Learning NeuroML2 with cerebellar Golgi modelling
- Based on mod files from Solinas et al, 2007. Original publication: Solinas S, Forti L, Cesana E, Mapelli J, De Schutter E, D’Angelo E. Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. Front Cell Neurosci. 2007;1:2
- NeuroML2 implementation verified against OSB version of Vervaeke et al. 2010. Original publication: Rapid Desynchronization of an Electrically Coupled Interneuron Network with Sparse Excitatory Synaptic Input, Neuron 2010.
- Currently has reduced morphology in test_channel.cell.nml
NOTE: Single compartment doesn't have right geometry/membrane res and causes unrealistic currents - could be optimized in future
- Single cell with 2 Ca pools: NeuroML2 file
- Single cell with one Ca pool (all channels read/write onto same pool): NeuroML2 mechanisms and cell
Using NEURON for simulation (via pyneuroml)
- From NML2 descriptions:
python run_simple_goc.py #Generates LEMS simulation file (LEMS_sim1_goc.xml), NML->mod file, hoc file, NEURON-python simulation file, compiles Mod files and runs the Nrn-python simulation.
- If nrn-python is already created/ edited externally, run:
python LEMS_sim_goc1_nrn.py - simple_cell with HCN and leak channels :
GoC_file_name = 'simple_cell.cell.nml' - single compartment GoC with all channels from Solinas et al 2007:
GoC_file_name = 'test_channel.cell.nml'