Python and NEURON scripts for running network simulations of reduced-morphology layer V pyramidal cells. Tuomo Maki-Marttunen, 2015-2017 CC BY 3.0 HOC-commands for simulations including in vivo-like synaptic firing based on (Hay & Segev 2015, "Dendritic excitability and gain control in recurrent cortical microcircuits", Cerebral Cortex 25(10): 3561-3571) Files included: models/TTC.hoc #HOC-file for simulations with reduced-morphology model and in vivo-like # synaptic inputs, synapses grouped to gain speed in simulations. models/TTC_det.hoc #HOC-file for simulations with reduced-morphology model and in vivo-like # synaptic inputs, synapses grouped to gain speed in simulations. The # activation times are predetermined and given to the synapse model as # parameters (this is needed when using non-stationary Poisson inputs). CaDynamics_E2.mod #mod-file for Ca2+ dynamics. From http://modeldb.yale.edu/139653 Ca_HVA.mod #mod-file for HVA Ca2+ currents. From http://modeldb.yale.edu/139653 Ca_LVAst.mod #mod-file for LVA Ca2+ currents. From http://modeldb.yale.edu/139653 Ih.mod #mod-file for HCN currents. From http://modeldb.yale.edu/139653 Im.mod #mod-file for Muscarinic K+ currents. From http://modeldb.yale.edu/139653 K_Pst.mod #mod-file for Persistent K+ currents. From http://modeldb.yale.edu/139653 K_Tst.mod #mod-file for Transient K+ currents. From http://modeldb.yale.edu/139653 NaTa_t.mod #mod-file for Transient Na+ currents. From http://modeldb.yale.edu/139653 Nap_Et2.mod #mod-file for Persisent Na+ currents. From http://modeldb.yale.edu/139653 ProbAMPANMDA2.mod #mod-file for AMPA-NMDA synapses. From http://modeldb.yale.edu/156780 ProbAMPANMDA2group.mod #mod-file for AMPA-NMDA synapse groups. Modified from ProbAMPANMDA2.mod. ProbAMPANMDA2groupdet.mod #mod-file for AMPA-NMDA synapse groups with predetermined order of synapse activation. ProbUDFsyn2.mod #mod-file for GABA synapses. From http://modeldb.yale.edu/156780 ProbUDFsyn2group.mod #mod-file for GABA synapse groups. Modified from ProbUDFsyn2.mod. ProbUDFsyn2groupdet.mod #mod-file for GABA synapse groups with predetermined order of synapse activation. SK_E2.mod #mod-file for SK currents. From http://modeldb.yale.edu/139653 SKv3_1.mod #mod-file for Kv3.1 currents. From http://modeldb.yale.edu/139653 extrapas.mod #mod-file for an additional glutamatergic passive conductance. approxhaynetstuff.py #Python file for setting the synaptic parameters of reduced-morphology neurons in # parallel simulations approxhaynetstuff_nonparallel.py #Python file for setting the synaptic parameters of reduced-morphology neurons in # non-parallel simulations calculate_spike_trains.py #Python file for running the network (N=150) simulations and saving the resulting # spike trains drawcumfr.py #Python file for drawing spike trains and cumulative firing rate curves from network # (N=150) simulations mytools.py #Python file for general utilities pars_withmids_combfs_final.sav #Final parameter set from the four fitting steps simseedburst_func_nonparallel_nonadaptive_allions.py #Python library for running the network (or single-cell) simulations To perform network simulations, run the following commands nrnivmodl #Compile the NEURON mechanisms python calculate_spike_trains.py #Run 14 repetitions of network simulations with three different intra-network synaptic strengths. # This is extremely slow (each of the 42 simulations may take 2-4 hours to finish). If possible, # divide to threads and run "python calculate_spike_trains.py $i", where i goes from 0 to 41. python drawcumfr.py #Plot the results These scripts reproduce Figure 8A-B (only for the model with reduced morphology and reduced number of synapses) in Mäki-Marttunen T, Halnes G, Devor A, Metzner C, Dale AM, Andreassen OA, Einevoll GT (2017): "A stepwise neuron model fitting procedure designed for recordings with high spatial resolution, application to layer V pyramidal cells".