Collection of simulated data for validation of methods of analysis of extracellular potentials
-
1
Nencki Institute, Neurophysiology, Poland
To test various methods of data analysis e.g. reconstruction of current source density (Potworowski et al. 2012), laminar population analysis (Einevoll et al. 2007) spike sorting algorithms etc. and to verify hypothesis concerning relationship between different aspects of recordings, e.g. local field potentials and synaptic currents we need realistic ground truth data. Generating such data requires plausible models of neural activity, access to high performance computers and time to prepare and run the simulations and process the output. To facilitate such tests and comparisons by the community we provide a rich collection of datasets including intracellular voltage traces; transmembrane currents; extracellular potential.
The data were generated in Neuron using the largest publicly available model of thalamocortical network (Traub et al. 2005). The model contains around 200 000 compartments in 3560 multicompartmental cells in 14 populations and was extended by adding 3-dimensional cell morphologies in NeuroML (Gleeson et al. 2007); distribution of cortical neurons in a cortical column; saving selected variables; providing additional stimuli.
The collection contains responses to oscillatory (12.5 Hz, 25 Hz, 50 Hz, 100 Hz, 200 Hz) or step input (2 ms) into thalamic cells. Since the datasets are large (e.g. voltage data from 600 ms of network activity takes more than 5 GB in binary format), more variables are provided for a simulation of 10% of the network.
Every dataset contains: voltage and sum of transmembrane currents in every segment every 0.1 ms; spike times; position of every segment; extracellular potential calculated on 28 electrodes. We also provide a script to calculate extracellular potential anywhere. For the simulation of the small network we further provided separate values of different transmembrane currents: GABA A, NMDA, AMPA, capacitive, passive, sodium, potassium, calcium, anomalous rectifier, two kinds of calcium low threshold T type currents (Traub et al. 2003, 2005) (not causing [Ca2+] influx) and other (e.g. steady bias and ectopic currents; Traub et al. 2005).
Fig 1: A) Cortical cells in the Traub's model. Blue: excitatory, red: inhibitory. B) Example input (100 Hz oscillatory injection) to thalamocortical relay cells C) Raster plot showing response to the stimulus in ten random cells from each population. D) Extracellular potential computed on 28 electrodes placed in the center of the column.
Acknowledgements
Grant N N303 542839 (Polish Ministry of Science and Higher Education)
Grant POIG.02.03.00-00-003/09 (Polish Ministry of Regional Development)
Grant POIG.02.03.00-00-018/08 (Polish Ministry of Regional Development)
EC-FP7-PEOPLE sponsored NAMASEN Marie-Curie Initial Training Network (grant n. 264872)
References
Einevoll GT et al. (2007) Laminar population analysis: estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex. J Neurophysiol. 97(3):2174-90
Gleeson P et al. (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219-35
Potworowski et al. (2012) Kernel current source density method. Neural Comput. 24(2):541-75
Traub RD et al. (2003) Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron. J Neurophysiol. 89:909-921
Traub RD et al. (2005) A single column thalamocortical network model. J Neurophysiol. 93(4):2194-232
Keywords:
extracellular potentials,
simulated data,
thalamocortical model,
computational neuroscience,
neural activity
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Głąbska
HT,
Chintaluri
H and
Wójcik
DK
(2014). Collection of simulated data for validation of methods of analysis of extracellular potentials.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00035
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
04 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Miss. Helena T Głąbska, Nencki Institute, Neurophysiology, Warsaw, Poland, hglabska@nencki.gov.pl