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Active learning of cortical connectivity from two-photon imaging data.

Martín A Bertrán1, Natalia L Martínez1, Ye Wang2

  • 1Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America.

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|May 3, 2018
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Summary

This study introduces an active learning method to efficiently map neural network connectivity using two-photon imaging. The approach refines network inference by focusing on key areas, enabling faster discovery and providing confidence intervals.

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Area of Science:

  • Systems Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Understanding neural network connectivity is crucial in systems neuroscience.
  • Perturbing network activity aids in identifying neural connections.
  • Efficiently inferring network structure from population activity is challenging.

Purpose of the Study:

  • To develop a novel method for inferring sparse connectivity graphs from in-vivo two-photon imaging data.
  • To introduce an active learning approach for optimal network refinement.
  • To enable efficient identification of neural network connectivity.

Main Methods:

  • In-vivo two-photon imaging of neural population activity.
  • Active learning framework with an incrementally learned recommended distribution.
  • Inference of sparse connectivity graphs from stimulus-evoked activity.
  • Simulations on artificial small-world networks and application to real data.

Main Results:

  • The active learning method focuses on key undiscovered network areas for faster inference.
  • The approach provides confidence intervals for inferred network parameters.
  • Analysis of real cortical data suggests a small-world topology.

Conclusions:

  • Active learning offers an efficient strategy for inferring neural network connectivity.
  • The developed method accelerates network identification and enhances parameter estimation.
  • Cortical networks exhibit characteristics consistent with small-world topology.