Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons
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Abstract
The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current. When impinging on its early phase, individual inhibitory synapses strongly, but transiently, dampen the NMDA spike; later inhibition prematurely terminates it. A single inhibitory synapse reduces the NMDA-mediated Ca 2+ current, a key player in plasticity, by up to 45%. NMDA spikes in distal dendritic branches/spines are longer-lasting and more resilient to inhibition, enhancing synaptic plasticity at these branches. We conclude that NMDA spikes are highly sensitive to dendritic inhibition; sparse weak inhibition can finely tune synaptic plasticity both locally at the dendritic branch level and globally at the level of the neuron's output.
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The NMDA-current has slow dynamics, promoting integration over a time window of tens of milliseconds.
“Moreover, the NMDA-current has slow dynamics, promoting integration over a time window of tens of milliseconds (Major, Larkum, and Schiller 2013; Doron et al. 2017).”
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Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954 · see citing works
Self-citationMizuseki, K., Sirota, A., Pastalkova, E., & Buzsáki, G. (2009) · Neuron · doi:10.1016/j.neuron.2009.08.037 · Cited by 718 · see citing works
Regulation of neuronal input transformations by tunable dendritic inhibition
Lovett-Barron, M., Turi, G. F., Kaifosh, P., Lee, P. H., Bolze, F., Sun, X.-H., Nicoud, J.-F., Zemelman, B. V., Sternson, S. M., & Losonczy, A. (2012) · Nature Neuroscience · doi:10.1038/nn.3024 · Cited by 417 · see citing works
Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study.
Nevian, T., Larkum, M. E., Polsky, A., & Schiller, J. (2007) · Nature Neuroscience · doi:10.1038/nn1826 · Cited by 406 · see citing works
GABAergic interneurons targeting dendrites of pyramidal cells in the CA1 area of the hippocampus
Klausberger, T. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06913.x · Cited by 260 · see citing works
Sensory-evoked LTP driven by dendritic plateau potentials in vivo
Gambino, F., Pagès, S., Kehayas, V., Baptista, D., Tatti, R., Carleton, A., & Holtmaat, A. (2014) · Nature · doi:10.1038/nature13664 · Cited by 253 · see citing works
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Research works printed but not linked: 7
- Carnevale, N.T., and Hines, M.L. (2006). The NEURON Book (Cambridge University Press). Cell Reports 21, 1550-1561, November 7, 2017 1559
- Cuntz, H., Remme, M.W.H., and Torben-Nielsen, B. (2014). The Computing Dendrite (Springer).
- Du, K., Wu, Y.-W., Lindroos, R., Liu, Y., Ro ´zsa, B., Katona, G., Ding, J.B., and Kotaleski, J.H. (2017). Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny projection neurons. Proc. Natl. Acad. Sci 114, E7612-7621.
- Graupner, M., and Brunel, N. (2012). Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proc. Natl. Acad. Sci. USA 109, 3991-3996.
- Jack, J.J.B., Noble, D., and Tsien, R.W. (1975). Electric Current Flow in Excit- able Cells (Clarendon Press).
- Magee, J.C. (2016). Dendritic voltage-gated ion channels. In Dendrites, G. Stuart, N. Spruston, and M. Ha ¨usser, eds. (Oxford University Press), pp. 259-284.
- Shouval, H.Z., Bear, M.F., and Cooper, L.N. (2002). A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc. Natl. Acad. Sci. USA 99, 10831-10836.
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Single cortical neurons as deep artificial neural networks
Beniaguev, D., Segev, I., & London, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.002 · Cited by 254 · see citing works
Self-citation
- Authors
- Doron, M., Chindemi, G., Muller, E., Markram, H., & Segev, I.
- Affiliations
- Hebrew University of Jerusalem (IL)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Doron, Michael
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 21
- Issue
- 6
- Pages
- 1550-1561
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W2767528263
- Cited by
- 73
- References
- 6
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 5 · 2023: 12 · 2022: 7 · 2021: 12 · 2020: 9 · 2019: 10 · 2018: 5
- FWCI
- 3.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Gatsby Charitable Foundation
- EPFL-Hebrew University Collaborative Grant
- EPFL
- ETH
- European Union Seventh Framework Program (604102)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- synaptic inhibition, dendritic inhibition, dendritic processing, cortical neurons, synaptic plasticity, calcium current, inhibitory synapses, dendritic spines, dynamical systems theory, single-neuron computation
- MeSH terms
- Calcium, Dendrites, Models, Biological, Neurons, Receptors, GABA-A, Synapses, Receptors, N-Methyl-D-Aspartate, N-Methylaspartate
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