Advances in neurotechnology electronics and informatics revised selected papers from the 2nd interna

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Advances in Neurotechnology Electronics and Informatics Revised Selected Papers from the 2nd International Congress on Neurotechnology Electronics and Informatics NEUROTECHNIX 2014

October 25 26 Rome Italy 1st Edition Ana Rita Londral

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Ana Rita Londral

Pedro Encarnação Editors

Advances in Neurotechnology, Electronics and Informatics

Revised Selected Papers from the 2nd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2014), October 25–26, Rome, Italy

Biosystems&Biorobotics

Volume12

Serieseditor

EugenioGuglielmelli,CampusBio-MedicoUniversityofRome,Rome,Italy

e-mail:e.guglielmelli@unicampus.it

EditorialBoard

DinoAccoto,CampusBio-MedicoUniversityofRome,Rome,Italy SunilAgrawal,UniversityofDelaware,Newark,DE,USA FabioBabiloni,SapienzaUniversityofRome,Rome,Italy JoseM.Carmena,UniversityofCalifornia,Berkeley,CA,USA MariaChiaraCarrozza,ScuolaSuperioreSant’Anna,Pisa,Italy PaoloDario,ScuolaSuperioreSant’Anna,Pisa,Italy

ArturoForner-Cordero,UniversityofSãoPaulo,SãoPaulo,Brazil

MasakatsuG.Fujie,WasedaUniversity,Tokyo,Japan NicolasGarcia,MiguelHernándezUniversityofElche,Elche,Spain NevilleHogan,MassachusettsInstituteofTechnology,Cambridge,MA,USA HermanoIgoKrebs,MassachusettsInstituteofTechnology,Cambridge,MA,USA DirkLefeber,UniversiteitBrussel,Brussels,Belgium RuiLoureiro,MiddlesexUniversity,London,UK MarkoMunih,UniversityofLjubljana,Ljubljana,Slovenia PaoloM.Rossini,UniversityCattolicadelSacroCuore,Rome,Italy AtsuoTakanishi,WasedaUniversity,Tokyo,Japan RussellH.Taylor,TheJohnsHopkinsUniversity,Baltimore,MA,USA DavidA.Weitz,HarvardUniversity,Cambridge,MA,USA LoredanaZollo,CampusBio-MedicoUniversityofRome,Rome,Italy

Aims&Scope

Biosystems&Bioroboticspublishesthelatestresearchdevelopmentsinthreemainareas: 1)understandingbiologicalsystemsfromabioengineeringpointofview,i.e.thestudyof biosystemsbyexploitingengineeringmethodsandtoolstounveiltheirfunctioningprinciples andunrivalledperformance;2)designanddevelopmentofbiologicallyinspiredmachines andsystemstobeusedfordifferentpurposesandinavarietyofapplicationcontexts.The serieswelcomescontributionsonnoveldesignapproaches,methodsandtoolsaswellascase studiesonspeci ficbioinspiredsystems;3)designanddevelopmentsofnano-,micro-, macrodevicesandsystemsforbiomedicalapplications,i.e.technologiesthatcanimprove modernhealthcareandwelfarebyenablingnovelsolutionsforprevention,diagnosis, surgery,prosthetics,rehabilitationandindependentliving.

Ononeside,theseriesfocusesonrecentmethodsandtechnologieswhichallowmultiscale, multi-physics,high-resolutionanalysisandmodelingofbiologicalsystems.Aspecial emphasisonthissideisgiventotheuseofmechatronicandroboticsystemsasatoolforbasic researchinbiology.Ontheotherside,theseriesauthoritativelyreportsoncurrenttheoretical andexperimentalchallengesanddevelopmentsrelatedtothe “biomechatronic” designofnovel bioroboticmachines.Aspecialemphasisonthissideisgiventohuman-machineinteraction andinterfacing,andalsototheethicalandsocialimplicationsofthisemergingresearcharea,as keychallengesfortheacceptabilityandsustainabilityofbioroboticstechnology.

Themaintargetoftheseriesareengineersinterestedinbiologyandmedicine,and specificallybioengineersandbioroboticists.Volumepublishedintheseriescomprise monographs,editedvolumes,lecturenotes,aswellasselectedconferenceproceedingsand PhDtheses.Theseriesalsopublishesbookspurposelydevotedtosupporteducationin bioengineering,biomedicalengineering,biomechatronicsandbioroboticsatgraduateand post-graduatelevels.

AbouttheCover

ThecoverofthebookseriesBiosystems&Bioroboticsfeaturesarobotichandprosthesis. Thislookslikeanaturalhandandisreadytobeimplantedonahumanamputeetohelpthem recovertheirphysicalcapabilities.Thispicturewaschosentorepresentavarietyofconcepts anddisciplines:fromtheunderstandingofbiologicalsystemstobiomechatronics, bioinspirationandbiomimetics;andfromtheconceptofhuman-robotandhuman-machine interactiontotheuseofrobotsand,moregenerally,ofengineeringtechniquesforbiological researchandinhealthcare.Thepicturealsopointstothesocialimpactofbioengineering researchandtoitspotentialforimprovinghumanhealthandthequalityoflifeofall individuals,includingthosewithspecialneeds.Thepicturewastakenduringthe LIFEHANDexperimentaltrialsrunatUniversità CampusBio-MedicoofRome(Italy)in 2008.TheLIFEHANDprojecttestedtheabilityofanamputeepatienttocontrolthe Cyberhand,aroboticprosthesisdevelopedatScuolaSuperioreSant’AnnainPisa(Italy), usingthetf-LIFEelectrodesdevelopedattheFraunhoferInstituteforBiomedical Engineering(IBMT,Germany),whichwereimplantedinthepatient’sarm.Theimplanted tf-LIFEelectrodeswereshowntoenablebidirectionalcommunication(frombraintohand andviceversa)betweenthebrainandtheCyberhand.Asaresult,thepatientwasableto controlcomplexmovementsoftheprosthesis,whilereceivingsensoryfeedbackintheform ofdirectneurostimulation.Formoreinformationpleasevisithttp://www.biorobotics.itor contacttheSeriesEditor.

Moreinformationaboutthisseriesathttp://www.springer.com/series/10421

Advancesin Neurotechnology, ElectronicsandInformatics

RevisedSelectedPapersfromthe2nd InternationalCongressonNeurotechnology, ElectronicsandInformatics (NEUROTECHNIX2014),October25–26, Rome,Italy

Editors

Lisbon

Portugal

PedroEncarnação UniversidadeCatólicaPortuguesa

Lisbon

Portugal

ISSN2195-3562ISSN2195-3570(electronic)

Biosystems&Biorobotics

ISBN978-3-319-26240-6ISBN978-3-319-26242-0(eBook) DOI10.1007/978-3-319-26242-0

LibraryofCongressControlNumber:2015954344

SpringerChamHeidelbergNewYorkDordrechtLondon © SpringerInternationalPublishingSwitzerland2016

Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart ofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations, recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped.

Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthis publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.

Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthis bookarebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernorthe authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor foranyerrorsoromissionsthatmayhavebeenmade.

Printedonacid-freepaper

SpringerInternationalPublishingAGSwitzerlandispartofSpringerScience+BusinessMedia (www.springer.com)

Preface

Thepresentbookincludesextendedandrevisedversionsofasetofselectedpapers fromtheSecondInternationalCongressonNeurotechnology,Electronicsand Informatics(NEUROTECHNIX2014),heldinRome,ItalyfromOctober25to26, 2014.

ThepurposeoftheInternationalCongressonNeurotechnology,Electronicsand Informaticsistobringtogetherresearchersandpractitionersinordertoexchange ideasanddevelopsynergieshighlightingnewadvancementsofneurotechnology, eitheringeneralorregardingaparticularcase,application,orpathology.

NEUROTECHNIX2014wassponsoredbyINSTICC(InstituteforSystemsand TechnologiesofInformation,ControlandCommunication),heldincooperation withMedinRes MedicalInformationandResearch,NansenNeuroscience Network,SociedadePortuguesadeNeurologia(SPN),AssociaçãoPortuguesade EEGeNeurofisiologiaClínica(APEEGNC),NeurotechNetwork,SocietaItaliana deNeurologia(SIN),WorldFederationforNeuroRehabilitation(WFNR)andthe InternationalNeuralNetworkSociety(INNS),andincollaborationwithThe MarketplaceforResearchAntibodies.

Thecongressreceivedsubmissionsfrom19countries,inallcontinents.To evaluateeachsubmission,adouble-blindpaperreviewwasperformedbythe programcommittee,whosemembersarehighlyquali fiedresearchersinthe NEUROTECHNIXtopicareas.

NEUROTECHNIX’sprogramincludedpanels,specialsessions,and fiveinvited talksdeliveredbyinternationallydistinguishedspeakers:ConstantinA.Rothkopf (TechnicalUniversityDarmstadt,Germany),DanilProkhorov(ToyotaTech Center,UnitedStates),EugenioGuglielmelli(Università CampusBio-Medico, Italy);FeboCincotti(SapienzaUniversityofRome,Italy),andHermanoIgoKrebs (MassachusettsInstituteofTechnology,UnitedStates).

Wewouldliketothanktheauthors,whoseresearchanddevelopmenteffortsare recordedhereforfuturegenerations.

