Learning reorganizes dendritic and stabilizes axon initial segment inhibitory synapses in CA1 pyramidal neurons.

Overview

1. Leibniz Institute for Neurobiology, Magdeburg, Germany
2. International Max Planck Research School for Translational Psychiatry, Munich, Germany
3. Center for Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany
4. Graduate School for Systemic Neuroscience, Ludwig-Maximilian University, Munich, Germany
5. Brain Research Institute (HiFo), University of Zurich and Neuroscience Center Zurich, Zurich, Switzerland
6. Max Planck Institute of Psychiatry, Munich, Germany
7. German Center for Mental Health (DZPG), partner site Halle-Jena-Magdeburg and Center for Behavioral Brain Science (CBBS), Magdeburg, Germany
8. German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

Abstract

Structural synaptic plasticity underlies the changes in brain connectivity required for learning and memory. Inhibitory synapses target all subcellular domains of excitatory pyramidal neurons, including dendrites, somata and axon initial segments. These subcellular domains have distinct molecular, structural and physiological profiles which underlie their functions. How structural plasticity of inhibitory synapses supports these functions as well as emerging properties such as memory is largely unknown. To tackle these questions we tracked inhibitory synapses on basal dendrites, somata and axon initial segments of pyramidal neurons in the dorsal hippocampal CA1 area of mice over two weeks. Size and temporal dynamics of inhibitory synapses showed a strong compartmentalization. Trace fear conditioning led to reorganization of dendritic and to stabilization of axon initial segments’ inhibitory synapses. Finally, mathematical modelling allowed us to probe the mechanisms underlying stabilization of inhibitory synapses upon learning.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Code availability

The code has been uploaded into Code Ocean (DOI link: 10.24433/CO.9762429.v1) and, additionally, will be made available upon request. Points of contact for code availability are: Felix Kuhn and David Kappel.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 0 scripts, each with its path and the digest of its content;
• no match between paragraphs and code yet;
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Source data are provided with this paper as Excel document containing all numerical values needed to generate each plot or table. Due to the size of the raw images, they will be provided without restriction upon request. Point of contact for data availability is Alessio Attardo. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 13 MeSH terms, 1 funder, 110 references.

Cite

This paper

Klimmt, H., Kuhn, F., Kappel, D., Murthy, B., Ulivi, A. F., Argunşah, A. Ö., Vieweg, S., Huettl, R. E., Remy, S., & Attardo, A. (2026). Learning reorganizes dendritic and stabilizes axon initial segment inhibitory synapses in CA1 pyramidal neurons. Nature communications, 17(1), 9791. https://doi.org/10.1038/s41467-026-77800-w

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.celrep.2026.117793 [code]

Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons.

Journal: Cell reports

In common: cellular / molecular, 13 references

[2] doi:10.1126/sciadv.aec3961 [code]

Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.

Journal: Science advances

In common: mouse, 8 references

[3] doi:10.1523/jneurosci.1540-25.2026 [code]

Dendritic Inhibition Terminates Plateau Potentials in CA1 Pyramidal Neurons.

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience

In common: mouse, cellular / molecular, 7 references

[4] doi:10.1038/s41467-026-74834-y [code]

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Journal: Nature communications

In common: mouse, 5 references

[5] doi:10.1038/s41467-026-74869-1 [code]

Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing.

Journal: Nature communications

In common: mouse, cellular / molecular, 4 references

[6] doi:10.1371/journal.pcbi.1014719 [code]

Manifold-constrained plasticity enables stable learning in recurrent neural circuits.

Journal: PLoS computational biology

In common: 4 references

[7] doi:10.1371/journal.pcbi.1014571 [code]

SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.

Journal: PLoS computational biology

In common: 4 references

[8] doi:10.1371/journal.pcbi.1014575 [code]

Remembering the "when": Hebbian memory models for the time of past events.

Journal: PLoS computational biology

In common: 4 references

[9] doi:10.1126/sciadv.aeb4265 [code]

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Journal: Science advances

In common: mouse, cellular / molecular, 3 references

[10] doi:10.7554/elife.107099 [code]

Local inhibitory dynamics underpin temporal integration and functional segregation between barrels and septa in the mouse barrel cortex.

Journal: eLife

In common: mouse, author Ali Öztürk Argunşah

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.