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Compartmentalized dendritic plasticity and input feature storage in neurons

Author

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  • Attila Losonczy

    (Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Dr Ashburn, Virginia 20147, USA)

  • Judit K. Makara

    (Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Dr Ashburn, Virginia 20147, USA)

  • Jeffrey C. Magee

    (Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Dr Ashburn, Virginia 20147, USA)

Abstract

Although information storage in the central nervous system is thought to be primarily mediated by various forms of synaptic plasticity, other mechanisms, such as modifications in membrane excitability, are available. Local dendritic spikes are nonlinear voltage events that are initiated within dendritic branches by spatially clustered and temporally synchronous synaptic input. That local spikes selectively respond only to appropriately correlated input allows them to function as input feature detectors and potentially as powerful information storage mechanisms. However, it is currently unknown whether any effective form of local dendritic spike plasticity exists. Here we show that the coupling between local dendritic spikes and the soma of rat hippocampal CA1 pyramidal neurons can be modified in a branch-specific manner through an N-methyl-d-aspartate receptor (NMDAR)-dependent regulation of dendritic Kv4.2 potassium channels. These data suggest that compartmentalized changes in branch excitability could store multiple complex features of synaptic input, such as their spatio-temporal correlation. We propose that this ‘branch strength potentiation’ represents a previously unknown form of information storage that is distinct from that produced by changes in synaptic efficacy both at the mechanistic level and in the type of information stored.

Suggested Citation

  • Attila Losonczy & Judit K. Makara & Jeffrey C. Magee, 2008. "Compartmentalized dendritic plasticity and input feature storage in neurons," Nature, Nature, vol. 452(7186), pages 436-441, March.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7186:d:10.1038_nature06725
    DOI: 10.1038/nature06725
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    Cited by:

    1. Linda Judák & Balázs Chiovini & Gábor Juhász & Dénes Pálfi & Zsolt Mezriczky & Zoltán Szadai & Gergely Katona & Benedek Szmola & Katalin Ócsai & Bernadett Martinecz & Anna Mihály & Ádám Dénes & Bálint, 2022. "Sharp-wave ripple doublets induce complex dendritic spikes in parvalbumin interneurons in vivo," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Matteo Farinella & Daniel T Ruedt & Padraig Gleeson & Frederic Lanore & R Angus Silver, 2014. "Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-21, April.
    3. Ruy Gómez-Ocádiz & Massimiliano Trippa & Chun-Lei Zhang & Lorenzo Posani & Simona Cocco & Rémi Monasson & Christoph Schmidt-Hieber, 2022. "A synaptic signal for novelty processing in the hippocampus," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Robert Legenstein & Niko Wilbert & Laurenz Wiskott, 2010. "Reinforcement Learning on Slow Features of High-Dimensional Input Streams," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-13, August.
    5. Dejan Pecevski & Lars Buesing & Wolfgang Maass, 2011. "Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-25, December.
    6. Hanle Zheng & Zhong Zheng & Rui Hu & Bo Xiao & Yujie Wu & Fangwen Yu & Xue Liu & Guoqi Li & Lei Deng, 2024. "Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    7. Ian Cone & Claudia Clopath, 2024. "Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Hang Zhou & Guo-Qiang Bi & Guosong Liu, 2024. "Intracellular magnesium optimizes transmission efficiency and plasticity of hippocampal synapses by reconfiguring their connectivity," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    9. Balázs Ujfalussy & Tamás Kiss & Péter Érdi, 2009. "Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-16, September.

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