April2015AnaRitaLondral PedroEncarnação v

Organization

CongressCo-chairs

AnaRitaLondral,UniversidadedeLisboa,Portugal

PedroEncarnação,UniversidadeCatólicaPortuguesa,Portugal

AreaCo-chairs

AldoFaisal,ImperialCollegeLondon,UK

HermanoIgoKrebs,MassachusettsInstituteofTechnology,USA

VivianK.Mushahwar,UniversityofAlberta,Canada

AntonioPedotti,PolitecnicodiMilano,Italy

OrganizingCommittee

HelderCoelhas,INSTICC,Portugal

LuciaGomes,INSTICC,Portugal

AnaGuerreiro,INSTICC,Portugal

André Lista,INSTICC,Portugal

AndreiaMoita,INSTICC,Portugal

VitorPedrosa,INSTICC,Portugal

CláudiaPinto,INSTICC,Portugal

SusanaRibeiro,INSTICC,Portugal

SaraSantiago,INSTICC,Portugal

MaraSilva,INSTICC,Portugal

José Varela,INSTICC,Portugal

PedroVarela,INSTICC,Portugal

ProgramCommittee

AmrAbdel-Dayem,LaurentianUniversity,Canada

GregorAdriany,UniversityofMinnesota,USA

LyubaAlboul,SheffieldHallamUniversity,UK

PedroAlmeida,UniversityofLisbon,Portugal

JesúsB.Alonso,UniversidaddeLasPalmasdeGranCanaria,Spain

AlexandreAndrade,FaculdadedeCiênciasdaUniversidadedeLisboa,Portugal

JoseM.Azorin,MiguelHernandezUniversity,Spain

RenetaBarneva,StateUniversityofNewYork,USA

AmmarBelatreche,UniversityofUlster,UK

MartinBogdan,UniversitätLeipzig,Germany

MamededeCarvalho,InstituteofPhysiology,FacultyofMedicine-Universityof Lisbon,Portugal

AmitavaChatterjee,JadavpurUniversity,India

AlbertC.S.Chung,TheHongKongUniversityofScienceandTechnology, HongKong

KrzysztofCios,VirginiaCommonwealthUniversity,USA

EmmanuelConchon,UniversityofToulouse,IRIT/ISIS,France

JoseLuisContreras-Vidal,UniversityofHouston,USA

JimmyT.Efird,EastCarolinaHeartInstitute,USA

GaryEgan,MonashUniversity,Australia

MarkJ.Embrechts,RensselaerPolytechnicInstitute,USA

HugoFerreira,FaculdadedeCiênciasdaUniversidadedeLisboa,Portugal

PatríciaFigueiredo,InstitutoSuperiorTécnico,Portugal

AlexanderFingelkurts,BM-Science Brain&MindTechnologiesResearch Centre,Finland

AndrewFingelkurts,BM-Science Brain&MindTechnologiesResearchCentre, Finland

AnselmoFrizera,UFES,Brazil

PetiaGeorgieva,UniversityofAveiro,Portugal

MicheleGiugliano,UniversityofAntwerp,Belgium RodrigoCapobiacoGuido,UniversityofSãoPaulo,Brazil

ChungY.Hsu,ChinaMedicalUniversity,Taiwan

XavierIntes,RensselaerPolytechnicInstitute,USA

PasiKarjalainen,UniversityofEasternFinland,Finland JonghwaKim,UniversityofAugsburg,Germany

ConstantineKotropoulos,AristotleUniversityofThessaloniki,Greece HermanoIgoKrebs,MassachusettsInstituteofTechnology,USA OndrejKrejcar,UniversityofHradecKralove,CzechRepublic NarayananKrishnamurthi,ArizonaStateUniversity,USA

MartinLauterbach,HospitaldaLuz,Portugal

Seung-HoonLee,SeoulNationalUniversity,Korea,Republicof ChristosLoizou,Intercollege,Cyprus

NunoMatela,FaculdadedeCiênciasdaUniversidadedeLisboa,Portugal

AlessandrodeMauro,Vicomtech-IK4,Spain

PauloMendes,UniversityofMinho,Portugal

ClaudioMoraga,UniversityofDortmund,Germany

AntónioJ.R.Neves,UniversityofAveiro,Portugal

SusanaNovais,CarnegieMellonUniversity,USA

HalukOgmen,UniversityofHouston,USA

JoaoPapa,UNESP UniversidadeEstadualPaulista,Brazil GeorgePerry,UniversityofTexasatSanAntonio,USA VictorPikov,HuntingtonMedicalResearchInstitutes,USA

ArmandoJ.Pinho,UniversityofAveiro,Portugal

SusanaPinto,InstituteofMolecularMedicine FacultyofMedicine,Universityof Lisbon,Portugal

GabrielPires,InstituteforSystemsandRobotics Coimbra/PolytechnicInstituteof Tomar,Portugal

HemersonPistori,DomBoscoCatholicUniversity,Brazil MirjanaPopovic,UniversityofBelgrade,Serbia

AlesProchazka,InstituteofChemicalTechnology,CzechRepublic RafaelRaya,SpanishNationalCouncilforScienceResearch,Spain

RobertRiener,ETHZürich,Switzerland

WimL.C.Rutten,UniversityofTwente,TheNetherlands

RicardoSalvador,FaculdadedeCiênciasdaUniversidadedeLisboa,Portugal

DespinaSanoudou,MedicalSchool,UniversityofAthens,Greece

André Saúde,FederalUniversityofLavras,Brazil

MohamadSawan,EcolePolytechniquedeMontreal,Canada

FriedhelmSchwenker,UniversityofUlm,Germany

MárioForjazSecca,CEFITEC,DepartamentodeFisica,FCT/UNL,Portugal

TapioSeppänen,UniversityofOulu,Finland

FiorellaSgallari,UniversityofBologna,Italy

MiguelTavaresdaSilva,InstitutoSuperiorTécnico,Portugal

ArmandoJ.Sousa,FaculdadedeEngenhariadaUniversidadedoPorto,Portugal SundaramSuresh,NanyangTechnologicalUniversity,Singapore

JoãoManuelR.S.Tavares,FEUP FaculdadedeEngenhariadaUniversidadedo Porto,Portugal

CarlosM.Travieso,UniversityofLasPalmasdeGranCanaria,Spain

NicolaVitiello,ScuolaSuperioreSant’Anna,Italy

DezhongYao,UniversityofElectronicScienceandTechnologyofChina,China

ZeyunYu,UniversityofWisconsinatMilwaukee,USA

GüntherZeck,NMIattheUniversityTübingen,Germany

ChunZhang,TsinghuaUniversity,China

AuxiliaryReviewers

CamiloCortes,Vicomtech-IK4ResearchAlliance,Spain

EnriqueHortal,MiguelHernándezUniversity,Spain

InvitedSpeakers

FeboCincotti,SapienzaUniversityofRome,Italy

EugenioGuglielmelli,Università CampusBio-Medico,Italy

ConstantinA.Rothkopf,TechnicalUniversityDarmstadt,Germany

DanilProkhorov,ToyotaTechCenter,USA

HermanoIgoKrebs,MassachusettsInstituteofTechnology,USA

Contents

FromBiologicaltoNumericalExperimentsinSystemic Neuroscience:ASimulationPlatform ..........................1 NicolasDenoyelle,MaximeCarrere,FlorianPouget, ThierryViévilleandFrédéricAlexandre

Physically-BasedSimulationandWebVisualization of C.elegans BehaviouralExperiments .........................19 AndoniMujika,GorkaEpelde,PeterLeškovský andDavidOyarzun

Sielegans:Modelingthe C.elegans NematodeNervous SystemUsingHighPerformanceFPGAS .......................31 PedroMachado,JohnWadeandT.M.McGinnity

ProbabilisticTractographyUsingParticleFiltering andClusteredDirectionalData ..............................47 AdelinoR.FerreiradaSilva

Post-strokeRoboticUpper-LimbTelerehabilitation UsingSeriousGamestoIncreasePatientMotivation: FirstResultsfromArmAssistSystemClinicalTrial ...............63 CristinaRodríguez-de-Pablo,MašaPopović,AndrejSavić, JoelC.Perry,AitorBelloso,TijanaDimkić Tomić andThierryKeller

ComparisonofElectro-OpticalStrategiesforMimicking C.elegans NetworkInterconnectivityinHardware ................79 LorenzoFerrara,AlexeyPetrushin,CarloLiberale, DaraBrannick,BrianConnolly,PatMitchellandAxelBlau

SupervisedEEGOcularArtefactCorrectionThrough Eye-Tracking ...........................................99

P.RenteLourenço,W.W.AbbottandA.AldoFaisal

AnfMRI-CompatibleSystemfor3DOFMotionTracking ofObjectsinHapticMotorControlStudies .....................115

M.Rodríguez,A.SylaidiandA.A.Faisal

ComparingMethodsforDecodingMovementTrajectory fromECoGinChronicStrokePatients ........................125

MartinSpüler,FlorianGrimm,AlirezaGharabaghi, MartinBogdanandWolfgangRosenstiel

DetectionofGaitInitiationThroughaERD-Based Brain-ComputerInterface ..................................141

E.Hortal,D.Planelles,E.Iáñez,A.Costa,A. Úbeda andJ.M.Azorín

AuthorIndex ...........................................151

FromBiologicaltoNumericalExperiments inSystemicNeuroscience:ASimulation Platform

NicolasDenoyelle,MaximeCarrere,FlorianPouget,ThierryViéville andFrédéricAlexandre

Abstract Studyingandmodelingthebrainasawholeisarealchallenge.Forsuch systemicmodels(incontrasttomodelsofonebrainareaoraspect),thereisareal needfornewtoolsdesignedtoperformcomplexnumericalexperiments,beyond usualtoolsdistributedinthecomputerscienceandneurosciencecommunities. Here,wedescribeaneffectivesolution,freelyavailableonlineandalreadyinuse, tovalidatesuchmodelsofthebrainfunctions.Weexplainwhythisisthebest choice,asacomplementtoroboticsetup,andwhatarethegeneralrequirementsfor suchabenchmarkingplatform.Inthisexperimentalsetup,thebrainy-botimplementingthemodeltostudyisembeddedinasimplifiedbutrealisticcontrolled environment.Fromvisual,tactileandolfactoryinput,tobody,armandeyemotor command,inadditiontovitalinteroceptivecues,complexsurvivalbehaviorscanbe experimented.Wealsodiscussherealgorithmichigh-levelcognitivemodules, makingthejobofbuildingbiologicallyplausiblebotseasier.Thekeypointisto possiblyalternatetheuseofsymbolicrepresentationandofcomplementaryand usualneuralcoding.Asaconsequence,algorithmicprincipleshavetobeconsideredathigherabstractlevel,beyondagivendatarepresentation,whichisan interestingchallenge.

