Distance-dependent inhibition in the hippocampus (Strüber et al. 2017)

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Accession:229750
Network model of a hippocampal circuit including interneurons and principal cells. Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input.
Reference:
1 . Strüber M, Sauer JF, Jonas P, Bartos M (2017) Distance-dependent inhibition facilitates focality of gamma oscillations in the dentate gyrus. Nat Commun 8:758 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s): Dentate gyrus granule cell; Dentate gyrus basket cell;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s): GabaA; Glutamate;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Gamma oscillations; Spatio-temporal Activity Patterns;
Implementer(s): Strüber, Michael [michael_strueber at hotmail.com];
Search NeuronDB for information about:  Dentate gyrus granule cell; GabaA; Glutamate; Gaba; Glutamate;
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DDnet
readme.txt
gap.mod
kaprox.mod *
kdrca1.mod *
km.mod *
na3n.mod *
net_hh_wbsh.mod
net_dd_ana.hoc
net_dd_emodel.hoc
net_dd_imodel.hoc
net_dd_params.hoc
net_dd_procs.hoc
net_dd_run.hoc
net_dd_vectors.hoc
                            
//////////////////////////////////////////////////////////
/* 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)

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