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Erschienen in: Cognitive Computation 5/2016

01.10.2016

An Automated Method for Characterization of Evoked Single-Trial Local Field Potentials Recorded from Rat Barrel Cortex Under Mechanical Whisker Stimulation

verfasst von: Mufti Mahmud, Claudia Cecchetto, Stefano Vassanelli

Erschienen in: Cognitive Computation | Ausgabe 5/2016

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Abstract

Rodents explore their surroundings through whisking by localizing objects and detecting textures very precisely. During such tactile exploration, whisker deflection is first mechanically transduced by receptors and then information encoded throughout the somatosensory pathway ending in the somatosensory ‘barrel’ cortex. In the barrel cortex, tactile information from a single whisker is segregated and processed in a cortical column corresponding to the deflected whisker. Local Field Potentials (LFPs) generated by whisker deflection in the barrel cortex present typical signatures in terms of shape and amplitude that are related to the activation of the local neuronal populations. Therefore, rigorous analysis of such responses may reveal important features about the function of underlying neuronal microcircuits. In this context, software methods for characterizing single-trial LFPs are needed that are also suitable for online extraction of LFP features and for brain–machine interfacing applications. In this work, we present an automated and efficient method to analyze evoked LFP responses in the rat barrel cortex through automatic removal of stimulation artifacts, detection of single events and characterization of their relevant parameters. Evoked single-trial LFPs recorded under two different anesthetics are examined to demonstrate the feasibility, accuracy and applicability of the method.

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Metadaten
Titel
An Automated Method for Characterization of Evoked Single-Trial Local Field Potentials Recorded from Rat Barrel Cortex Under Mechanical Whisker Stimulation
verfasst von
Mufti Mahmud
Claudia Cecchetto
Stefano Vassanelli
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2016
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-016-9399-3

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