Model-based Inference of a Directed Network of Circadian Neurons

Abstract

The suprachiasmatic nucleus (SCN) is the master clock of the brain. It is a network of neurons that behave like biological oscillators, capable of synchronizing and maintaining daily rhythms. The detailed structure of this network is still unknown, and the role that the connectivity pattern plays in the network’s ability to generate robust oscillations has yet to be fully elucidated. In recent work, we used an information theory–based technique to infer the structure of the functional network for synchronization, from bioluminescence reporter data. Here, we propose a computational method to determine the directionality of the connections between the neurons. We find that most SCN neurons have a similar number of incoming connections, but the number of outgoing connections per neuron varies widely, with the most highly connected neurons residing preferentially in the core.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 07, 2018
Source ID
10.1177/0748730418790402

Entities

People

  • David Mcbride
  • Linda Petzold

Organizations

  • Army Research Office
  • The Institute for Collaborative Biotechnologies
  • University of California, Santa Barbara

Tags

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Computer Networking
  • Molecular and Cellular Biology

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks