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Flocking Method for Identifying of Neural Circuits in Optogenetic Datasets

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Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

  • 13 Accesses

Abstract

This work introduces a new approach to spatial analysis of brain activity in optogenetics datasets based on application of flocking method for an identification of stable neuronal activity locations. Our method uses a multiple local directivity and interaction in neuronal activity paths. It can be seen as a flocking behaviour that promotes sustainable structuration because they use collective information to move. We processed sets of mouse brain images obtained by light-sheet fluorescence microscopy method. Location variations of neural activity patterns were calculated on the basis of flocking algorithm. An important advantage of using this method is the identification of locations where a pronounced directionality of neuronal activity trajectories can be observed in a sequence of several adjacent slices, as well as the identification of areas of through intersection of activities. The trace activity of neural circuits can affect parameters of subsequent activation of neurons occurring in the same locations. We analyzed neuronal activity based on its distributions from slice to slice obtained with a time delay. We used GDAL Tools and LF Tools in QGIS for geometric and topological analysis of multi-page TIFF files with optogenetics datasets. As a result, we were able to identify localizations of sites with small movements of group neuronal activity passing in the same locations (with retaining localization) from slice to slice over time.

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Moscow, Russia

    Margarita Zaleshina

  2. Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia

    Alexander Zaleshin

Authors
  1. Margarita Zaleshina
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  2. Alexander Zaleshin
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Correspondence to Margarita Zaleshina .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Zaleshina, M., Zaleshin, A. (2024). Flocking Method for Identifying of Neural Circuits in Optogenetic Datasets. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

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