Keywords Simulation Computationalneuroscience Virtualreality

N.Denoyelle M.Carrere F.Pouget T.Viéville(&) F.Alexandre InriaBordeauxSud-Ouest,200AvenuedelaVieilleTour,33405,Talence,France

e-mail:Thierry.Vieville@inria.fr

F.Alexandre

e-mail:Frederic.Alexandre@inria.fr

N.Denoyelle M.Carrere F.Pouget F.Alexandre LaBRI,InstitutPolytechniquedeBordeaux,CNRS,UMR5800, Université deBordeaux,Talence,France

M.Carrere F.Alexandre InstitutdesMaladiesNeurodégénératives,CNRS,UMR5293, Université deBordeaux,Bordeaux,France

© SpringerInternationalPublishingSwitzerland2016

A.R.LondralandP.Encarnação(eds.), AdvancesinNeurotechnology, ElectronicsandInformatics,Biosystems&Biorobotics12, DOI10.1007/978-3-319-26242-0_1

1Introduction

Computationalneuroscienceisoftenpresentedasawaytobetterunderstandthe complexrelationsbetweenstructuresandfunctionsinthebrain.Particularly,these relationsarecomplexbecausetheyarenotsymmetrical:onestructureparticipates toseveralfunctionsandfunctionsaredistributedamongmanystructures.Thisisan importantlimitationagainstdevelopingamodelofastructureinisolation,whichis oftenthecaseincomputationalneuroscience.Thisisnotsufficienttoemulateone behavioralfunction,butparticipatesonlytostudyingsomeofitsproperties,with theriskofneglectingthekeyinfluenceofanotherstructureonthisfunction. Consequently,formodelingstudiesinterestedinintegrativeandbehavioralneuroscienceandintheemulationofbehavioralfunctions,thisanalysisisapleafor designingbrainmodelsincludingmanybrainstructures.Inaddition,inthe frameworkofstudyingbehavioralfunctions,thebrainmustalsobeconsideredasa complexsystemininteractionwiththebodyandtheenvironment.Twoimportant consequencescanbedrawn.The firstconsequenceisrelatedtothemodelitself. Additionalmodulesmustbeconsidered,toallowforthesensationandprocessing ofsignalsfromtheenvironment(exteroception)andalsofromthebody(interoception).Designingsuchanetworkofbrainstructuresandmodulesattheinterface withtheouterandinnerworldincludesnotonlyunderstandinghoweachsubsystem (visual,motor,emotional,etc.)worksbutalsohowthesesubsystemsinteractasa whole,toyieldemergingbehaviors,i.e.effectsthatresultfrominteractionsbetween subsystems.Thesecondconsequenceisrelatedtotheuseofthiscomplexsystem. Studyingandvalidatingfunctionalmodelsofbrainstructuresatamacroscopic behavioralscalecannotbeperformedwithrestrainedartificialstaticparadigmsbut requiresexperimentsincomplexenvironments,withrealisticsensory-motortasks tobeperformed,includinghigh-levelinteractivebehaviors(e.g.survivalstrategyin thepresenceofprey/predators)andlong-termprotocols(sincebothstatistical studiesandbiologically-plausiblelearningmechanismsrequirelongepochs).Such paradigmsaretoberelatedtobiologicalexperimentsconductedonanimals.These statementsarenotonlycharacterizingbrainmodelsworkingininteractionwith theirenvironment,theyalsogivestrongrequirementsonthetoolsthatmustbe designedtosimulatethesemodelsandtoexperimentthem.

Designingsuchtoolsisalsoanexcellentwaytoaddressatthesametimethetwo mainobjectivesofsuchbrainmodelsatthemacroscopicscale.Onehand,theyare intendedtoserveneuroscientistsasanewplatformofexperimentation,onwhich theycanapplytheirclassicalprotocolsofobservationandanalysisofanimalsatthe behavioralaswellaselectrophysiologicallevels.Itisconsequentlyimportantthat neuroscientistscanobservetheinneractivityofthemodels,astheyusetodofor examplewithelectrodes(butwecanimaginethatthisobservationindigitalmodels mightbemoreeasythanintherealbrain).Itisalsoimportantthattheycandefine classicalbehavioralprotocolsliketheydoinanimals(e.g.fearconditioning)in ordertoobservetheresultingbehaviorandthecorrespondingbrainactivation.

De finingsuchprotocolsimpliesthatthestructureoftheexternalworld(e.g.,maze,

foodmagazine)aswellasitsintrinsicrules(e.g.tonefollowedbyanelectricshock) shouldbeeasytodesign.

Ontheotherhand,thesetoolsarealsointendedtoservecomputerscientistsasa waytodesignartificialautonomoussystems,drivenbybrainmodels.Inthiscase,it isimportantforthesupposedpropertiesofthemodels(e.g.,capacitytolearn, robustnesstonoiseorchangingrules)tobeassessedbyrigorousevaluationprocedures,asitisdefi nedforexampleinthedomainofmachinelearning.Inthiscase alsoaneasyaccessmustbeproposedbothtotheinnercircuitryofthemodelsand tothespecificationoftheexternalworld.

Withinmindthisdoublegoalofofferingconvenienttoolstobothscienti fic communities,wereportinthispaperthespeci ficationsthatwehaveelaboratedand presentthecorrespondingsoftwareplatformthatwecallVirtualEnaction.Wealso introducethecasestudyofabehavioralfunctionpresentlyunderstudyinourteam, pavlovianconditioning,asanillustrationoftheuseofVirtualEnaction.Beforethat, somemorewordsmustbesaidtojustifytheneedforsuchaplatform.

2ProblemPosition

Concerningthenatureofsuchasimulator,realroboticsystemsareoftenusedand answerparticularlywelltothesecondrequirementaboutarealisticenvironment. However,buildingviableroboticsystemsandmakingthemevolveinrealistic environments(e.g.naturalsites)forlongperiodsoftime(e.g.severaldays)isjust tooexpensiveintermofcostandmanpowerinmanycircumstancesandparticularly duringearlyphasesofdevelopment.Furthermore,thegoalofsuchsimulationisnot onlytomakeademo,butalso,andmoreimportantly,tostudyandquantifythe behavioroffunctionalmodelsofthebrain.Asaconsequencewenotonlyneeda complex,long-term,realisticexperimentalsetup,butwealsoneedacontrollable andmeasurablesetupwherestimulicanbetunedandresponsescanbemeasured.In fact,realenvironmentcomplexityandparametersareintrinsicallydifficultwhennot impossibletocontrol.Thisisthereasonwhyweproposetouseadigitalsimulator implementingrealisticsurvivalandotherbiologicalscenarios.

Astepfurther,availablemacroscopicmodelsofbrainfunctionsarenotdesigned for “performance” buttoproperlyimplementphenomenologicalconceptsthathave beeninvestigatedinsomecognitiveorbehavioralframework.Theywouldtherefore have “nochance” inarealworld.Notethatrecentcomputersciencemechanisms designedwithoutanyconstraintregardingbiologicalplausibilitybutonlytowards finalperformancesarenowadaysprobablymoreefficientbutthatarenotrelevant regardingthebrainbehaviorexplanation.

Asaconsequencewealsoneedasetupwhichcanprovidea “simplifi edenvironment”,forsystemicmodelsofthebrainatthestateoftheartnottofail immediately.Wemustalsotakeintoaccountthefactthat(i)suchmodelsarerather slowtosimulate(unlesshugecomputerpowerisavailable),andthat(ii)theyare

notsupposedtofocusonpreciseissuesregardinglow-levelsensoryinputormotor outputbutonintegratedcognitivefunctionsandtheresultingbehaviors. This,inadditiontotechnicalconstraints,yieldsthreekeycharacteristics:

1.Noreal-timebutalook-and-moveparadigm:Themainrestrictionweproposeis tohavethesimulatorrunningata “slower” time(i.e.usingseveralsecondsto simulateonereal-timesecond)andalsotoconsiderdiscretetimesampling.This seemsobviousasfarasdigitalsimulationisconcerned,butintermsof underlyingframework,thishasseveralconsequences(e.g.,givingupthepossibilityforahumanobservertointeractwiththesimulation,restrainingto clock-based(andnotevent-based)dynamicalmodels,etc.)[1].

2.Norealroboticcontrolbutonlymotorcommand:Sinceinthenervoussystem motorcontrolseemstobeahierarchicalsystemwithhigh-levelmotorcommands,whiletheirclosedloopexecutionisdelegatedtotheperipheralmotor system[2],wemayaccepttoonlysimulategestureanddisplacementatarather symboliclevelsuchas “stand-up” or “turn90° rightward”.Thisindeedcancels thepossibilitytostudysharpphenomenaofmotorinteractionswiththeenvironmentbutallowsustoconcentrateonhigh-levelcontrolsuchasaction selectionmechanismandmotorplanning.

3.Complexbutnotnecessarilynaturalvisualenvironment:Thethirdmain restrictionweproposetoacceptistoconsideracomplexvisualenvironment (withvisualtextures,severalobjectsinmotion,etc.)butnottoinvestinthe simulationofarealisticnaturalscenesimulation.Thereasonofthischoiceis thatnaturalimagevisionisanissuealreadywellstudied[3].Thegeneral conclusionisthatbiologicalvisualsystemsaretunedtonaturalimagestatistics, decomposedbytheearlyvisualfront-endinsuchawaythathigher-levelvisual inputonlyrelatesoncuesorthogonal(inawidesense)tonaturalimage statistics.Inotherwords,thejobregardingthisaspectisdonebyearly-vision layersandwemayconsidermorestylisticvisualcuesatahigher-level. Dependingonthestudy,wemayalsowishtoworkoneitherapixelicora symbolicrepresentationofthevisualscene.See[4]fordetailsofhowthe early-visualsystemimplementssuchdualrepresentation.

3SystemDescription

Weconsiderthata “brainy-bot”,i.e.theimplementationofaglobalmodelofthe brainfunctionalities,interactswithitsenvironmentwiththesimplegoalofsurviving.Ourobjectiveistosimulatethesensory-motorinteractionsofthisbotwith respecttoitsenvironment.ExamplesofsuchsurroundingsareshowninFig. 1 Survivalispreciselydefi nedasmaintainingvitalvariablevaluesincorrect ranges,asformalizedin,e.g.,[5].Inourcontext,health,food,water,energy,and 4N.Denoyelleetal.

