//////////////////////////////////////////////////////////
/* net_dd_params.hoc -----------------------------------*/
//////////////////////////////////////////////////////////
strdef fnbase
sprint(fnbase,"DDnet")
/* Distance dependence ------------------------------------------------------ */
// IN-IN Synapses
shuffled_II = 0
g_scale_II = 1
a_ddg_II = 0
b_ddtau_II = 0
// IN-PN Synapses
shuffled_IE = 0
g_scale_IE = 1
a_ddg_IE = 0
b_ddtau_IE = 0
/* Network mode? ------------------------------------------------------------ */
feedback = 0 // does the network contain feedback excitation on INs?
recurrent = 0 // is the network a recurrent one (CA3) or not (DG)?
/* Spatial input --------------------------------------------------------- */
IN_input_format = 2
PN_input_format = 2 // '0': no spatial input
// '1': spatial input applied through current injection
// '2': spatial input applied through synaptic events
/* Define global simulation ctrl params ---------------------------------- */
celsius = 6.3 // C - temperature
secondorder = 1
// temperature has no real meaning in the simulations
// this is the def.temp in Neuron (HH. model)
// timeconstants of m, h and n are adjusted directly in the mod file
t = 0 // ms - simulation starts
// BUT synaptic connections are activated only at t = 0
tdrv1 = 0
tdrv2 = 50 // ms - init period to randomize firing pattern 75
tsyn = 100 // 150
trun = 700
tstop = trun+tsyn // ms - simulation ends
dt = 0.01 // ms - simulation time step (=100kHz)
t_step = 0.1 // ms - collecting data only @10kHz for analysis
n_step = int(t_step/dt) // should be 10 ;-) sample is taken every n cycle
inj_step = 1 // ms - shaped input time step
/* Define network size & connectivity pattern ---------------------------- */
nIN = 200 // number of neurons in the network
nPN = 800
// IN to IN
Msyn_II = 60 // Average number of presynaptic neurons, convergence factor
sd_ddcon_II = 25 // Space constant of the connectivity. This value was adjusted by trial
// so that a connection probability of 1 is not exceeded. This guarantees
// that there is really an Msyn of the given value set.
max_ddcon_II = (nIN/2)-1 // Maximal distance between two connected INs?
// (given in IN-index numbers)
// IN to PN
Msyn_IE = 20
sd_ddcon_IE = 25
max_ddcon_IE = (nIN/2)-1
// PN to IN
Msyn_EI = 50
sd_ddcon_EI = 10*int(nPN/nIN)
max_ddcon_EI = (nPN/2)-1
// PN to PN
Msyn_EE = 10
sd_ddcon_EE = 10*int(nPN/nIN)
max_ddcon_EE = (nPN/2)-1
//GAPS
Gaps = 1 // Boolean - gaps inserted or not
GapR = 100000 // MOhm resistance corresponding to 0.01 nS conductance
// that correspondes to 1nS for real cells of ~10000 um^2 surface
// x 0.01 mS/cm^2
/* Constant IClamp excitatory drive ----------------------------------------- */
Imu_interneuron = 0 // uA/cm^2 - amplitude here as a density
Icv_interneuron = 0.1 // coeff.var. of the current distribution
Imu_principalneuron = 0 // uA/cm^2 - amplitude here as a density
Icv_principalneuron = 0.1 // coeff.var. of the current distribution
/* Background synaptic drive ------------------------------------------------ */
//----------------Interneurons
N_BGStim_IN = nIN
BGStim_f_IN = 300
BGStim_N_IN = 2*trun*BGStim_f_IN/1000
BGStim_noise_IN = 0.5
BGSyn_rise_IN = 0.1
BGSyn_decay_IN = 2
BGSyn_strength_IN = 0.01
BGSyn_strength_cv_IN = 0.2
BGCon_dd_IN = 0 // Is the background input spatially correlated?
BGCon_N_IN = 1
BGCon_SD_IN = 3
//----------------Principalneurons
N_BGStim_PN = nPN
BGStim_f_PN = 500
BGStim_N_PN = 2*trun*BGStim_f_PN/1000
BGStim_noise_PN = 0.1
BGSyn_rise_PN = 0.1
BGSyn_decay_PN = 2
BGSyn_strength_PN = 0.02
BGSyn_strength_cv_PN = 0.1
BGCon_dd_PN = 0 // Is the background input spatially correlated?
