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
Affiliations
Aidan Schneider
Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
Mehdi Azabou
School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Louis McDougall-Vigier
Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
David F. Parks
Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
Sahara Ensley
Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
Kiran Bhaskaran-Nair
Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
Tomasz Nowakowski
Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
Eva L. Dyer
School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Keith B. Hengen
Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA; Corresponding author
Summary: 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.