Fig.1 Twoexamplesofdigitalexperimentalenvironmentsforsystemicneuroscience. Left A minimalenvironmentcorrespondingtoastandardmazereinforcementlearningtask(source oneof ourvirtualenactionbuilt). Right Acomplexenvironmentinwhichsurvivalcapabilitiesaretobe checked(source landscapeencounteredwhenplayingwiththestandardgame)

oxygenarethevitalstatevariables.Thebothasaccesstothesevalues.These variablesdecreaseorincreasewithtimesincethebotbodyissupposedtoconsume therelatedresourcedependingonitsactivity,orchangeinthepresenceofan externalevent(e.g.energyduringapredatorattack),andrestoreresources. Restoringresourcesisobtainedeitherbyingestingitemsortakingrest(i.e.,when makingthechoiceofstoppinganaction,withthebenefitofvitalresourceincrease andthedrawbackofnotactingontheenvironment,thismightbeashort-term policychoiceifvitalvariablesarelowandisamiddle-termpolicychoice otherwise).

Theenvironmentstructureisverysimpleandmadeof “blocks”.Eachobjectin thisenvironment(includingthe floor,relief,.)isacollectionofblocks.Eachblock isdefinedbyits3Dposition,orientationandsize,roughness,hardness,temperature, andcolor.Someblockscorrespondtoeatableordrinkableresources.Otherentities correspondtoobjectsthatinteractwiththebot(e.g.predatorsthatattackthebotor lavatoavoid).Atthislevel,survivalcorrespondstoavoidorkillthepredatorsand findresourcestoeatordrink.

Thebotanatomyisfunctionallymadeofabody,anarmandanhead/eye.It carriesabagwithobjectsfoundinitsenvironment.Thebodycanmoveatagiven speedandinagivendirection,andalsorotateateachsteptoagivenangle.Itcan alsojumpuptoagivenrelativeheight,orkneedowntotakearest.Thehead/eye cangazeinagivenyaw/pitchdirection.Thearmcanperformprede finedsymbolic gestures:takeanobjectinhandoutofitsbag,puttheobjectinhandintoitsbag, eitherdroporthrowtheobjectinhand,ingesttheblockinhand(foodorwater), grasptheobjectinfrontofhim.Thearmcanalsoattack(quanti fiedbyaforcevalue andwithorwithoutanobjectinhand)theobjectinfrontofhim.Thisisthe completedescriptionofthebotmotorcommandoutputinthepresentcontext.

Thebotsensoryinputcorrespondstocuesrelatedtotheblockswhicharearound it.Thetouchcuesallowthebottoestimatetheroughness,hardnessandtemperature

Fig.2 Left Thesoftwareinterfaceistrivial:eachimplementationofabrainy-botprovidesan initializationroutine initBot() andastopfunction stopCondition(),whileateach time-stepthe brainDo() methodiscalled. Right Allstatus,inputandoutputfunctionalitiesare availableviaasimpleAPI.Forinstance,handorvitalinput,bodyandheaddisplacement,gestures ofresourceinjectionandattackagainstpredatorareshown

oftheobjectinhand.Theolfactorycuesallowtoestimatethesmelltypeand intensityofobjectsclosetoit(computedbyintegratingaveragevaluesoverthe blockscharacteristics).Atthebotlevel,pixelicvisionprovidesanimageofthe visual fieldview(i.e.,calculatingtheblockstextureandcolorprojectionon thevirtualretina).Finally,theproprioceptivecuescorrespondtogazedirection estimation.

Inordertoquantifythebotbehavior,theinterfaceprovidesanadditionalaccess tothebotabsolutepositionandorientationinspace.Symbolicvisionisalso available,asalistofblocksvisibleinitsvisual field,withanaccesstotheblock characteristics.

Anadaptationofthe https://minecraft.net/minecraftminecraft opengamesoftware yieldstheproperanswertothiswish-listandthesocalled http://virtualenaction. gforge.inria.frvirtualenaction isanopen-sourcefree-licenseimplementationof thesespecifications.Eachuserbuysaend-userlow-cost https://account.mojang. com/documents/minecraft_eulamojang license(<20$)for https://minecraft. net/minecraftminecraft,while http://virtualenaction.gforge.inria.frvirtualenaction is freeofuseundera http://www.cecill.info/licences/Licence_CeCILL-C_V1-en. txtCeCILL-C license.Fully-documentedscriptsfacilitatetheinstallationofthe softwarebundleunderLinuxOS.Thebotisimplementedineither C/C++ ,or possiblyin Python (viaanexisting http://www.swig.orgswigwrapper )orother computerlanguages.Itusesa http://virtualenaction.gforge.inria.fr/VEdoxygen/html/ da/d5b/classbotplug_1_1BotAPI.html simpleAPI,asdescribedinFig. 2. Furthermore,inordertobothobserveinslow-downreal-timethebotbehaviorand interactwiththedigitalexperiment,agraphicuserinterfaceisavailableasdescribed inFig. 4.ThesimplearchitectureoftheplatformisschematizedinFig. 3.

Fig.3 Theplatformarchitecture.Attheuserlevel,onlythenotionof(i)gameserverwherethe environmentanditsmobileobjectsinteractionsarecomputed,(ii)gameclientwherethe game-playisrenderedand(iii)brainy-botwherethebrainmodelsimulationisissuedhavetobe takenintoaccount

Fig.4 Thesoftware’sgraphicuserinterface.Inordertoobserveandcontroltheexperiment,a viewofthebotvision,status,inputandoutputisavailableviainteractivepanels.Itisalsopossible to “cheat” bycontrollingthesevariablesvalues,inordertodebuganexperimentandunderstandin detailswhathappensinsuchacomplexsysteminteraction

4Brainy-BotGenericFunctionality

Evenwhenrestrainingtofunctionalmodeling,itisnotpossibletosimulateall sub-systemsofthebrainatthesamelevelofdetails.Wethusmustrepresentpartsof thesystemwitheffi cientmodels,butwithoutbiologicallyplausiblemodels.Afewsets ofgenericfunctionalitiesaretobeproposedtothisend.Letusdiscussthreeofthem.

1. AssociativeMemory:Anysystemhasnotonlytotakeintoaccountthepresent inputandoutputinordertogenerateaproperbehavior,butalsotostoreandrecall pastinformation.Informationishoweverneverexactlythesameandsimilarpieces ofinformation(i.e.,beingalmostequaluptoagiventhreshold)havetobeconsideredasauniqueitem.Suchamemorymustnotonlystoreandretrieveinformation,butalsodefi neanotionofindistinguishableinformation.

Inourcontextthishasaspecialmeaning:theinput/outputinformationcorrespondstoahierarchicallogical-structure,1 mixingquantitativeandqualitative information,2 andusing3 structureslike set or sequence,inadditionto tuples as containers.Moreprecisely:

Notionofdivergence. Asaconsequence,itisnotobvioustodefinea “distance” betweentosuchstatevalues(i.e.thewholeinput/outputdatastructure).Butthisis requiredtodecideiftheyaredistinguishableornot.Ourclaimisthatthecorrect

1Suchastructureisofcommonuseincomputerscience:Itcorrespondsto,e.g.,a XML logical-structure,orthe Json syntaxunderlyingdatamodel.

2Thefollowingscalarvaluesareused: bool foraneithertrueorfalseBooleanvalue, percent foraproportionvaluebetween0and1, degree foranangleindegree, index standsfora non-negativeintegerindex, count standsforanon-negativeintegercountvalue, color stands foraRGBcolorvalue, real standsforaunboundeddecimalvalue.

3Setsareunorderedlistswithoutrepetition,Sequencesaresequentiallists,andt-uplesmapstrings, ornames,totheircorrespondingvalue.

notionisthenotionof premetric4 or divergence (seeFootnote4),fortwostate values s1 and s2:

d ðs1 ; s2 Þ 0and d ðs1 ; s2 Þ¼ 0 , s1 ¼ s2 asbeingtheweakestconceptthatallowstostatethattwovaluesaredistinguishable ifandonlyif d ðs1 ; s2 Þ e [ 0,forsomethreshold ε.Itappearsthatallpremetrics weneedarealwayssymmetric(i.e.,itisasemi-metric(seeFootnote4)).Inour context,thedivergencecorrespondstoaEuclideandistancefora position , angulardistanceforan orientation ,whilethedivergencebetweentwotuplesis simplydefinedastheweightedsumbetweeneachtuplevaluesdivergence,fora givensetofweights,whilethedivergencewithrespecttoaundefinedvaluemustbe definedforthisdefinitiontobecoherent.Atthisstage,divergencecomputationis linearwithrespecttothestatestructuresize.Theintroductionofdissimilarity computationoftuplesisthecauseofbeinginthecontextofasemi-metricandnota completemetricwiththetriangleinequalityveri fied.

Astepfurther,weneedpairing(seeFootnote4)divergencebetweentwo unsortedsequences(i.e.,itcorrespondstotheclassical “stablemarriageproblem” (seeFootnote4)).Thistoolisrequiredtodefinehowtwosetsofblocks(e.g.,for symbolicvisioninput)match,inorder,e.g.,torecognizeavisualobject.This pairingisnotadistanceinthegeneralcase.

Wealsoneedtodefineaneditdistance(ageneralizationoftheLevenshtein(see Footnote4)distance,calledtheVictor-Purpura(seeFootnote4)metric)todefine howtwotemporalsequencesmatch.

Bothpairingandeditdivergencecanbeimplementedasalgorithmsquadratic withrespecttothesetorsequencesize.

Letusalsomentionthatinformingthebotaboutitsabsolutepositionandorientationoraboutsymbolicinformationofthescene,asitisthecase,isawayto shortcutitslocalizationandsensorymodulesandprovideintegratedcognitive information,withoutconsideringtherelatedcomputationalissues.

Withthissetoftools,themechanismofassociativememoryofstatevaluesis entirelyspeci fied,anditmustbepointedoutthatsincethespaceofstateshasno specialstructurethealgorithmsallowingtoretrieveavalueinthememory(namely theclosestvaluewhosedivergencewithrespecttotheinputvalueisminimal),or insertanewvalue(ifnotalreadyinthememoryinanindistinguishableform),or deleteit,cannothavelessthanacomplexityof O(memory-size),sincehash-coding isnotcompatiblewithretrievingasimilarvalue.

