Transcriptomic cell type structures in vivo neuronal activity across multiple timescales

Aidan Schneider, Mehdi Azabou, Louis McDougall-Vigier, David F. Parks, Sahara Ensley, Kiran Bhaskaran-Nair, Tomasz Nowakowski, Eva L. Dyer, Keith B. Hengen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.

Original languageEnglish
Article number112318
JournalCell Reports
Volume42
Issue number4
DOIs
StatePublished - Apr 25 2023

Keywords

  • CP: Neuroscience
  • cell types
  • deep learning
  • electrophysiology
  • multihead attention
  • optophysiology
  • transcriptomics
  • visual cortex

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