A functional model of adult dentate gyrus neurogenesis

  1. Olivia Gozel  Is a corresponding author
  2. Wulfram Gerstner
  1. University of Chicago, United States
  2. École Polytechnique Fédérale de Lausanne, Switzerland


In adult dentate gyrus neurogenesis, the link between maturation of newborn neurons and their function, such as behavioral pattern separation, has remained puzzling. By analyzing a theoretical model, we show that the switch from excitation to inhibition of the GABAergic input onto maturing newborn cells is crucial for their proper functional integration. When the GABAergic input is excitatory, cooperativity drives the growth of synapses such that newborn cells become sensitive to stimuli similar to those that activate mature cells. When GABAergic input switches to inhibitory, competition pushes the configuration of synapses onto newborn cells towards stimuli that are different from previously stored ones. This enables the maturing newborn cells to code for concepts that are novel, yet similar to familiar ones. Our theory of newborn cell maturation explains both how adult-born dentate granule cells integrate into the preexisting network and why they promote separation of similar but not distinct patterns.

Data availability

Simulation and plotting scripts can be found at: https://github.com/ogozel/NeurogenesisModel.

The following previously published data sets were used

Article and author information

Author details

  1. Olivia Gozel

    Department of Neurobiology, University of Chicago, Chicago, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2223-4097
  2. Wulfram Gerstner

    School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.


Swiss National Science Foundation (no. 200020 184615)

  • Wulfram Gerstner

European Union Horizon 2020 Framework Program (no. 785907 (HumanBrain Project,SGA2))

  • Wulfram Gerstner

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Tatyana O Sharpee, Salk Institute for Biological Studies, United States

Publication history

  1. Received: January 12, 2021
  2. Accepted: June 16, 2021
  3. Accepted Manuscript published: June 17, 2021 (version 1)
  4. Version of Record published: July 6, 2021 (version 2)


© 2021, Gozel & Gerstner

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


  • 1,410
    Page views
  • 214
  • 1

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Olivia Gozel
  2. Wulfram Gerstner
A functional model of adult dentate gyrus neurogenesis
eLife 10:e66463.

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Laura M Doherty et al.
    Research Article

    Deubiquitinating enzymes (DUBs), ~100 of which are found in human cells, are proteases that remove ubiquitin conjugates from proteins, thereby regulating protein turnover. They are involved in a wide range of cellular activities and are emerging therapeutic targets for cancer and other diseases. Drugs targeting USP1 and USP30 are in clinical development for cancer and kidney disease respectively. However, the majority of substrates and pathways regulated by DUBs remain unknown, impeding efforts to prioritize specific enzymes for research and drug development. To assemble a knowledgebase of DUB activities, co-dependent genes, and substrates, we combined targeted experiments using CRISPR libraries and inhibitors with systematic mining of functional genomic databases. Analysis of the Dependency Map, Connectivity Map, Cancer Cell Line Encyclopedia, and multiple protein-protein interaction databases yielded specific hypotheses about DUB function, a subset of which were confirmed in follow-on experiments. The data in this paper are browsable online in a newly developed DUB Portal and promise to improve understanding of DUBs as a family as well as the activities of incompletely characterized DUBs (e.g. USPL1 and USP32) and those already targeted with investigational cancer therapeutics (e.g. USP14, UCHL5, and USP7).

    1. Computational and Systems Biology
    2. Neuroscience
    Rany Abend et al.
    Research Article Updated

    Influential theories implicate variations in the mechanisms supporting threat learning in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity in humans. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We identified the best-fitting models for these processes and tested associations among latent learning parameters, whole-brain anatomy, and anxiety severity. Results indicate that greater anxiety severity related specifically to slower safety learning and slower extinction of response to safe stimuli. Nucleus accumbens gray-matter volume moderated learning-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates.