Whatdoneuronsreallywant?Theroleofsemanticsincortical1

representations2

3

GabrielKreiman4

Children’sHospital,HarvardMedicalSchool5

Gabriel.kreiman@tch.harvard.edu6

7

Keywords:visualrecognition,deepconvolutionalnetwork,computationalmodels,8

ventralvisualcortex,abstractmeaning,categorization,neurophysiology9

10

NumberofFigures:211

12

Abstract13

Whatvisualinputsbesttriggeractivityforagivenneuronincortexandwhat14

type of semantic information may guide those neuronal responses? We revisit the15

methodologiesusedsofartodesignvisualexperiments,andwhatthosemethodologies16

havetaughtusaboutneuralcodinginvisualcortex.Despiteheroicandseminalwork17

inventralvisualcortex,westilldonotknowwhattypesofvisualfeaturesareoptimal18

for cortical neurons. We briefly review state-of-the-art standard models of visual19

recognitionandarguethatsuchmodelsshouldconstitutethenullhypothesisforany20

measurementthatpurportstoascribesemanticmeaningtoneuronalresponses.While21

it remains unclear when, where, and how abstract semantic information is22

incorporated in visual neurophysiology, there exists clear evidence of top-down23

modulationintheformofattention,task-modulationandexpectations.Suchtop-down24

signals open the doors to some of the most exciting questions today towards25

elucidating how abstract knowledge can be incorporated into our models of visual26

