Abstract
A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. Moreover, these data can be used to study the effects of different tissue models on the measurement. The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al., Journal of Neurophysiology, 93(4), 2194–2232 (Traub et al. 2005)), with activity evoked by different thalamic stimuli.
Highlights
The complexity of experimental protocols in neuroscience grows with technology
In the analysis of extracellular potentials we may wish to use signal decomposition methods, such as principal or independent component analysis for signals coming from coupled neural populations (Di et al 1990; Łeski et al 2010; Makarov et al 2010), or other more complex methods which take into account the physiology, such as laminar population came about as the acronym of ’local field potential’, which, as we know today, is a misnomer. It was shown by many including us Łeski et al (2007) and Hunt et al (2011) that due to the long-range nature of the electric field the same sources are visible in the rat’s brain on distances of the order of the whole brain, which makes low frequency part1 (LFP) a very non-local quantity. This is why we suggest to drop the name local field potential and read LFP as ’low frequency part’ of extracellular potential, which is the definition of LFP
We provide a collection of scripts to to compute the extracellular potential at arbitrary electrode positions
Summary
The complexity of experimental protocols in neuroscience grows with technology. This enables more voluminous data collection and their analysis requires increasingly sophisticated approaches. Generating ground truth data requires significant modeling experience, time to prepare, run and document the simulation, and access to high performance computers This whole exercise is often impractical for someone who would just want to validate applicability of a specific method of data analysis and apply it to her experimental data. We provide a collection of scripts to to compute the extracellular potential at arbitrary electrode positions We intend these data to serve as a proxy for experimental ground truth data and as benchmarks for validation and comparisons of different methods of neural data analysis. These datasets are provided in the Neuroscience Simulation Data Format (NSDF) (Ray et al 2016). We believe that providing data in a standardized format will further aid its scientific merit, as visualization tools and analytic methods can assume a common interface facilitating their generalization
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