The proposedworkintroducesaneuralcontrolstrategyforguidingadaptationinspikingneuralstructures acting asnonlinearcontrollersinagroupofbio-inspiredrobotswhichcompeteinreachingtargetsina virtual environment.Theneuralstructuresembeddedintoeachagentareinspiredbyaspecific partofthe insect brain,namelyCentralComplex,devotedtodetect,learnandmemorizevisualfeaturesfortargeted motor control.Areduced-ordermodelofaspikingneuronisusedasthebasicbuildingblockfortheneural controller. Thecontrolmethodologyemploysbio-inspired,correlationbasedlearningmechanismslike Spike timingdependentplasticity with theadditionofareward/punishment-basedmethodexperimentally found ininsects.Thereferencesignalfortheoverallmulti-agentcontrolsystemisimposedbyaglobal reward, whichguidesmotorlearningtodirecteachagenttowardsspecific visualtargets.Theneural controllers withintheagentsstartfromidenticalconditions:thelearningstrategyinduceseachrobottoshow anticipated targetingactionsuponspecific visualstimuli.Thewholecontrolstructurealsocontributesto make therobotsrefractoryormoresensitivetospecific visualstimuli,showingdistinctpreferencesinfuture choices. Thisleadstoanenvironmentallyinduced,targetedmotorcontrol,evenwithoutadirect communication amongtheagents,givingrobots,whilerunning,theabilitytoperformadaptationinreal- time. Experiments,carriedoutinadynamicsimulationenvironment,showthesuitabilityoftheproposed approach. Specific performanceindexes,likeShannon'sEntropy,areadoptedtoquantitativelyanalyze diversity andspecializationwithinthegroup.

Spiking neural controllers in multi-agent competitive systems for adaptive targeted motor learning

Patane L.;
2015

Abstract

The proposedworkintroducesaneuralcontrolstrategyforguidingadaptationinspikingneuralstructures acting asnonlinearcontrollersinagroupofbio-inspiredrobotswhichcompeteinreachingtargetsina virtual environment.Theneuralstructuresembeddedintoeachagentareinspiredbyaspecific partofthe insect brain,namelyCentralComplex,devotedtodetect,learnandmemorizevisualfeaturesfortargeted motor control.Areduced-ordermodelofaspikingneuronisusedasthebasicbuildingblockfortheneural controller. Thecontrolmethodologyemploysbio-inspired,correlationbasedlearningmechanismslike Spike timingdependentplasticity with theadditionofareward/punishment-basedmethodexperimentally found ininsects.Thereferencesignalfortheoverallmulti-agentcontrolsystemisimposedbyaglobal reward, whichguidesmotorlearningtodirecteachagenttowardsspecific visualtargets.Theneural controllers withintheagentsstartfromidenticalconditions:thelearningstrategyinduceseachrobottoshow anticipated targetingactionsuponspecific visualstimuli.Thewholecontrolstructurealsocontributesto make therobotsrefractoryormoresensitivetospecific visualstimuli,showingdistinctpreferencesinfuture choices. Thisleadstoanenvironmentallyinduced,targetedmotorcontrol,evenwithoutadirect communication amongtheagents,givingrobots,whilerunning,theabilitytoperformadaptationinreal- time. Experiments,carriedoutinadynamicsimulationenvironment,showthesuitabilityoftheproposed approach. Specific performanceindexes,likeShannon'sEntropy,areadoptedtoquantitativelyanalyze diversity andspecializationwithinthegroup.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/3148488
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