2 StateCategorization:Agenerickeycognitivefeatureisthecapabilityto “extract” symbolicinformationfromabundleofquantitativeorqualitativevalues. Duringasupervisedlearningphaseprototypesofstatevalueswiththecorrespondingknowncategoryareregistered.Then,foranincomingstatethemost plausiblecategoryiscalculated.Suchmechanismincludessensoryeventsdetection

(e.g.,detectthepresenceofpredatorfromsensorycues),objectlabeling(e.g.,an elementasaresourcetoingest):Suchexamplesarequalitativevalues. Abiologicallyplausiblesupportvectormachinemechanismisavailabletothisend, withversatileuses[6].Thisalsoincludesquantitativevalues(e.g.,associatea rewardtoagivensensationoraction),andsupportvectormachinesalsoincludethe capabilitytocategorizeusinga floatingpointvalue.

Inourcase,theissueisthefollowing:Ifasimplenearestneighbormechanism (i.e.,thecategoryofanincomingstatevalueisset(orinterpolated)usingthe categoryofthenearestneighbor(orneighbors))isused,thenthenotionofdivergenceintroducedbeforeissufficient.However,suchamechanismisknownas beingtheworstintermofgeneralization(i.e.,inferringcorrectlythecategoryfor incomingstatevaluenotcorrespondingtotheprototypes)androbustness(i.e., providingreliableresultsevenifsomespuriousprototypes).

Notionofgradient.Inordertointroduceabettermechanismofcategorization thatwilloptimizeacriterion,weneedtodefinethenotionof gradient,i.e.,small variationofagivenstatevalue.Oneimpedimentisthefactthatsomevariablesare discretevariables.Inthepresentcase, index and count arenottobeoptimized, thusremain fixed.Booleanvariables bool howevercorrespondtoeffectivechoices ormeaningfulinformation,andmustbetakenintoaccount.Asaconsequence,we wouldhavetodealwiththecombinatorycomplexityofdiscretevaluesoptimization,knownasNP-hard.Toovercomethiscaveat,wesimplyproposetoembed each b = bool valueinbounded v ¼½0; 1 value,withtheobviouscorrespondence:

v ¼ if b then1else0;

withthesespeci ficationchoices,givenastate s wecandefi neasmallvariation @ s as smallvariationsofallquantitativescalarvalues(namely real,color, degree,percent and bool representedbya ½0; 1 value).Givenastatecriterion,i.e.,arealvaluefunctionofthestate,wecanoptimizethiscriterionwith respecttoquantitativevalues.Thisconstructionimplicitlymapsastatetoa N-dimensionalrealspaceofthedifferentquantitativevariables.Thisisnotexactly anEuclideanspacesincerealvaluelikecolororangleliesinsomenon-Euclidean metricspaces,butusualdifferentialmethodscanbeused.

Followingthemethodin[6]thatsimplyrequiresoptimizationwithrespecttothe prototypesstatevalue(andcontrarytotheoriginalSVMalgorithmthatproceedina dualspace,whichwecannotdefi neheresincewedonothaveaconcretemetric) wethuscanimprovetherawnearestneighbormechanism,byeliminatingor merginguselessprototypes,andmodifyingprototypesvaluestomaximizethe marginbetweenclasses.Itmustbeunderstoodthatthismethodwillnot(i)perform qualitativeoptimization(e.g.,willnot findthatwemustaddordeleteablockto representanobject)and(ii)issub-optimalsincewereuseawell-foundedmethodin acontextwherenotallassumptionsareveri fied.

3. GestureInterpolation:Atafunctionallevela “behavior” (i.e.,acomplex gesture)canbespecifi edas findingapathfromaninitialstate(e.g.,beinghungry whilefoodisknowntobepresentelsewhere)toa finalstate(e.g.,havingthefood

ingested)takingconstraintsintoaccount(e.g.,avoidingormovingasideobstacles ontheway).Genericspeci ficationofsuchproblemanduniversalalgorithmsto solvethemexist,inrelationwithharmoniccontrolwhichisabiologicallyplausible framework(i.e.,withfullydistributedcomputationbasedondiffusionmechanism) [7].Thisalsocorrespondstothefactthat,inthemotorcortex,amotorcommandis representedbyits finalstateor “endpoint”,whilethetrajectorygenerationtoattain this finalstateisgeneratedelsewhere.

Inordertodefi nesuchamechanismwesimplyneedthenotionofstatedivergenceandstategradientintroducedpreviously.Obstacletoavoidissimplydefined byconstraints(e.g.realvaluefunctionofthestatethatmustremain,say,positive). Thegoalisattainedwhenthedivergencetothe fi nalstateisbelowtheindistinguishabilitythreshold.Thealgorithmproposedin[7]buildsharmonicpotential, hereintherealspaceonwhichthestatequantitativevariablesaremapped.It requirestheoperationofprojection(ofastateontoaconstraint)todefi nea repulsivepoint,andsuchalgorithmreducestoanoptimizationproblem,implementablethankstoourdifferentdesignchoices.

Thoughwepresentthesimplestaspectofthisclassofmethods,harmoniccontrol iseasilylinkedtooptimalcontrol[8],inlinkwithreinforcementlearningconsideringanon-finiterealisticstate-space[9].

Asaconclusion,thissectionhasanalyzedtowhichextentsomepowerful genericmachinelearningtechniquescanbereusedinthiscontextandadaptedto thesymbolichierarchicaldatastructuredefiningthebotinputandoutput.Thetwo mainideasaretouseaweakernotionofdistanceandacceptadaptationmechanism ofthestaterestrainedtoquantitativevalues.Furtherdevelopingthelinkwith machine-learningalgorithmsappliedonsuchdatastructureinordertodefinehigh levelalgorithmicersatzofcognitivefunctionsisaperspectiveofthiswork.

5NeuroscienceApplication

Letusnowdiscusshowthissetupconstitutesasteptowardsintegrativeneurosciencedigitalexperimentation.

Firstofall,letuscomparethisprojectwithcomplementaryconnectedprojects. The http://www.animatlab.comAnimatLab isasoftwareplatformallowingtosimulateembodiment(bio-mechanicalsimulationofabody)allowingtoinvestigatethe relationbetweenbrainandbody[10].Furthermore,itproposesaneuralnetwork architecturefortheimplementationofcognitivefunctions.Onthecontrary,the presentframeworkhasaratherlimiteddescriptionoftheembodiment,butamuch largersetofpossibleinteractionswiththeenvironment.Astepfurther,notonly artificialneuralnetworkmodelsareusableinVirtualEnaction,whereastheinterface withanyexistingneurosciencesimulationtool(e.g.,pythonbasedneuralsimulators,see[11, 12])isstraightforward.Thisfeatureisessential,sincewemustsimulatethesystematdifferentmodelingscales,asdevelopednow.The http://www. openrobots.org/morseMorse isagenericsimulatorforacademicrobotics,with

realistic3Dsimulationofsmalltolargeenvironments,allowingcompleteintegrationwithanysimulationtools.Itoutperformsconcurrentsystemslike http:// www.cyberbotics.com/overviewWebot.TheinterestofVirtualEnactionwith respecttoMorseistwofold:Sincewetargetintegrativecognitivetasksofsurvival whichisexactlywhathappenswiththeMinecraftenvironment,usingthisspecializedproductisfarsimplerandsomehowmoredemonstrative.Intermsof performances,asbeinglesssophisticated(usingasimplified3Drendering,while Morsehasall3Dcapabilities)andbeingagnosticintermsofprogramminglanguages(i.e.,allowingfastC/C++implementationofusermodules,whereasMorse islimitedtoPythonscripts)theVirtualEnactionplatformisaprioriexpectedtobe moreeffi cientintermsofCPUusage.However,withalargerhumanpowerallwhat hasbeendevelopedwithinVirtualEnactioncouldhavebeendevelopedinMorse. Themainapplicationregardingneuroscienceistotest cognitivecomputational models inrealisticconditions.Verysimply,abehavioralexperimentisperformed onananimalmodeloronhumans,usuallywithatrainingphase,themeasurement phaseandthedataanalysis.Inordertoformalizetheobtainedresultacomputationalmodelisproposedthatexplainsthedata,andmayalsohaveprediction regardingotherfalsifiablefutureexperiments.Thepresentsoftwareandmethodologicaltoolallowustoproposetoenhancethisverygeneralparadigminthe followingdirections:

• Testthemodelpredictionforseveralothersexperimentalconditionsormodel parameterranges:Theideaistoreproducetheexperimentalsetupinthisvirtual environment(e.g.,adelayedrewardtask,anexplorationparadigm)andconnect thecomputationalmodeltothisparadigm.Asforusualcomputationalmodeling,thechosenbiologicalmeasurements(e.g.neuralactivity,tasksuccess performance)aresimulatedwhenrunningthemodel.Suchmodelisindeed expectedtoreproducequalitativelythegroundtruth,foragivensetofparametersvalues.Astepfurther,itisveryimportanttonumericallyverifywhat happenswhenmodifyinganyquantitativeorqualitativeparametervalue.Ifthe numericalsensibilityissostrongthattheresultscannotbereproducedforsome tinyparametervariation,themodelismeaninglessbecausebiologicalvalues makesenseasanumericalrange,notasinglenumber.Ifthenumericalsensibilityissoweakthatanyparametervalueproducestheexpectedresult,this parameterismeaninglessandasimplermodelverylikelyexplainsthesamedata set.Thekey-pointhereisthatsuchpredictiveveri ficationisnotonlygoingtobe possible,givena fi xeddataset,butforanydatasetobtainedduringvirtual environmentsimulations.Inotherwords,thecomputationalmodelvariantsare goingtobealwaystestedinsitu.Withnopracticalboundsontheexperimental variants(e.g.,numberoftrials,sensoryinputprecision,taskcomplexity).

• Designnewexperimentalparadigmstoconfrontthecomputationalmodelto falsi fiableconditions:Buildinganexperimentconsideringananimalmodel, trainingtheanimalsforweeks,restartingfromthebeginningifitappearsthat thereisanunexpectedtrap(e.g.,thetaskistoosimpleorunfeasible,ordoesnot allowtodiscriminatebetweentwoconcurrentmodels)maybeahugework.