processing.27

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29

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31

InthisChapter,Iaimtohighlightcriticallacunainourunderstandingofthe32

tuningpropertiesofvisualneurons,especiallytheroleofhigh-levelknowledgein33

neuralcodingofvisualinputs.Firstofall,Ishouldclarifytheobvious.Neuronsdo34

not“want”anything.Aneuronemitsanactionpotentialwhenitsintracellular35

voltageexceedsacertainthreshold,typicallybutnotexclusively,intheaxonhillock36

(Koch,1999).Thisvoltageisaweightedsumoftheinfluencesreceivedthroughthe37

neuron’sthousandsofdendriticinputs,whichincludebottom-up,horizontal,and38

top-downconnections.Itremainsexperimentallychallengingtotraceallincoming39

signalstoagivencorticalneuronandtopropagatethosesignalsallthewaybackto40

thesensoryinputs,nottomentionallothernon-sensoryinputs.Thus,inthevast41

majorityofcases,wecorrelatetheactivityofacorticalneuronwiththepresentation42

ofsensorysignals.Itisinthissensethatthequestioninthetitleshouldbe43

understood.Iaskwhatsensoryinputsbesttriggeractivityforagivencorticalneuron44

andwhattypeofsemanticinformationmayguideormodulatethoseneuronal45

responses.46

Istartwithasuccinctdescriptionoftheclassicalviewonwhattypesofvisual47

stimulitriggeractivityinneuronsalongtheventralvisualcortex.Iintroducestate-48

of-the-artstandardcomputationalmodelsofvisionandconsiderthemasabasic49

nullhypothesistoevaluateneuronaltuningpropertiesandpotentialsemantic50

influences,particularlyinthecontextofvisualcategorizationtasks.Next,Iprovidea51

fewexamplesofhowtop-downsignalscanmodulateresponsesalongtheventral52

visualcortexwhileemphasizingthatwehavealongwaytogotounderstandthe53

roleofcommonknowledgeonvisualprocessing.Iconcludebydiscussingcritical54

Hilbertquestioninthefieldandabriefglimpseoftheampleopportunitiesand55

challengesahead.56

57

Assumptionsanddefinitions58

Ifocushereontriggeringactivityinthesenseoffiringratesdefinedasspike59

countsinshortwindowsspanningtensofmilliseconds.Thisisbynomeanstheonly60

oragreeduponrelevantpropertyofcorticalneurons,therehasbeenextensive61

discussionaboutneuralcodes(seeforexample(Kreiman,2004)).Aneuronmight62

contributetorepresentinginformationbyfiringonlyafewspikesataprecisetime63

inconcertwithotherspikesinthenetwork.Aneuronmayalsorepresent64

informationbynotfiring,inthesamewaythatSherlockHolmesintuitedwhothe65

murdererwasbyattendingtothedogthatdidnotbark.Additionally,innon-66

invasivestudies,therearemultipleexperimentaltechniquesthatmeasurenon-67

neuronalsignalsthatarelesswellunderstoodandwhicharedifficulttointerpret68

directlyintermsofneuronalfiringrates.Thegeneralflavorofthediscussionhere69

couldwellbeextendedtootherneuralcodes,butintheinterestofsimplicitywe70

understandthequestioninthetitletoindicatewhattypeofsensoryinputsleadto71

highfiringratesforagivencorticalneuron.72

Afewassumptionsanddisclaimersarepertinentbeforeproceeding.Inorder73

toinvestigatewhatneuronswant,Irestrictthequestiontovisionandsolicit74

inspirationfrombiologicallyplausiblecomputationalmodelsofvision.Thefocuson75

visionismerelyapracticalone:(i)Weknowmoreaboutthearchitectureofthe76

visualsystemthanothersystems;(ii)Wecanstandontheshouldersofgiantsthat77

havepavedthewaythroughmorethanacenturyofbehavioralstudiesofvisionand78

overhalfacenturyofneurophysiologicalscrutinyofvisualcortex;(iii)Wehavean79

arsenaloftoolstosynthesizevisualstimuli,topreciselycontrolthetimingof80

presentation,tomeasureeyemovements,andtocapitalizeonmillionsofdigital81

imagesandvideos.Theemphasisonbiologicallyplausiblecomputationalmodelsof82

visionreflectstheneedtoformalizeourassumptions,andtogenerateacommon83

languagethatcanbeusedtodirectlytestourideasacrosslabsandacross84

experiments.Verbaldescriptionssuchas“neuronsinV2respondpreferentiallyto85

angles”or“neuronsinITrespondpreferentiallytoobjects”arenotsufficientand86

vague,lackpredictivepower,arehardtorejectorvalidate,andoftengetusinto87

trouble.Weneedmathematicalmodelsthatareinstantiatedintocomputercode88

wherewecanuseexactlythesameconditionsandexactlythesameimagesasin89

behavioralorneurophysiologicalexperiments.90

Beyondthepatternofinputspropagatedfromtheretinaontovisualcortex,91

whataneuronwantsislikelytobemodulatedbysemanticpriorknowledgeabout92

theworld,probablyconveyedthroughtop-downconnections.Whatexactlydowe93

meanbysemantics?TheOxford’sEnglishDictionarydefinessemanticsas“…the94

branchoflinguisticsandlogicconcernedwithmeaning.”Howthisdefinitionapplies95

tointerpretingtheresponsesofneuronsalongventralvisualcortexisnotclear.We96

attempttoprovideamorequantitativedefinitionlateroninthischapter.Forthe97

