Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)

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Accession:83319
This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
Reference:
1 . Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786-95 [PubMed]
2 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschl├Ąger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349-98 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; GENESIS; NEST; C or C++ program; XPP; CSIM; NCS; SPLIT; MVASpike; SciLab; Brian; PyNN; Python;
Model Concept(s): Activity Patterns; Methods;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu]; Hines, Michael [Michael.Hines at Yale.edu]; Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]; Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]; Ermentrout, Bard [bard_at_pitt.edu]; Brette R; Bower, James; Beeman, Dave; Diesmann M; Morrison A ; Goodman PH; Harris Jr, FC; Zirpe M ; Natschlager T ; Pecevski D ; Djurfeldt M; Lansner, Anders [ala at kth.se]; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at unic.cnrs-gif.fr]; Rudolph M ;
/*     ###########     Exact simulation of integrate-and-fire models with synaptic conductances ######################

This code is associated with the paper :
"Brette Romain (2006) , Exact simulation of integrate-and-fire models with synaptic conductances" :
http://www.di.ens.fr/~brette/papers/Brette2005NC.htm
*************************
*************************
*************************
Cohen Benjamin
benjamin.cohen _AT_ ens-lyon.fr
Last updated : Jul 2006 

If you modify the source file, please don't delete this header
***********************************************************************************************************
*/


//###########################CONFIGURATION########################################																	

//##########	NETWORK		#######
#define NEURONS 4000				//number of neurons		
#define LTH 1000.					//Length of the simulation in ms
#define AVERAGE_SYNAPSES 80			//Average number of synapses ( linked oriented neurons ) per neuron
#define INHIBITORY_PROPORTION 0.2	//Inhibitor neurons proportion, Warning !! : 0 <= INHIBITORY_PROPORTION <= 1 , type = double
//###################################


//#################  OUTPUT   ##################
		//Comment next line to see results in the terminal	
#define NO_TERM
#define FILE_OUT "Network.log"  //OUTPUT FILE
//#############################################

//####################  Constants ##############
#define REFRACT_ 5.		//Length of the refract. Period , ms. 
#define Taum 20.		//Membrane time constant , ms
#define Dge_ 0.6		//Excitatory synaptic weight
#define Dgi_ 6.7		//Inhibitory synaptic weight

//Be carrefull, you must write next values between ( ) 
#define El__ (-60.)		//Leak reversal potential, mV
#define Vt__ (-50.)		//Threshold  potential, mV
#define Vr__ (-60.)		//Reset potential, mV
#define Ee__ (0.)		//Excitatory reversal potential, mV
#define El__ (-60.)		//Leak reversal potential, mV
#define Ei__ (-80.)		//Inhibitory reversal potential, mV


//#############################################


//#################################################################################




//################For EXPERIMENTED USERS##########################################

/*Uncomment next line to use lower bound spiking time to insert a spike in the queue. 
As soon as the spike is pop(), the network computes the exact spiking time and insert the spike. 
If the line is commented, the network computes the exact spiking time each time.*/

//#define WITH_LOWER_BOUND

/*Don'T MODIFY next line*/
#ifdef WITH_LOWER_BOUND
/*-----*/

#define MAX_ITER 1	//# Max of iterations for computing the lower bound in the newton raphson method

/*Don't Modify Next Line*/
#endif 
/*----*/



#define AVERAGE_DAY_LENGTH (0.12) //Experimental value founded such as : in the queue, at less 80% of spikes must be in [t,t+AVERAGE_DAY_LENGTH] where t is a relative time
/*This option optimizes the computation time, but cannot modify the simulation*/
//##################################################################################





//################################## DON'T MODIFY NEXT LINES: #######################################"
#define INHIBITORY_NEURONS ((int)(NEURONS*INHIBITORY_PROPORTION))									
#define SYNAPSES_ (((double)NEURONS)/(double)AVERAGE_SYNAPSES)
#define EPS 0.00000001
#define REFRACT REFRACT_/taum	 
#define Taue (5./Taum) //***
#define Taui Taue	//***
#define SIM_LENGTH LTH/Taum //Double


#define Vt_ (Vt__- El__)   
#define Vr_ (Vr__- El__ )	
#define Ee_ (Ee__-El__)	
#define El_ 0.	
#define Ei_ (Ei__-El__)	
//#####################################################################################################