Abstract. We propose an event-driven framework dedicated to the design and the simulation of networks of spiking neurons. It consists of an abstract model of spiking neurons and an efficient event-driven simulation engine so as to achieve good performance

Usingagooddatastructureforeventordering(priorityqueue)isoftenseenasthecriticalpointwhenimplementinggeneral-purposeevent-drivensimula-tionframeworks[10].Inthecontextofspikingneuronsimulation,thatissuehasbeenaddressedinrecentworksonsimulationalgorithmsforsomespeci cspikingneuronsmodels,suchasintegrate-and- reneuronsin[7]or nitestateautomata-basedneuronsin[1].Itmustbenotedhoweverthatthesimulationenginehastodealwithtworatherdi erenteventtypes:thereceptionevents,oncescheduled,cannotbecancellednorrescheduledatanothertime,whilethe ringeventscanberescheduledorcancelledbyforthcomingreceptionevents.Thatparticularpointmeansthatitisalmostessentialtodesigntwodi erentdatastructuresaimedatproperorderingofeacheventtype.Asanexhaustivestudyofthepossibledatastructuresforimplementinggoodpriorityqueuesineachcaseisbeyondthescopeofthispaper,wewilljustpointoutaworthwhileoptimizationwhichisrelatedtothewaytheneuronsinteract,asexplainedbe-foreinsection2.2.Thesuccessorlistprovidesthebasicstoschedule,at ringtime,thereceptioneventsforeachsuccessorneuron.Whenusingtimedelayedreceptions,itisgenerallyworthwhiletomaintaintheselistsordered,soastoinsertinthependingeventlistonlytheeventassociatedwiththesmallertimedelay,thuse ectivelylimitingthepriorityqueuelength.Assoonasthiseventwillbeprocessed,thenexteventintheorderedlistwillbeexplicitelyscheduleduntilnoremainingconnectionisleft.

Anotherreasonthatcomplicatesthedesignoftheunderlyingdatastruc-turesreliesonthefactthatsomeevents(e.g.pulsereceptions)cansharethesametimestamp(synchrony).Untilnow,weassumedimplicitelythatthesim-ulationenginewasprovidedawayoforderingtheevents,i.e.sortingeventsbytheirtimestamps.Inordertofullyde nethesimulationofanetworkofspikingneurons,wehavethentoprovideanexplicitrulefororderingeventswithequaltimestamps.Multiplerulescanbeused,dependingonthechoiceoftheuser:data-structurebased(FIFO1-like),randomchoice,ormorespeci crulesde nedfromtheavailableparameters(synapseidentity,neuronidentity,typeofevent...).

4Conclusion

Wehavepresentedanevent-drivenframeworkthatconsistsofanabstractmodelofspikingneuronsandane cientevent-drivensimulationengine.Thisframeworkisdedicatedtothedesignandthesimulationofnetworksofspikingneuronsandpresentsahighlevelof exibilityandprogrammability.Thisallowstobuildandsimulatenetworksofclassicalspikingneuronssuchasintegrate-and- reneuronsorofmoreabstractneuronsspeci callydesignedfortheap-plicationathand.Wehaveusedthisevent-drivenframeworkinthesetwosituations:(1)forthesimulationofleakyintegrate-and- reneuronswiththeaimofcontourdetectionbysynchronization[6]and(2)forthesimulationof1Firstin, rstout