moment,asanexampleofsemanticsinthecontextofvisualrecognition,we98

understandpicturesofgrapes,oranges,orpineapplestorepresentdifferenttypesof99

fruits,eventhoughtheyareratherdifferentintermsoftheirvisualfeatures.100

Similarly,werefertoants,elephantsorgoldfishasanimals.Wewillaskwhatroles101

thisandothertypesofhigh-levelknowledgeabouttheworldplaysinvisual102

processing.103

104

Neuronalresponsesinvisualcortex,theclassicalview105

106

Theintroductionoftechniquestorecordtheactivityofneuronsinthe107

beginningofthelastcenturyledtodecadesofexperimentsinterrogatingneuronal108

responsestovisualstimulation.Thehistoryofstudyingneuronaltuningproperties109

invisualcortexisthehistoryofvisualstimuli.Howdoweinvestigatethefeature110

preferencesofaneuroninvisualcortex?Weneedtodecidewhichstimulitousein111

theexperiments.Thecentralchallengeinansweringthisquestionisthatitis112

essentiallyimpossiblewithcurrent(andforeseeable)technologytoexhaustively113

exploretheentirespaceofimages:thenumberofpossibleimagesisbeyond114

astronomical.Consideringasmallimagepatchof100x100pixels,thereare115

210,000~103,010possiblebinaryimages,~1024,082grayscaleimageswith256shadesof116

grayperpixel,and~1072,2478-bitcolorimages.Asaconsequence,investigators117

havetraditionallyusedseveralastuteandreasonablestrategiestoselectvisual118

stimuliforexperiments:119

(i)Stimulifrompreviousstudies.Pastperformanceisastrongpredictorofcurrent120

performanceforneurons.Stimulithathaveexcitedneuronsinpreviousstudiesare121

oftenagoodinitialguesstodesignexperiments.Forexample,eversincethe122

discoverythatV1neuronsincatsandmonkeysrespondvigorouslytobarsor123

gratingsofspecificorientations(HubelandWiesel,1968),investigatorshaveused124

orientedbarsandgratingstoprobetheresponsesessentiallyineveryspeciesand125

everyvisualarea(Coogan,1993;Chapmanetal.,1996;HegdeandVanEssen,2007;126

GhoseandMaunsell,2008;NiellandStryker,2010;Nassietal.,2014).Avariationof127

thisapproachistostartwitheffectivestimulifrompreviousstudiesandevaluate128

neuronalresponsestomodifiedversionsofthosestimuli(KobatakeandTanaka,129

1994;Tanaka,2003;Leopoldetal.,2006;Tsaoetal.,2006).130

(ii)Naturalstimuli.Itseemsreasonabletoassumethatneuronsrepresent131

behaviorallyrelevantstimuliandthetypesofimagesencounteredintherealworld.132

Thus,multiplestudieshaveprobedneuralresponsestonaturalimagesandmovies133

(OlshausenandField,1996;VinjeandGallant,2000;SimoncelliandOlshausen,134

2001;LesicaandStanley,2004;Okazawaetal.,2015;Isiketal.,2017),andalsoto135

everydayobjects(LogothetisandSheinberg,1996;SheinbergandLogothetis,2001;136

Hungetal.,2005;Liuetal.,2009),includingfaces(Desimoneetal.,1984;Allisonet137

al.,1994;Kanwisheretal.,1997;Tsaoetal.,2006).138

(iii)Semi-serendipitousfindings.HubelandWieselclaimedthattheydiscovered139

orientationtuningwhiletheywerescrutinizingtheactivityofprimaryvisualcortex140

neuronsandobservedtheresponseselicitedwhentheyinsertedaslideinthe141

projector(Hubel,1981).GrossandDesimoneobservedthatneuronsinITCfired142

vigorouslywhenoneoftheinvestigatorspassedinfrontofthemonkey,leadingto143

theinvestigationsaboutneuronsthatrespondtofacestimuli(Gross,1994).Our144

owndescriptionsofso-called“Clinton”or“Anniston”cellsinthehumanmedial145

temporallobewerealsofortuitous(Kreiman,2002;QuianQuirogaetal.,2005).146

Whiletheroleofluckcanbedebated,rigorousanalysesofneuralresponsestonovel147

stimulicanleadtodiscoveringunexpectedfeaturepreferences.148

(iv)Computationalmethods.Despiteenormousprogressindeveloping149

computationalmodelstoexplainandpredictneuralresponsesalongventralvisual150

cortex(RiesenhuberandPoggio,1999;Wuetal.,2006;Connoretal.,2007;Serreet151

al.,2007;DiCarloetal.,2012),therehavebeenfeweffortstousethosemodelsto152

createstimulithatefficientlydriveavisualneuron.Oneoftheseapproachesis153

reversecorrelationwherebyarapidsuccessionofwhitenoisestimuliispresented154

followedbyaveragingtheimagesprecedingspikes(Jonesetal.,1987).This155

approachhasbeensuccessfulinelucidatingthestructureofreceptivefieldsinthe156

retinaand,tosomeextent,inprimaryvisualcortex,butitdoesnotseemtoworkin157

highervisualareas,duetotheaccumulationofnon-linearitiesandalsothecurseof158

dimensionalitydictatedbythereducedsamplingofstimulusspace.Ratherthan159

startingfromnoise,exploitingnaturalstimulusstatisticshasbeenaproductiveway160

ofsynthesizingimagesandpredictingresponsesinareasV1(OlshausenandField,161

1996;OlshausenandField,2004),V2(Freemanetal.,2013),andV4(Okazawaetal.,162

2015).Anelegantalternativeapproachistouseageneticalgorithmwherebythe163

neuronunderstudycanitselfdictatewhichstimuliitprefers.Asuccessful164