Startingtodesigntheexperimentinavirtualenvironmentcompletelychanges themethod:thehypothesizedcomputationalmodelis fi rsttestedinsilico(i.e., neitherinvivo,norinvitro,butinasoftwareenvironment)andonlyconfronted tothebiologicalrealityinasecondstage.Theworkplanisinvertedwithrespect tousualneurosciencestudies,butincomputationalengineering(e.g.,designing newairplanes)thisisexactlythewayitgoesuntilafewdecades.Itishowever notnewinneuroscience,atthescaleofmesoscopicbrainmapstudy(seee.g., [13]or[11]).Thekeypointhereisthatsuchapproachisnowpossibleatthe behaviorallevel,consideringsensory-motorinteractionswithasimpleor complexenvironment.Suchprocessalsoobviouslyyieldsaparsimonioususeof animalmodels.Astepfurther,itlaysdownthechallengeofperformingrealistic experimentwithacomputationalmodelofthebrainbehaviorandnotonly consideringtoysituationswheretheplausibilityofthemodelcannotbe checked.

6CaseStudy:PavlovianConditioning

Thedegreeofequivalencebetweensimulationoutcomeandneuroscienceexperimentalresultsisakeyissue.Thisisthereasonwhywehavechosenasoftware platformwhereusualbehavioralneuroscienceexperimentscanbereproduced “asa whole”.Suchclassicalexperimentsincludepavlovianandoperantconditioning, spatialnavigationwithtasksinvolvingthefocusofattentionandmulti-sensory integrationandotherhighlevelcognitivefunctionsinvolvingworkingmemoryand planning.Themainbrainstructuresinvolvedinsuchtasksarethethalamusand posteriorcortexforsensoryprocessing,thebasalgangliasystemforselectionof actionanddecision,thehippocampusforepisodicmemoryandtheprefrontal cortexforthetemporalorganizationofbehavior.Dependingonthetask,othermore specializedstructuresliketheamygdala,cerebellum,superiorcolliculus,hypothalamusandothersmayalsobeconsidered.Thesetasksareoftenrelatedtofundamentalsurvivalprograms(nourishment,reproduction,integrityofthebody)andare organizedtowardsgoals,de finedfromtheenvironment(acquiringappetitivegoals oravoidingnoxiousones)orfromthebody(satisfyinginternalneeds),which endowsthemwithastrongbehavioralecologicalanchoring.

Asanillustration,weintroducepavlovianconditioning,presentlystudiedinour team,includingtheintegrationofrelatedcomputationalmodelsinVirtualEnaction. Pavlovianconditioningisafundamentallearningcapability,allowingtoidentify aversiveandappetitiveeventsintheenvironmentandalsoexploitedinmany complextasks.Thislearningreliesontheabilityofanimalstoautomaticallydetect biologicallysigni ficantstimuli(alsocalledUS,unconditionalstimuli)andtotrigger correspondingreflexes.Pavlovianlearningoccurswhenaneutralstimulus(conditionedstimulus,CS)reliablypredictstheoccurrenceoftheUS.Afteracquisition, theCSpresentedalonewillalsotriggeraresponseandallowstheanimalto

anticipatethenatureoftheUStocome.Forexample,inthecaseoffearconditioning[14],theUScanbeanelectricshockautomaticallytriggeringfreezing. SubsequentlytothepairingoftheUSwithaCSliketheauditoryperceptionofa tone,theCSalonewillevokefreezing.Itwillalsoallowtheanimaltoanticipatethe natureoftheUSandpreparemoreadaptedbehaviorlikeanescape,dependingon thenatureofthetaskandtheenvironment.

Fearconditioninghasbeenextensivelystudiedbecauseinitsbasicforms,it involvesarathersimpleandwellknowcerebralcircuit,associatedtoaccessible stimuliintheenvironment.Theamygdalaisthekeystructurefortheacquisition andexpressionoffearconditioning[15].Itslateralnucleusreceivesexteroand interoceptiveinformationfromthethalamusandthesensorycortexandisreported toperformtheacquisitionofCS-USassociationsandtoactivateitscentralnucleus, amotorstructureresponsiblefortheexpressionofpavlovianresponses.

Thissimpleassociativelearninghasalsobeenstudiedbecausebeyonditssimple expression,itdemonstratesnontrivialcharacteristics.Forexample,theCScanbe morecomplexthanasimpletone,becauseitincludesatemporalstructure(delays) orspecifi cspatialcharacteristicslikethecontextoflearning(theroominwhich conditioningoccurs)orthecompositionofseveralstimuli(compoundstimuli).In thiscase,thesensorycortexisnotabletosupplytheinformationinanadapted configurationandthehippocampushasbeenshowntobenecessaryforthe acquisitionofthisCS-USassociation,throughitsprojectionstothebasalnucleusof theamygdala[16].Theinvolvementofthehippocampusisnotreallysurprising here,sincethisstructureisknownforitsroleinepisodiclearning,anotherkindof associativelearningofarbitraryrelationsbetweenfeaturesincludingspatialand temporalcontexts[17].

LearningCS-USassociationcanalsobecomemorecomplexwhentheassociationisnotdeterministicbutcanvaryintime.Thiscanbeduetostochasticity(the associationisprobabilistic)andalsotochangesintherulesofourdynamicand changingworld,asitisobservedinthecaseofextinction[14].Experimentsin neurosciencehaveshownthatwhenapredictedUSnolongeroccurs,thisdoesnot correspondtotheremovalofthelearnedCS-USassociationbuttoitstemporary inhibitionbythemedialprefrontalcortex,learninghistoryofperformancein behavior.Particularly,thismeansthat,inthecaseofrenewal(theoldrulebecomes validagain),reacquisitionoftheCS-USassociationisimmediatebecauseitwasnot forgottenbutonlyinhibited.

Pavlovianconditioningisalsostudiedforitsimpact,atasystemiclevel,onother kindsoflearninginthebrain.Withoutgoingtoodeepindetails,itcanbementionedthatsuccessesanderrorsofUSpredictionhavealsoadeepimpacton episodiclearninginthehippocampusandonperceptivecategorylearninginthe sensorycortexandalsoinoperantconditioning.Itmustbealsounderlinedthat stimulilearnedbythepavlovianprocedureareoftenre-usedasgoalsinoperant conditioning.

Thisshortoverviewofpavlovianconditioningwasonlymeanttoindicatethat behavioralsequencesrelatedtothiskindoflearningcanbestudiedatdifferent levelsofcomplexity,whichisthecaseinneuroscience,dependingonthecontextof

theexperiment.Accordingly,ourVirtualEnactionplatformmustadapttothese contexts.Initssimplestform,theenvironmentincludessimplesensoryCSandUS andthebodymustexpressstereotypedavoidanceorattractionmovements.Inthis case,ourbrainy-botintegratesamodeloftheamygdalaaswecan findinthe literatureoraswedevelopedintheteam.Inmorecomplexcases,wemightfor exampletakeintoaccountthespatialcontextinwhichtheCSisperceived,which requirestointegrateafunctionalitylikeepisodicmemory.Inthiscase,wecan integrateagenericfunctionalityofsymbolicinformationmanipulationasdescribed inSect. 4 oramoreadvancedneuronalmodelofthehippocampus,dependingon theaspectsthataretobemoredeeplystudiedinrelationtobiologicalexperiments. Theprinciplewouldbethesameforintegratingamodelofthemedialprefrontal cortexorasimplestatisticalanalysisoftherecenthistoryofreceivedandpredicted US,incaseofanexperimentrelatedtoanextinctionprocedure.

Inadditiontothedesignofthesystemadaptedtothetaskunderstudy,another criticalstepisaboutthedesignofthescenariostobetested.Here,scenariostands forthedescriptionoftheenvironment(stimuliandtheirperceptualdimensions, contextslikeacageoramaze),ofthelawsrulingthisenvironment(whichstimulus isaUSandtriggersanunconditionalresponse;whichstimulusisaCSandhow reliablyitpredictstheUS;andotherphysicallawsoftheenvironmentlikeobjects thatcanbemoved)andoftheprecisetimingofsequenceofevents(defining protocolsimplementingacquisition,extinctionandrenewalprocedures).Initsmost basicform,suchadesignisgenerallythoughtto fitbiologicalexperimentsandwill beusedforthepurposeofcomparisonwithbehavioralperformancesaswellaswith patternsofneuronalpopulationactivationandlearning.Inamoreadvancedform, thissystemcouldbealsoexploitedonthesideofcomputerscience,eitherinterms ofperformanceoflearning,tobeevaluatedinamachinelearningperspective,orin termsofautonomousagent,tobeevaluatedwithregardtotherangeofavailable behaviorsandtothepertinenceoftheirselection.Implementingnotonlypavlovian conditioningbutalsooperantconditioningisastrongprerequisiteforthislatter perspective,astheauthorsarepresentlyconsidering.

7Discussion

Inthispaper,wehavepresentedtheplatformVirtualEnaction,forimplementing brainmodelsandtheirbodilyandexternalenvironment.Beyondthedescriptionof thefunctionalitiesoftheplatform,wehavealsoexplainedwhyitwascorrespondingtoaneedinthebehavioralandcomputationalneurosciencecommunity. Wehaveillustratedtheseelementswithacasestudyrelatedtoanimportantclassof learning,fromabehavioralpointofview.Indeed,letusmentionthatthisplatform hasalreadybeenusedforpreliminarydigitalexperimentsaboutPavlovianconditioning[18]involvingthefunctionalmodelingoftheamygdalaandhippocampus, decisionmakingmechanismsinlinkwithreinforcingsignalsyieldedbyaversiveor

appetitivestimuliandinternalcomputation[19],plusastudentworkoftheAGREL connectionistcategorizationmodelhereconfrontedtoarealisticenvironment[20].

Buildingone “brainy-bot ” isaratherhugetaskandrequiresseveralhigh-level cognitivefunctionalities.However,thoughsystemicneurosciencerequirestostudy thesystemasawhole,itdoesnotimplythateachfunctionalityhastobestudiedat thesamelevelofdetails.Severalblocksmaybeconsideredasblack-boxesinteractingwiththepartofthesystemtobeextensivelystudied.Thisisthereasonwhy thepresentplatformisnotlimitedtoasurvivalenvironment,butcomesalsowith middleware(presentlyindevelopment)relatedtothebasiccognitivefunctionality involvedinsuchparadigms[21].Somemoduleswillthusbeimplemented accordingtoaroughdescription,e.g.,viaanalgorithmicersatz.Thenervous sub-systemunderstudyonthereverse,isgoingtobeimplementedatavery fine scale(neuralnetworkmesoscopicmodelsorevenspikingneuralnetworks).

