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Generalized leaky integrate-and-fire models classify multiple neuron types

Author

Listed:
  • Corinne Teeter

    (Allen Institute for Brain Science)

  • Ramakrishnan Iyer

    (Allen Institute for Brain Science)

  • Vilas Menon

    (Allen Institute for Brain Science
    Howard Hughes Medical Institute, Janelia Research Campus)

  • Nathan Gouwens

    (Allen Institute for Brain Science)

  • David Feng

    (Allen Institute for Brain Science)

  • Jim Berg

    (Allen Institute for Brain Science)

  • Aaron Szafer

    (Allen Institute for Brain Science)

  • Nicholas Cain

    (Allen Institute for Brain Science)

  • Hongkui Zeng

    (Allen Institute for Brain Science)

  • Michael Hawrylycz

    (Allen Institute for Brain Science)

  • Christof Koch

    (Allen Institute for Brain Science)

  • Stefan Mihalas

    (Allen Institute for Brain Science)

Abstract

There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a set of generalized leaky integrate-and-fire (GLIF) models of increasing complexity to reproduce the spiking behaviors of 645 recorded neurons from 16 transgenic lines. The more complex models have an increased capacity to predict spiking behavior of hold-out stimuli. We use unsupervised methods to classify cell types, and find that high level GLIF model parameters are able to differentiate transgenic lines comparable to electrophysiological features. The more complex model parameters also have an increased ability to differentiate between transgenic lines. Thus, creating simple models is an effective dimensionality reduction technique that enables the differentiation of cell types from electrophysiological responses without the need for a priori-defined features. This database will provide a set of simplified models of multiple cell types for the community to use in network models.

Suggested Citation

  • Corinne Teeter & Ramakrishnan Iyer & Vilas Menon & Nathan Gouwens & David Feng & Jim Berg & Aaron Szafer & Nicholas Cain & Hongkui Zeng & Michael Hawrylycz & Christof Koch & Stefan Mihalas, 2018. "Generalized leaky integrate-and-fire models classify multiple neuron types," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02717-4
    DOI: 10.1038/s41467-017-02717-4
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    Cited by:

    1. Cristina Rueda & Itziar Fernández & Yolanda Larriba & Alejandro Rodríguez-Collado, 2021. "The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    2. Lukas Ramlow & Benjamin Lindner, 2021. "Interspike interval correlations in neuron models with adaptation and correlated noise," PLOS Computational Biology, Public Library of Science, vol. 17(8), pages 1-35, August.
    3. Shao, Yan & Wu, Fuqiang & Wang, Qingyun, 2024. "Dynamics and stability of neural systems with indirect interactions involved energy levels," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    4. Julian Rossbroich & Daniel Trotter & John Beninger & Katalin Tóth & Richard Naud, 2021. "Linear-nonlinear cascades capture synaptic dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-27, March.

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