code” for each neuron: a low-dimensional representation of its morphological features and its response properties to natural stimuli. Using these techniques, we tackle the structure-function question by harnessing a large-scale functional anatomy dataset: a combination of electron-microscopy reconstructions at sub-micrometer resolution with two-photon functional imaging of nearly all excitatory neurons in one cubic millimeter of the mouse visual cortex. If successful, our project could fundamentally change our view on the diversity of excitatory cell types and reveal how morphological features are linked to a neuron’s computational output. It could pave the way towards a unified definition of cell types, one of the fundamental building blocks of the brain. The same approach could be used in other brain areas and even other cellular systems beyond the brain. More broadly, while machine learning is promising to transform the scientific discovery process as a whole, our project could serve as a prime example of this transformation process in neuroscience and show how machine learning can help to discover structure in nature.