BGCon_N_PN = 1
BGCon_SD_PN = 3
/* Define shaped input ----------------------------------------------------- */
//----------------Interneurons
tINinj_on = 400
tINinj_off = 800
tvarINinj = 3 // Indicates the variability in the onset of the stimulus for different cells.
// The spatial parameters for the two / one input:
spainjIN_M1 = 100 // center of the first input
spainjIN_sd1 = 20 // SD in case of a Gaussian input, width in case of a square input
spainjIN_M2 = 120 // center of the second input
spainjIN_sd2 = 10 // SD in case of a Gaussian input, width in case of a square input
spainjIN_ratio = 1 // Ratio between input1 and input2: Amp(input1) = 1, Amp(input2) = spainj_ratio
// a) IClamp
INinj_amp = 0
// b) Synaptic events
IN_N_SIGNStim = nIN
IN_SIGNStim_f = 600
IN_SIGNStim_N = (tINinj_off-tINinj_on)*IN_SIGNStim_f/1000
IN_SIGNStim_noise = 0.1
IN_SIGNSyn_rise = 0.1
IN_SIGNSyn_decay = 2
IN_SIGNSyn_strength = 0.01
IN_SIGNCon_rnd = 0 // Does the input wiring contain some randomness?
IN_SIGNCon_N = 1
//----------------Principalneurons
tPNinj_on = 400
tPNinj_off = 800
tvarPNinj = 20 // Indicates the variability in the onset of the stimulus for different cells.
// The spatial parameters for the two / one input:
spainjPN_M1 = 100 // center of the first input
spainjPN_sd1 = 20 // SD in case of a Gaussian input, width in case of a square input
spainjPN_M2 = 120 // center of the second input
spainjPN_sd2 = 10 // SD in case of a Gaussian input, width in case of a square input
spainjPN_ratio = 1 // Ratio between input1 and input2: Amp(input1) = 1, Amp(input2) = spainj_ratio
// a) IClamp
PNinj_amp = 0
// b) Synaptic events
PN_N_SIGNStim = nPN
PN_SIGNStim_f = 6000
PN_SIGNStim_N = (tPNinj_off-tPNinj_on)*PN_SIGNStim_f/1000
PN_SIGNStim_noise = 0.1
PN_SIGNSyn_rise = 0.1
PN_SIGNSyn_decay = 2
PN_SIGNSyn_strength = 0.02
PN_SIGNCon_rnd = 0 // Does the input wiring contain some randomness?
PN_SIGNCon_N = 1
// These vectors are defined here, because they are needed for constructing the
// cell models!
objref temiinj_IN, temiinj_PN, iinj_time, spaiinj_IN, spaiinj_PN
/* Define geometry of neurons --------------------------------------------- */
n_d_interneuron = 5.6419 // um - diam to give 100 um^2 surface
n_L_interneuron = 5.6419 // um - length
n_seg_interneuron = 1 // single compartment
n_d_principalneuron = 5.6419 // um - diam to give 100 um^2 surface
n_L_principalneuron = 5.6419 // um - length
n_seg_principalneuron = 1 // single compartment
// No real meaning as all conductances and currents are defined by density parameters!
/* Define cable params ---------------------------------------------------- */
ARes_interneuron = 172 // ohm cm - axial resistance
MCap_interneuron = 0.93 // uF/cm^2 - membrane capacitance
Rm_interneuron = 7600 // ohm cm^2 - membrane resistance
ARes_principalneuron = 194 // ohm cm - axial resistance
MCap_principalneuron = 1.01 // uF/cm^2 - membrane capacitance
Rm_principalneuron = 38000 // ohm cm^2 - membrane resistance
// Defines a leakage cond. of 0.1 mS/cm^2
// This is built into the mod file, it is here only for reference
Ek_interneuron = -90
shift_interneuron = 0
Ek_principalneuron = -90
gna_principalneuron = 0.01
shift_principalneuron = 13
ena_principalneuron = 55
gkdr_principalneuron = 0.015
gka_principalneuron = 0
gkm_principalneuron = 0
// Initial membrane voltages
Vrest_interneuron = -65
Vmax_interneuron = Vrest_interneuron-2
Vmin_interneuron = Vrest_interneuron+2
Vrest_principalneuron = -75
Vmax_principalneuron = Vrest_principalneuron-2
Vmin_principalneuron = Vrest_principalneuron+2
/* Define Synaptic Currents ---------------------------------------------- */
// using:
// - a Exp2Syn object (parameters: tau1 - rise and tau2 - decay
// timeconstants [ms], e - reversal potantial [mV])
// - a NetCon object (parameters: threshold - will be set to 0 mV (IN) and -20 mV (PN) respectively,
// delay [ms] and weight - variable between 0 and 1 [1 corresponding to 1 uS])
SynDel = 0.5 // ms - synaptic delay
SynADel= 0.2 // ms - conduction delay unit
// between 2 adjacent INs (distance = 0.05 mm; vAP = 0.25 mm/ms)
/* --------------- IPSCs onto Interneurons ---------------------------------- */
Syn_II_N = 50 // number of synapses on each neuron - remember to change the number also in the imodel-file!
