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

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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.

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Most cited references 49

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Large-scale recording of neuronal ensembles.

György Buzsáki (2004)

How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

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Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena.

U Mitzdorf (1984)

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A simple white noise analysis of neuronal light responses.

E Chichilnisky (2001)

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

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Author and article information

Contributors

Domokos Meszéna:

ORCID: http://orcid.org/0000-0003-4042-2542

Lucia Wittner:

ORCID: http://orcid.org/0000-0001-6800-0953

Kinga Tóth:

ORCID: http://orcid.org/0000-0002-8751-8499

István Ulbert:

ORCID: http://orcid.org/0000-0001-9941-9159

Zoltán Somogyvári:

ORCID: http://orcid.org/0000-0002-4385-3025

Daniel K Wójcik:

ORCID: http://orcid.org/0000-0003-0812-9872

Frances K Skinner: Role: Reviewing Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 17 November 2017

Publication date Collection: 2017

Volume: 6

Electronic Location Identifier: e29384

Affiliations

[1 ]deptWigner Research Centre for Physics Hungarian Academy of Sciences BudapestHungary

[2 ]deptInstitute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences Hungarian Academy of Sciences BudapestHungary

[3 ]deptFaculty of Information Technology and Bionics Pázmány Péter Catholic University BudapestHungary

[4 ]National Institute of Clinical Neurosciences BudapestHungary

[5 ]Neuromicrosystems Ltd. BudapestHungary

[6 ]deptDepartment of Neurophysiology Nencki Institute of Experimental Biology of Polish Academy of Sciences WarsawPoland

Krembil Research Institute, University Health Network Canada

Author information

Dorottya Cserpán https://orcid.org/0000-0002-7538-1931

Domokos Meszéna http://orcid.org/0000-0003-4042-2542

Lucia Wittner http://orcid.org/0000-0001-6800-0953

Kinga Tóth http://orcid.org/0000-0002-8751-8499

István Ulbert http://orcid.org/0000-0001-9941-9159

Zoltán Somogyvári http://orcid.org/0000-0002-4385-3025

Daniel K Wójcik http://orcid.org/0000-0003-0812-9872

Article

Publisher ID: 29384

DOI: 10.7554/eLife.29384

PMC ID: 5716668

PubMed ID: 29148974

SO-VID: e9c739b8-f758-4a15-8d15-cb7581673be6

License:

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

History

Date received : 07 June 2017

Date accepted : 16 November 2017

Funding

Funded by: Nemzeti Kutatasi, Fejlesztesi es Innovacios Hivatal;

Award ID: K119443

Award Recipient : Lucia Wittner

Funded by: Nemzeti Agykutatasi Program;

Award ID: KTIA-13-NAP-A-IV/1 2 3 4 6

Award Recipient : István Ulbert

Funded by: Nemzeti Agykutatasi Program;

Award ID: KTIA NAP 13-1-2013-0001

Award Recipient : Zoltán Somogyvári

Funded by: Nemzeti Kutatási, Fejlesztesi és Innovacios Hivatal;

Award ID: K 113147

Award Recipient : Zoltán Somogyvári

Funded by: Nemzeti Kutatasi, Fejilesztesi es Innovacios Hivatal;

Award ID: NN 118902

Award Recipient : Zoltán Somogyvári

Funded by: FundRef http://dx.doi.org/10.13039/501100004569, Ministerstwo Nauki i Szkolnictwa Wyższego;

Award ID: 2729/7.PR/2013/2

Award Recipient : Daniel K Wójcik

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

Custom metadata

Author impact statement A computational method reveals current sources along the dendritic tree of a single neuron of known morphology from extracellular potential.

ScienceOpen disciplines: Life sciences

Keywords: local field potential,lfp,current source density,csd,kernel methods,inverse problem,rat

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: local field potential, lfp, current source density, csd, kernel methods, inverse problem, rat

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

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Abstract

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Most cited references 49

Large-scale recording of neuronal ensembles.

Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena.

A simple white noise analysis of neuronal light responses.

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