4.6 Article

An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo

期刊

PLOS BIOLOGY
卷 19, 期 5, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pbio.3001213

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资金

  1. National Institutes of Health [R01NS39600, U01MH114829]
  2. Spanish Ministerio de Economia y Competitividad (MINECO) [RTI2018-098581-B-I00, FJCI-2017-32719]
  3. Academy of Finland [275954]
  4. European Molecular Biology Organization [EMBO ALTF 1161-2017]
  5. Human Frontiers Science Program [LT0000717/2018]
  6. Academy of Finland (AKA) [275954, 275954] Funding Source: Academy of Finland (AKA)

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This study focuses on understanding the dynamics of different brain-wide subcircuits by linking basic behavioral traits to cell-type specific dynamics. By mining literature and adding new data, the researchers explore the equivalences and differences across conditions, and offer novel observations. They also discuss the potential for classifying extracellular recordings of single cells and integrating single-cell phenotypes with circuit function in the future.
Understanding brain operation demands linking basic behavioral traits to cell-type specific dynamics of different brain-wide subcircuits. This requires a system to classify the basic operational modes of neurons and circuits. Single-cell phenotyping of firing behavior during ongoing oscillations in vivo has provided a large body of evidence on entorhinal-hippocampal function, but data are dispersed and diverse. Here, we mined literature to search for information regarding the phase-timing dynamics of over 100 hippocampal/entorhinal neuron types defined in . We identified missing and unresolved pieces of knowledge (e.g., the preferred theta phase for a specific neuron type) and complemented the dataset with our own new data. By confronting the effect of brain state and recording methods, we highlight the equivalences and differences across conditions and offer a number of novel observations. We show how a heuristic approach based on oscillatory features of morphologically identified neurons can aid in classifying extracellular recordings of single cells and discuss future opportunities and challenges towards integrating single-cell phenotypes with circuit function.

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