Syn_II_rise = 0.16 // ms - IPSC rise
Syn_II_decay_standard = 1.88 // ms - IPSC decay
SynE_II = -55 // mV - IPSC rev. potential
g_standard_II = 0.2*g_scale_II
// SynG_II will be calculated later
SynGcv_II = 0.1 // Variability of the SynG
// DD in synaptic conductance
ddg_II_low = 0.1 // Lowest possible value for the synaptic conductance (as a factor! = %).
// It should correspond to a quantal conductance.
// DD in the decay time constant
Syn_II_decay_bottom = 0.5
Syn_II_decay_top = 4.0
/* ----------------- IPSCs onto Principalneurons ---------------------------- */
Syn_IE_N = 50 // number of synapses on each neuron - remember to change the number also in the imodel-file!
Syn_IE_rise = 0.16 // ms - IPSC rise
Syn_IE_decay_standard = 7.0 // ms - IPSC decay
SynE_IE = -65 // mV - IPSC rev. potential
g_standard_IE = 0.1*g_scale_IE
// SynG_IE will be calculated later
SynGcv_IE = 0.1 // Variability of the SynG
// DD in synaptic conductance
ddg_IE_low = 0.1 // Lowest possible value for the synaptic conductance (as a factor! = %).
// It should correspond to a quantal conductance.
// DD in the decay time constant
Syn_IE_decay_bottom = 3.0
Syn_IE_decay_top = 13.0
/* ----------------- EPSCs onto Interneurons -------------------------------- */
Syn_EI_rise = 0.2 // ms - EPSC rise, Geiger et al., 1997
Syn_EI_decay = 1 // ms - EPSC decay
SynE_EI = 0 // mV - EPSC rev. potential
SynG_EI = 0.05 // mS/cm^2 - EPSC conductance density!
SynGcv_EI = 0.1 // and its variability
/* ----------------- EPSCs onto Principalneurons ---------------------------- */
Syn_EE_rise = 0.2 // ms - EPSC rise
Syn_EE_decay = 1 // ms - EPSC decay
SynE_EE = 0 // mV - EPSC rev. potential
SynG_EE = 0.05 // mS/cm^2 - EPSC conductance density!
SynGcv_EE = 0.1 // and its variability
/* Plotting ------------------------------------------------------------------*/
plotting = 0 // Boolean. Do you want plots at all?
export_traces = 0 // Boolean. Do you want to export voltage- and conductance traces?
record_lfp = 1 // Boolean. DO you want to export an LFP analogon?
/* Testing -------------------------------------------------------------------*/
conmx = 0 // export connectivity matrix?
/* Random --------------------------------------------------------------------*/
objref rdg, rduni, rdnorm, rdnormII, rdnormIE
//rseed = 18
rdg = new Random(rseed) // use for gaps
rdg.binomial(1,0.5)
rduni = new Random(rseed) // use for Vm, synapses
rduni.uniform(0,1)
rdnorm = new Random(rseed) // use for exc.drv, synaptic connectivity
rdnorm.normal(0,1)
rdnormII = new Random(rseed) // use for the nodd synaptic property picking procedure
rdnormII.normal(0,sd_ddcon_II*sd_ddcon_II)
rdnormIE = new Random(rseed) // use for the nodd synaptic property picking procedure
rdnormIE.normal(0,sd_ddcon_IE*sd_ddcon_IE) | 
