Active learning of cortical connectivity from two-photon imaging data

Investigators
Dario Ringach, Guillermo Sapiro, David Dunson
Contact info (email)
martin.bertran@duke.edu
1. Define context(s)
other
Current Conformance Level / Target Conformance Level
Adequate
Primary goal of the model/tool/database

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.

Biological domain of the model
Neuroscience
Structure(s) of interest in the model
Neurons in visual cortex
Spatial scales included in the model
Individual Neurons
Time scales included in the model
~0.1 seconds
2. Data for building and validating the model
Data for building the model Published? Private? How is credibility checked? Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal) Yes
in vivo pre-clinical (large animal)
Human subjects/clinical
Other: ________________________
Data for validating the model Published? Private? How is credibility checked? Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal) Yes
in vivo pre-clinical (large animal)
Human subjects/clinical
Other: ________________________
3. Validate within context(s)
Who does it? When does it happen? How is it done? Current Conformance Level / Target Conformance Level
Verification Primary Authors Throughout research Detailed inspection Adequate
Validation Primary Authors Throughout research Cross validation Adequate
Uncertainty quantification Primary Authors Throughout research Fisher information Adequate
Sensitivity analysis Primary Authors Throughout research Fisher information Adequate
Other:__________ Adequate
Additional Comments Adequate
4. Limitations
Disclaimer statement (explain key limitations) Who needs to know about this disclaimer? How is this disclaimer shared with that audience? Current Conformance Level / Target Conformance Level
Spiking times are inferred from noisy calcium traces Technical analists described in paper Adequate
5. Version control
Current Conformance Level / Target Conformance Level
Adequate
Naming Conventions? Repository? Code Review?
individual modeler
within the lab
collaborators
6. Documentation
Current Conformance Level / Target Conformance Level
Code commented? Adequate
Scope and intended use described? Adequate
User’s guide? Adequate
Developer’s guide? Adequate
7. Dissemination
Current Conformance Level / Target Conformance Level
Adequate
Target Audience(s): “Inner circle” Scientific community Public
Simulations Bertrán, Martín A et al. “Active learning of cortical connectivity from two-photon imaging data.” PloS one vol. 13,5 e0196527. 2 May. 2018, doi:10.1371/journal.pone.0196527
Models Bertrán, Martín A et al. “Active learning of cortical connectivity from two-photon imaging data.” PloS one vol. 13,5 e0196527. 2 May. 2018, doi:10.1371/journal.pone.0196527
Software Bertrán, Martín A et al. “Active learning of cortical connectivity from two-photon imaging data.” PloS one vol. 13,5 e0196527. 2 May. 2018, doi:10.1371/journal.pone.0196527
Results Bertrán, Martín A et al. “Active learning of cortical connectivity from two-photon imaging data.” PloS one vol. 13,5 e0196527. 2 May. 2018, doi:10.1371/journal.pone.0196527
Implications of results Bertrán, Martín A et al. “Active learning of cortical connectivity from two-photon imaging data.” PloS one vol. 13,5 e0196527. 2 May. 2018, doi:10.1371/journal.pone.0196527
8. Independent reviews
Current Conformance Level / Target Conformance Level
Adequate
Reviewer(s) name & affiliation: Public Library of Science reviewers
When was review performed? before publication
How was review performed and outcomes of the review? results were published
9. Test competing implementations
Current Conformance Level / Target Conformance Level
Adequate
Yes or No (briefly summarize)
Were competing implementations tested? Yes
Did this lead to model refinement or improvement? Yes
10. Conform to standards
Current Conformance Level / Target Conformance Level
Adequate
Yes or No (briefly summarize)
Are there operating procedures, guidelines, or standards for this type of multiscale modeling? Yes
How do your modeling efforts conform?