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.;
2015-01-01

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.
2015
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3148488
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact