Revealing the distribution of transmembrane currents along the dendritic tree of a neuron from extracellular recordings

  1. Dorottya Cserpán
  2. Domokos Meszéna
  3. Lucia Wittner
  4. Kinga Tóth
  5. István Ulbert
  6. Zoltán Somogyvári
  7. Daniel K Wojcik  Is a corresponding author
  1. Hungarian Academy of Sciences, Hungary
  2. Nencki Institute of Experimental Biology, Poland

Abstract

Revealing the current source distribution along the neuronal membrane is a key step on the way to understanding neural computations, however, the experimental and theoretical tools to achieve sufficient spatiotemporal resolution for the estimation remain to be established. Here we address this problem using extracellularly recorded potentials with arbitrarily distributed electrodes for a neuron of known morphology. We use simulations of models with varying complexity to validate the proposed method and to give recommendations for experimental applications. The method is applied to in vitro data from rat hippocampus.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Dorottya Cserpán

    Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  2. Domokos Meszéna

    Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  3. Lucia Wittner

    Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  4. Kinga Tóth

    Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  5. István Ulbert

    Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  6. Zoltán Somogyvári

    Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  7. Daniel K Wojcik

    Department of Neurophysiology, Nencki Institute of Experimental Biology, Warsaw, Poland
    For correspondence
    d.wojcik@nencki.gov.pl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0812-9872

Funding

Ministerstwo Nauki i Szkolnictwa Wyższego (Grant 2729/7.PR/2013/2)

  • Daniel K Wojcik

Nemzeti Kutatási, Fejlesztesi és Innovacios Hivatal (Grant K 113147)

  • Zoltán Somogyvári

Nemzeti Agykutatasi Program (Grant KTIA NAP 13-1-2013-0001)

  • István Ulbert

Nemzeti Kutatasi, Fejilesztesi es Innovacios Hivatal (Grant NN 118902)

  • Zoltán Somogyvári

Nemzeti Agykutatasi Program (Grant KTIA-13-NAP-A-IV/1 2 3 4 6)

  • István Ulbert

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

Ethics

Animal experimentation: The in vitro experiment was performed according to the EC Council Directive of November 24, 1986 (86/89/EEC) and all procedures were reviewed and approved by the local ethical committee and the Hungarian Central Government Office (license number: PEI/001/695-9/2015).

Copyright

© 2017, Cserpán 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

  • 2,073
    views
  • 382
    downloads
  • 13
    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. Dorottya Cserpán
  2. Domokos Meszéna
  3. Lucia Wittner
  4. Kinga Tóth
  5. István Ulbert
  6. Zoltán Somogyvári
  7. Daniel K Wojcik
(2017)
Revealing the distribution of transmembrane currents along the dendritic tree of a neuron from extracellular recordings
eLife 6:e29384.
https://doi.org/10.7554/eLife.29384

Share this article

https://doi.org/10.7554/eLife.29384

Further reading

    1. Neuroscience
    Ankur Sinha, Padraig Gleeson ... Robin Angus Silver
    Tools and Resources

    Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

    1. Neuroscience
    Gyeong Hee Pyeon, Hyewon Cho ... Yong Sang Jo
    Research Article

    Recent studies suggest that calcitonin gene-related peptide (CGRP) neurons in the parabrachial nucleus (PBN) represent aversive information and signal a general alarm to the forebrain. If CGRP neurons serve as a true general alarm, their activation would modulate both passive nad active defensive behaviors depending on the magnitude and context of the threat. However, most prior research has focused on the role of CGRP neurons in passive freezing responses, with limited exploration of their involvement in active defensive behaviors. To address this, we examined the role of CGRP neurons in active defensive behavior using a predator-like robot programmed to chase mice. Our electrophysiological results revealed that CGRP neurons encode the intensity of aversive stimuli through variations in firing durations and amplitudes. Optogenetic activation of CGRP neuron during robot chasing elevated flight responses in both conditioning and retention tests, presumably by amyplifying the perception of the threat as more imminent and dangerous. In contrast, animals with inactivated CGRP neurons exhibited reduced flight responses, even when the robot was programmed to appear highly threatening during conditioning. These findings expand the understanding of CGRP neurons in the PBN as a critical alarm system, capable of dynamically regulating active defensive behaviors by amplifying threat perception, ensuring adaptive responses to varying levels of danger.