Thekeyfeaturesofthisdigitalexperimentationplatformincludethecapability toperformexperimentsinvolvingbothlong-termcontinuoustimeparadigmsor short-termdecisiontaskswithafewtime-steps.Italsoallowsustoconsidereither symbolicmotorcommandorsensoryinput(e.g.,ingestornotfood,detectthe presenceofastimulus)orquantitativegesturesandcomplextrajectorygeneration (e.g., findresourcesinanunknownenvironment).Akeypointistobeableto mimicandrepeatatwillexperimentsperformedinneurosciencelaboratoryon animals.Here,theobtainedcomputationalmodelsarenotonlygoingto “fi tthe data” buttobeexploredfarbeyond,yieldingthepossibilitytostudylong-term adaptation,statisticalrobustness,etc.Notonlyoneinstanceofabotcanbechecked, butseveralparallelexperimentscanberuninordertoexploredifferentparameter ranges,orcomparealternativemodels.

Beyondthesebasicfeatures,theinputandoutputcanbeeasilymanipulatedin ordertoenlargetheexperimentalsetup.Uptonow,themainaspectis “inputor outputdegradation”,i.e.addingnoise.Originally,botperceptionandactionare performedwithoutanyaddednoiseorrandommistake.Dependingontheexperimenttobeconducted,itisobvious(i.e.insertingafewlinesofcodebetweenthe platformandthebot),eithertoreducevariablesprecisionrange(e.g.,addnoiseto thepixelicimage)ortorandomlydrawthefactthatagesturemaysucceedorfail (e.g.,introducespuriouscommand).

Astepfurther,asalreadyimplementedasplugins,sinceallenvironmentelementsareavailable(nottothebot,buttotheexperimentsoftware),itwouldbe possibletodesignothercues,ormoregenerallyotherinteractionswiththeenvironment.However,incollaborationwithneuroscienceexperimentalists,wehave carefullyselectedwhatseemsusefultoexplorebiologicalsystemicmodels,and avoidedtoprovideatoogeneraltoolthatdoesanything.

Itwouldalsobeinstructivetobetterunderstandtowhichextentsuchbio-inspired architecturesactuallyrequiredtocontrolabiologicalsystemcouldenhanceartifi cial controlrulescommonlyappliedinroboticsorgameengines.Thisisachallenging issue,beyondthepresentstudy,butaninterestingperspectiveofthepresentwork.

Acknowledgments HugethankstoNicolasRougierforpreciousadvice.

References

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discover that they are arranged like leaves on a stem. After all, as we know, these scales are only modified leaves. The bracts of the pelargonium are leaves modified to protect the young buds, and the scales of the hyacinth are leaves modified to protect and feed the plant within.

For what do you think? At the very center of the hyacinth bulb is a tiny flower cluster wrapped about by half a dozen tiny leaves! These are white and delicate and very, very small. But in the spring they grow and come out of the bulb in the form of green leaves and bright flowers.

THE BEE.

I am a rollicking bumblebee. I sail through the air as it pleases me. I sail by the trees and around the flowers; I love the sun and hate the showers.

I have a taste does credit to me; I never eat bread and such fiddle-dee-dee. For honey and pollen’s the sensible food; They favor digestion and suit the mood.

I sleep in my nest all winter long, But rush fearlessly forth in the March wind’s song, For I’m sure there’s some one waiting for me, Since a hyacinth blue’s in love with this bee!

STORIES ABOUT ALL SORTS OF THINGS

NECTAR GUIDES.

The bee is always in a hurry. She flies from flower to flower as fast as she can.

She sees the flowers far off and comes straight to them, choosing the brightest. She has learned that the bright flowers hold much honey and often have guides to the nectary, so that she does not have to hunt about, but, alighting on a flower, follows the bright guide. Sometimes it is a spot in front of the nectary and sometimes a line leading to it. It leads her at once by the shortest path to the nectar, and since she is in such haste, the nectar guides are her good friends, helping her to save time.

CELLS.

Cells are a matter of importance.

To be sure there are cells and cells, and some are much more important than others.

For instance, there are prison cells, more’s the pity, and anther cells and honeycomb cells and ovary cells and many more like them. All these are small, hollow spaces with walls around them.

But there is another kind of cell, more important than all these others put together, and they are not hollow and do not always have a wall.

Perhaps you are not very much interested in cells, but you had better be in these we are going to talk about, for they have a great deal to do with football games and dancing and going to parties and picnics. In fact, without them there could be no football and no dancing and no parties nor picnics.

All these things depend upon cells. So we may as well begin at once to find out what they are.

These cells that we are going to talk about are alive. They are made of protoplasm. You do not know what protoplasm is? I can tell you it is time you did then, for if it had not been for protoplasm you would not be in the land of the living. The protoplasm made you; so if you are not interested in it, I think you ought to have been a cabbage or a squash or a liriodendron or some other thoughtless vegetable not expected to be interested in protoplasm.

Like a good many other interesting things, protoplasm cannot usually be seen by the naked eye; it is in such small quantities that it takes a microscope to find it. And when you have found it, so far as its looks are concerned, it would hardly seem to pay for the trouble, for to the eye it is nothing but a colorless, jelly-like substance. It

looks more like the white of an egg than anything else. But remember it is not safe to judge protoplasm or people by looks alone.

Napoleon was small, and he was not handsome; yet if you had seen him, you would have seen the greatest man living in the world at that time.

So when you look at protoplasm you see something very much more wonderful than it seems. In fact, the great Napoleon himself owed his physical life to protoplasm, as did also Shakespeare and Plato, and every person who has ever lived, for protoplasm is the only living matter in the world.

You cannot understand that all in a minute, but you begin to see that protoplasm is rather important, and as well worth knowing about as the latest fashion in bicycles or sleeve patterns.

Sometimes a bit of protoplasm lives all by itself. It is just a little speck of colorless, jelly-like substance. Yet it can do a number of things. One little creature, which is only a bit of protoplasm, has a name much larger than itself. We call it “Amœba.”

Rather a pretty name, on the whole, and very uncommon. I doubt if you know a single person by that name.

It is a name, too, that everybody ought to know.

Well, as I told you before, and shall probably tell you a great many more times, for I do not want you to forget it, the amœba is only a bit of protoplasm.

Yet it can go about. You watch it some fine day under your microscope and see it travel. It runs out a little, thin bit of its body, so and then the rest of the body sort of pulls itself up to that. In this way, by putting out little finger-like projections and drawing the rest of the body up to them, it can move quite a distance if you give it time enough. You can imagine so changeable a creature as the amœba can scarcely be found twice of the same shape, and how its friends recognize it is more than I can tell. Suppose you were in the habit of changing your shape whenever you moved, being long and thin one minute, short and thick another, having fourteen arms

one day and none the next? How could you expect people to know you when they met you?

But perhaps the amœba has an unsocial nature and does not care whether it is recognized or not.

Because it changes its shape so often the amœba has received its pretty name. For “amœba,” you must know, comes from a Greek word meaning “change.”

It is sometimes called “Proteus” for the same reason. Of course you know all about Proteus, the sea god who lived at the bottom of the ocean and paid homage to the great god Neptune, who was ruler of the seas. Proteus took care of the sea calves, and he had a queer way of changing his shape whenever he chose. He used to go to sleep on the rocks while the calves were sunning themselves, and because he was very wise and could help people who were in trouble, they used to go there and catch him. But he was not as friendly as he was wise, and would never tell anything unless forced to; and when he found himself a prisoner, he would at once change his form, and so try to escape by frightening his captors. He had a pleasant habit of all at once changing into an enormous serpent and opening a mouth full of frightful teeth; then, if that did not frighten badly enough, he would all at once turn into a bull or a raging fire or a fierce torrent. He has been known to change into a dozen dreadful things in as many minutes, so no wonder his name has come to mean “something that changes.” And no wonder the amœba is called “proteus,” not that it indulges in any such outrageous transformations as the sea god, for it never does anything worse than change the shape of its own little jelly-like body.

Although it can move along, I do not think it would amount to much in a race, as it only moves a few inches in the course of a day; still that is a good deal, considering its size.

A great deal depends upon size in this world.

You could go as far in ten seconds as a snail could in as many hours. The distance would not count for much as far as you are concerned, but it would be a good day’s work for the snail. So when an amœba travels a few inches, that counts for as much in its life as a long day’s walk of a good many miles would in yours, or as a few hundreds of miles on a railway train.

The amœba can do more than travel. If you touch one it will shrink together, showing that this little bit of protoplasm has a sort of feeling power.

When it is hungry it eats. For an amœba can get as hungry as anybody.

Hunger does not depend upon size. You can get as hungry as an elephant, although you cannot eat as much. You would starve to death, too, as soon as an elephant, perhaps sooner. An amœba no doubt gets as hungry as you do, and it certainly would starve to death if it did not have something to eat.

How can it eat without a mouth? Just as easily as it can travel without feet. You do not know protoplasm if you think it cannot eat when it is hungry. Very likely the reason it travels about is because it wants to find something good to eat. It does not care for roast turkey and cranberry sauce, nor for apple pie and plum pudding.

That is not what it is looking for. It is looking for some tiny speck of food smaller than itself.

It lives in the water, of course. It would dry up if it were out in the air. You should think it would melt in the water? Well, it does not, any more than a jellyfish melts. When it comes to some little speck of dead plant or animal, or, for all I know, to some living speck small enough, it proceeds to eat it.

It glides over it in the way you know about, and wraps the food speck up in its body. Then it draws out all the good part of the food into its own substance and goes on, leaving behind the waste particles.

Do you not think that is a good deal for an amœba to be able to do? But it can do more than this; it can divide itself in two and make two amœbæ out of one.

The little amœba is called a “cell.” After awhile you will see why. The whole amœba is just one cell.

As to whether it is a plant or an animal you will have to ask the amœba, for I cannot tell you. Some think it is a plant and some say it is an animal.

I do not think it makes much difference which you say it is.

A bit of protoplasm living by itself is called a “cell.”

Many plants and animals have, like the amœba, only one cell. Very often the little one-celled being has a thick outside wall. The protoplasm changes part of the food into a hard substance, that is, it builds itself a wall.

