Active learning of cortical connectivity from two-photon imaging data.
Journal Article (Journal Article)
Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.
Full Text
Duke Authors
Cited Authors
- Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D
Published Date
- January 2018
Published In
Volume / Issue
- 13 / 5
Start / End Page
- e0196527 -
PubMed ID
- 29718955
Pubmed Central ID
- PMC5931643
Electronic International Standard Serial Number (EISSN)
- 1932-6203
International Standard Serial Number (ISSN)
- 1932-6203
Digital Object Identifier (DOI)
- 10.1371/journal.pone.0196527
Language
- eng