Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus

  1. Diego M Arribas
  2. Antonia Marin-Burgin  Is a corresponding author
  3. Luis G Morelli  Is a corresponding author
  1. Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Argentina

Abstract

Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs encoding properties. Parameter values from this fit re ect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.

Data availability

The data generated in this study is publicly available at Dryad,doi:10.5061/dryad.73n5tb309. Custom code produced and used in the study is available at Github, https://github.com/diegoarri91/iclamp-glm.

The following data sets were generated

Article and author information

Author details

  1. Diego M Arribas

    Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
    Competing interests
    The authors declare that no competing interests exist.
  2. Antonia Marin-Burgin

    Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
    For correspondence
    aburgin@ibioba-mpsp-conicet.gov.ar
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0684-9796
  3. Luis G Morelli

    Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
    For correspondence
    lmorelli@ibioba-mpsp-conicet.gov.ar
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5614-073X

Funding

Agencia Nacional de Promoción Científica y Tecnológica (PICT 2015 0634)

  • Antonia Marin-Burgin

Agencia Nacional de Promoción Científica y Tecnológica (PICT 2018 0880)

  • Antonia Marin-Burgin

Agencia Nacional de Promoción Científica y Tecnológica (PICT 2017 3753)

  • Luis G Morelli

Agencia Nacional de Promoción Científica y Tecnológica (PICT 2019 0445)

  • Luis G Morelli

International Development Research Centre (IDRC108878)

  • Antonia Marin-Burgin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Open access funding provided by Max Planck Society.

Ethics

Animal experimentation: Experimental protocol (2020-03-NE) was evaluated by the Institutional Animal Care and Use Committee of the IBioBA-CONICET according to the Principles for Biomedical Research involving animals of the Council for International Organizations for Medical Sciences and provisions stated in the Guide for the Care and Use of Laboratory Animals.

Copyright

© 2023, Arribas et al.

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.

Metrics

  • 603
    views
  • 92
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Diego M Arribas
  2. Antonia Marin-Burgin
  3. Luis G Morelli
(2023)
Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus
eLife 12:e80250.
https://doi.org/10.7554/eLife.80250

Share this article

https://doi.org/10.7554/eLife.80250

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.