Very often cells live together in colonies instead of living alone. In such cases, the first cell divides into two cells, but the two stay together instead of entirely separating. Then each of these two cells divides again, and the four cells stay together, and so it goes on until a large body is built up of many cells.

The truth is, plants are only collections of cells which have agreed to work together. Where there is but one cell, it has to do all sorts of work; but where there are many, some do one kind of work, some another,—just as Robinson Crusoe, living all alone on the island of Juan Fernandez, had to do all sorts of things for himself: make his own shoes and clothes, get his own food and cook it, build his own house, and gather his own wood. But in a town one set of men makes shoes, another chops wood, another raises vegetables and grain, another grinds the grain, and another bakes the bread; then they all exchange with each other, and everybody has enough—or ought to have.

So in the plant made of many cells. One set of cells makes hard walls to protect the plant. Another set draws up water from the earth for all the cells in the plant, for living things require a great deal of water. Another set takes gas from the air and changes it into food. Another set makes tubes for the sap to flow through. Other sets do other things. Each set of cells does something for the whole plant.

If you look at a leaf or a bit of skin from a stem under a microscope, you will see they are built up of cells, as a house is built of bricks. Only the cells are not placed regularly like the bricks in a house, and they are not solid like bricks. The walls of these cells are sometimes hard and sometimes soft, sometimes tough and sometimes tender; but the walls were all built by the protoplasm that lived in them. Sometimes the protoplasm leaves the little house it has built and goes somewhere else.

Then the empty, wall-surrounded space is left like a cell of honeycomb before the honey is put in, or an anther cell after the pollen has fallen out and left nothing in it.

Before microscopes were as perfect as they are now, these empty spaces with their surrounding walls were discovered. Even where the cells contained protoplasm the microscope was not strong enough to reveal it, so only the cell walls were seen.

It was soon known that plants were built up of these little compartments, and because they resembled cells in being small and shut in by walls, they were called “cells.” After awhile it was discovered that the living part of the plant was the colorless, jelly-like protoplasm which lived in the cells. Yet later, particles of wall-less protoplasm were found building up plants and animals. What were these soft little protoplasmic atoms to be called?

The plant was really built up by them, and only part of them had walls, so they were called by the name the people had already given to the walled spaces which they supposed built up the plant, and so got the name of “cells,” which is not at all an appropriate name.

There is nothing quite so easy as to be mistaken, you see, and the botanists, having seen that the plant was built of little compartments, and never suspecting the presence of the living protoplasm lurking in some of them, had called the compartments “cells”; later, when the protoplasm was discovered to be the real builder, the old name was kept. So you see how the amœba came to be called a “cell.”

There are a great many different kinds of cells in one plant.

But every living cell has very much the same powers as the amœba, though in many of them some one power is developed at the expense of all

Some of the cells in one plant.

the rest. In this way different sets of cells are able to perform different kinds of work, and do it very well indeed.

The amœba is not the only single-celled creature. There are a great many different kinds of single-celled plants or animals, and some of them take very curious and beautiful forms, with streamers floating about them.

Such are not protean, like the amœba; they do not change their shapes.

Plants are not the only things that have cells. Animals, too, are built up of them. Animal cells are usually softer than plant cells, because they very often have no hard walls. Bone cells of course have hard walls, and there are others, but most of the animal cells are without walls.

So you see all living things are built of cells, and the living part of the cells is the protoplasm.

You yourself are built up of millions of cells, and without the help of protoplasm you would not be living, for protoplasm made your cells, and protoplasm is the only thing in you that is alive. Your muscles are made of muscle cells, and the protoplasm in them moves, and when the muscle cells all move together, that moves your arm or your leg or your head or some other part of your body.

Since your muscle cells devote themselves to moving, they do not try to do much else; so other cells digest the food which the blood carries to the muscle cells. Yet other cells build a good thick skin to protect the soft muscles, and yet another set of cells thinks for the muscles, and tells them where and when and how to move. Each set of cells has its own work.

Your brain is made up of nerve cells, and the protoplasm in them in some way enables you to think and feel. Your bone cells are hard and resisting, your sinew cells strong and flexible. So each part of your body is made up of different kinds of cells.

But what has all this to do with football and parties and picnics you would like to know?

Why, a great deal, to be sure. If it were not for cells and protoplasm there would be no people.

And how could you have football games and picnics without people, I should like to know?

POLLEN CELLS.

In the dark little dungeon cells of the anthers, the pollen grains lie. Hundreds, and sometimes thousands of them, are packed in there as closely as they can be. But they do not mind it, not in the least. They grow and get ripe, and as soon as this happens, their prison door opens and out they pour.

They are funny little things, not at all what they seem to be. For you would think they were just little specks of dust of almost no shape at all. But that is your fault, or rather the fault of your eyes.

You see your eyes were not meant to look at things so tiny as pollen grains. You can see a common ball or even a small shot very well indeed; but when it comes to pollen grains you are as blind as a mole. You will have to put on your spectacles to see that, I can tell you, and very powerful spectacles they will have to be, too. The best spectacles for you to look through are the ones we call a microscope. Just put your eye to that tube and you will see what you will see, for there are pollen grains at the other end—pollen grains from several kinds of flowers; there are some in the corner from our friend the morning-glory. And now you know what I meant when I said you could not see a pollen grain; for those little specks of dust have all at once become large and important objects. Some are round and some

are not, and all are creased or pitted or ridged or covered with little points or marked in some other way. Now you see why they stick so easily to the hairs on the bee or the butterfly or whatever comes visiting the flowers for nectar. They are not smooth, but all roughened over by these ridges and points.

And this is not the end of it. You have not yet seen a pollen grain. You have only seen the outside of one.

For it has an inside. You think it is too small to have anything inside of it?

I can tell you things much smaller than that have something inside of them. The truth is, these things seem so small because we are so large. If we were as small as they, they would not seem small at all. They would seem a very ordinary size indeed, and we would expect them to have an outside and an inside.

The truth is, pollen grains are hollow. They are as hollow as the baby’s rubber ball. But they are not empty. The baby’s rubber ball is not empty; it is full of air. These pollen grains are not full of air. If you were to see what is in them, you might not think it very important, but that would be a great mistake, for they are full of—protoplasm!

The truth of the matter is, the pollen grain is a cell; it has a wall outside and is made of protoplasm inside.

Protoplasm, you remember, is the material out of which every living thing is made. You are made from protoplasm yourself; flowers are made from it, too, and leaves and birds and everything that lives.

So you see if a pollen grain is filled with protoplasm, that is rather a serious matter.

This pollen grain, small as it is, has a tough outer skin. It is not as tough as leather, but it is tough for so small a grain, and is strong enough to keep the protoplasm from running out.

The protoplasm in the pollen grain is what the ovule needs to nourish it and make it able to grow. The ovule, too, is a cell filled with protoplasm, and the protoplasm of the pollen and of the ovule must somehow come together before the ovule can do any more growing.

You know how the bees and butterflies and all sorts of insects carry the pollen from flower to flower and dust the stigmas with it. You may think that when a pollen grain is safely landed on a stigma then the rest is easy enough. But if you suppose the pollen grain can pass through the style you are very much mistaken. It cannot even pass through the stigma. It is true, the tissues of both style and stigma are rather loose, and that the style is sometimes hollow. But, as far as I know, the pollen never passes through. Small as it is, it is too large to get through the tiny openings in the stigma, and then, you know, the stigma is sticky and holds it fast.

Here is an interesting state of affairs! The ovule cell is waiting for protoplasm, and the pollen cell is anchored safe and fast at the stigma.

But you may be sure there is a way out of this difficulty.

To begin, the pollen grain has two coats, a tough outer one and a delicate inner one. There are openings, or at least weak places, in the outer coat, and after the pollen has lodged on the moist stigma, the protoplasm inside swells and comes bulging through these weak places. The inner coat is forced out, as though some extremely small fairy had stuck her finger through the wall from the inside and pushed out a part of the inner lining. Well, this finger-like part that comes through the wall does not break open, but begins to grow. It grows longer and longer until a tube is formed, a tube so small that only the microscope can enable us to see it.

This tube pushes its way through the stigma into the style; there it continues to grow like a long root, only it is not a root, and it is hollow; and the protoplasm from the inside of the pollen grain runs down this tube.

You can guess what happens next. The tube grows and grows; it finds plenty of nourishment in the tissue of the style, which is made of material suitable to feed it. Of course, it grows down the style into the ovary, because the style opens into the ovary.

When it reaches the ovary it finds its way to an ovule, and goes in at a little door which the ovule keeps open for it.

Now, you see, there is an open path between the pollen grain and the ovule, and the protoplasm from the pollen grain which has run down the tube enters the ovule. Here it passes out of the tube by breaking through the delicate wall, and unites with the protoplasm of the ovule.

Thus the ovule is fertilized. It is nourished and strengthened, and at once begins to grow into a seed.

Meantime the shell of the pollen lies on the stigma, a little dried-up, empty thing. Its work is done. Thanks to the bee or the butterfly or some other flower-loving friend, it has been taken to the right place, and all that was living in it, its protoplasm, goes on living in the little ovule.

The pollen grains the bees carry home have a very different fate. They are crushed and soaked and kneaded with honey and fed to baby bees.

But the flowers are willing the bees should have some to live on, and so each flower makes thousands more than it needs. You see, if it did not give the bees something to eat, they would not come and they could not live on honey alone; they, too, need the protoplasm in the pollen to nourish them.

Some kinds of flowers use their own pollen. They do not need the bees and do not want them. So they keep their pollen shut up tightly and do not make any honey to coax the bees to come. But nearly all flowers wish to have other pollen than their own. And this they can

only get by the help of other people’s wings, as they have none of their own.

THE POLLEN.

What does the pollen do?

It helps the ovule change to a seed.

It feeds the bees and the wasps and the flies.

But above all, it helps the ovule change to a seed.

THE ANTHERS.

Anthers, anthers, full of pollen, Cunning cupboards of the bee, Stamen flour amply hiding, What have you for me, for me? What have you for me?

Pollen have I, plenty of it, Pollen for my darling bee;

Pollen every day I blossom

For my bee, but none for thee, For thee, none for thee.

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