Thalamocortical-network

These are simulation results from the largest publicly available, physiologically realistic, thalamocortical network model from a rodent, as presented in the original paper by Roger Traub (2005). The original model was developed in FORTRAN, later converted into NEURON by Michael Hines. The 3D morphologies of the cells were taken from the NeuroML version by Padraig Gleeson. This model has some known limitations - however, it is one of the few detailed thalamocortical compartmental models, that has been investigated by many groups and translated to multiple simulator software versions. The results presented here were obtained with the NEURON code modified by Helena Głąbska to incorporate tracking of trans-membrane currents. In the simulations represented here the axonal gap junctions from the original Fortran model were turned off. The data were computed using the parallelized NEURON version on an IBM's Blue Gene Q cluster at ICM (Univ. of Warsaw). The code used to generate these results is available at github. The datasets contain: Cell morphologies Spike times Membrane potentials Trans-membrane currents Contributions to total transmembrane current from different currents (pasive, capacitive, synaptic, sodium, etc) (for the downscaled model only) The results are provided in the Neuroscience Simulation Data Format (NSDF) format which is the subspecification of the HDF5 and can be directly viewed using HDFViewer software. The exact specification of NSDF is presented here: S. Ray, C. Chintaluri, U. S. Bhalla, D.K. Wójcik: NSDF: Neuroscience Simulation Data Format, Neuroinformatics 14:2 (2016) 147-167, doi:10.1007/s12021-015-9282-5 These data were used in the following research: H. Głąbska, J. Potworowski, S. Łęski, D. K. Wójcik: Independent components of neural activity carry information on individual populations, PLOS ONE 9 (2014), e105071 T.V. Ness, C. Chintaluri, J. Potworowski, S. Łęski, H. Głąbska, D.K. Wójcik, G.T. Einevoll: Modelling and analysis of neural electrical potentials recorded in microelectrode arrays (MEAs), Neuroinformatics 13:4 (2015), 403-426 H. Głąbska, E. Norheim, A. Devor, A.M. Dale, G.T. Einevoll, D.K. Wójcik: Generalized Laminar Population Analysis (gLPA) for interpretation of multielectrode data from cortex, Frontiers in Neuroinformatics (2016) 10:1. doi: 10.3389/fninf.2016.00001 These data are described in detail in the publication: https://link.springer.com/article/10.1007/s12021-016-9319-4