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.
87 - 99
Current source density, Data sharing, Extracellular potential, HDF5, Local field potential, Microelectrode array, Modelling, Simulated data, Animals, Cerebral Cortex, Computer Simulation, Datasets as Topic, Humans, Information Dissemination, Membrane Potentials, Models, Neurological, Neural Networks (Computer), Neural Pathways, Neurons, Software, Thalamus