SOURCE-LINKED INTELLIGENCE

Deep Neuron Embeddings: Data-driven multi-modal discovery of cell types in the neocortex

CORDIS · observation · Publication date unknown

ct 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, I will 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, I will 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, my 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 ev

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR