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Dendritic excitability controls overdispersion

Nature Computational Science volume 4, pages 19–28 (2024)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

The brain is an intricate assembly of intercommunicating neurons whose input–output function is only partially understood. The role of active dendrites in shaping spiking responses, in particular, is unclear. Although existing models account for active dendrites and spiking responses, they are too complex to analyze analytically and demand long stochastic simulations. Here we combine cable and renewal theory to describe how input fluctuations shape the response of neuronal ensembles with active dendrites. We found that dendritic input readily and potently controls interspike interval dispersion. This phenomenon can be understood by considering that neurons display three fundamental operating regimes: one mean-driven regime and two fluctuation-driven regimes. We show that these results are expected to appear for a wide range of dendritic properties and verify predictions of the model in experimental data. These findings have implications for the role of interspike interval dispersion in learning and for theories of attractor states.

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Fig. 1: Three operational regimes.
Fig. 2: Experimental validation.

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Data availability

The raw electrophysiology data can be obtained by writing to the corresponding author, who will seek the permission of the data owners (Matthew Larkum and Christine Grienberger). Source data are provided with this paper. Source data used in Fig. 1, Extended Data Fig. 1 and Supplementary Figs. 1, 3 and 4 can also be generated using the publicly available code.

Code availability

All code used in this manuscript is publicly available on Code Ocean (ref. 38) and on GitHub at https://github.com/ZachFriedenberger/dendritic-renewal-theory.

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Acknowledgements

We thank all members of the Neural Coding Lab for helpful discussions regarding the manuscript. This work was supported by an NSERC Discovery Grant (to R.N., RGPIN-2017-06872) and an NSERC PGS-D Scholarship (to Z.F.).

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Authors and Affiliations

  1. Centre for Neural Dynamics and Artificial Intelligence, University of Ottawa, Ottawa, Ontario, Canada

    Zachary Friedenberger & Richard Naud

  2. Department of Physics, University of Ottawa, Ottawa, Ontario, Canada

    Zachary Friedenberger & Richard Naud

  3. Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Richard Naud

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  1. Zachary Friedenberger
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Contributions

Z.F. provided conceptualization, writing, mathematical derivations, simulations and data analysis. R.N. provided conceptualization, writing and supervision. All authors reviewed the manuscript and approved the final version.

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Correspondence to Richard Naud.

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The authors declare no competing interests.

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Nature Computational Science thanks Maria Psarrou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Dendritic control of overdispersion is observed for a wide range of dendritic spike amplitudes and durations.

Dendritic input controls frequency shift, gain, and overdispersion. a Schematic of the parametrization of dendritic input modulation in terms of the shift (a) and gain (g) of the f-I curve, as well as overdispersion (c). b-d Heatmaps of the effect on shift (a), gain (g) and overdispersion (c) for different amplitude and duration of the dendritic spikes. The color scale bar corresponds to the dimensionless values of (a), (g), and (c). The undefined region in c-d corresponds to a region where our definitions of gain and dispersion break down (see Methods section ‘Effect of the dendritic spike amplitude and duration’).

Source data

Supplementary information

Source data

Source Data Fig. 1

CSV files for the data in Fig. 1.

Source Data Fig. 2

CSV files for the data in Fig. 2.

Source Data Extended Data Fig. 1

CSV files for the data in Extended Data Fig. 1.

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Friedenberger, Z., Naud, R. Dendritic excitability controls overdispersion. Nat Comput Sci 4, 19–28 (2024). https://doi.org/10.1038/s43588-023-00580-6

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