implementationofthisideabyConnorandcolleagues(Yamaneetal.,2008)has165

beenusedtoinvestigateselectivityinmacaqueareasV4andITC(Carlsonetal.,166

2011;Hungetal.,2012;VaziriandConnor,2016).167

Usingacombinationofthesestimulusselectionapproaches,seminalstudies168

ledtofoundationaldiscoveriesaboutvisualprocessing,includingcenter-surround169

receptivefields(Kuffler,1953),neuronsinprimaryvisualcortexthataretunedto170

theorientationofabarplacedwithintheirreceptivefields(HubelandWiesel,171

1962),neuronsinareaMTthatdiscriminatemotiondirection(Movshonand172

Newsome,1992),neuronsinareaV4thataresensitivetocolors(Zeki,1983)and173

curvature(Gallantetal.,1993;PasupathyandConnor,2001),selectivitytonatural174

objects(LogothetisandSheinberg,1996;DiCarloetal.,2012)includingfaces175

(Desimoneetal.,1984;Tsaoetal.,2006),amongmanyothers.Despitethese176

extensiveandheroicefforts,westilldonotknowthatanyofthosetuningproperties177

areoptimalforthoseneurons–whereoptimalmeanstriggeringhighfiringrates.It178

isconceivablethattherecouldbeotherstimulithatmightmorestronglydrive179

neuronsinallthoseareas.Mechanisticmodelscanhelpusunderstandhow180

neuronalresponsesariseandthusdesignbetterstimuli.Thelasttwodecadeshave181

seensignificantprogressinthedevelopmentofcomputationalmodelstohelpus182

understandneuraltuningpropertiesinvisualcortex.183

184

Computationalmodelsofventralvisualcortex185

Inspiredbyneuroanatomyandneurophysiology,manyinvestigatorshave186

developedcomputationalmodelsthatcapturethebasicprinciplesthat187

progressivelytransformapixel-likerepresentationofinputsintocomplexfeatures188

thatcanbelinearlydecodedtorecognizeobjects(Fukushima,1980;Olshausenet189

al.,1993;Mel,1997;WallisandRolls,1997;RiesenhuberandPoggio,1999;Deco190

andRolls,2004;Serreetal.,2007;DiCarloetal.,2012).Morerecently,thisfamilyof191

modelshastakenoverthecomputervisioncommunityintheformofdeep192

convolutionalnetworkarchitecturesthatperformquitewellinmanyobjectlabeling193

andobjectdetectiontasks(Krizhevskyetal.,2012;SimonyanandZisserman,2014;194

Heetal.,2015;Heetal.,2018;Serre,2019).195

Whilethereareimportantvariationsacrossdifferentmodels,theyallshare196

basicdesignprinciplesandwegenericallyrefertothemasafamilyofmodels.The197

modelsarehierarchical,typicallyfollowingasequentialpathofoperations,198

mimickingtheapproximatelyhierarchicalnatureofventralvisualcortex(Felleman199

andVanEssen,1991).Themodelsconsistofmultiplelayers,followingadivide-and-200

conquerstrategybreakingtheproblemofobjectrecognitionintomultiplesmaller201

andsimplersteps.Eachofthesestepsischaracterizedbyaseriesofbiologically202

plausiblecanonicalcomputations,typicallyincludingafilterimplementedbyadot203

product,anormalizationstep,andamaxpoolingoperation.Inmostofthesteps,the204

operationsareperformedinaconvolutionalfashion,suchthatthesame205

computationisrepeatedthroughouttheentirevisualfield.Thedotproduct206

operationischaracterizedbysetsofweightsthatarelearntviatraining.Inthe207

computervisionliterature,aprominentwayoftrainingtheseweightsisvia208

supervisedlearningalgorithmsimplementingback-propagation.Throughthe209

sequenceofcomputations,unitsshowtuningforincreasinglymorecomplex210

features,accompaniedbyanincreasingdegreeofinvariancetotransformationsof211

thosefeaturessuchaschangesinposition,scale,etc.212

Thesemodelsperformquitewellinmanyobjectlabelingtasks.Forexample,213

theResNetarchitectureachieveda4%top-5errorrateintheImageNetdataset214

consistingof1,000possiblecategories(Heetal.,2015).Thesemodelsalsoprovidea215

reasonable–yetcertainlyimperfect–firstorderapproximationtocharacterize216

humanandmonkeybehavioralperformanceinrapidobjectcategorizationtasks217

(Russakovskyetal.,2014;Rajalinghametal.,2018).Forexample,arecentstudy218

showedthatdeepconvolutionalnetworkarchitecturesperformedaswellas,andin219

manycasesbetterthan,forensicfacialexaminerexperts,facialreviewersandso-220

calledfacial“super-recognizers”inafaceidentificationtask(Phillipsetal.,2018).221

Furthermore,theactivityofunitsinthesemodelscanbemappedontotheactivityof222

neuronsalongtheventralvisualcortex(Yaminsetal.,2014),evenextrapolating223

acrosscategorieswhenlearningthetransformationfrommodelstoneurons224

(O'Connelletal.,2018).225

Despitethemultiplesuccessesofthisfamilyofmodels,itisclearthatthey226

onlyscratchthesurfaceofwhatweneedtounderstandaboutvisualcortexand227

thereisalargeamountofneuroscienceandbehavioraldatathatcannotquitebe228

accountedbycurrentinstantiationsofthesealgorithms(Markovetal.,2014;229

Kubiliusetal.,2016;Ullmanetal.,2016;Linsleyetal.,2017;Tangetal.,2018;Serre,230

2019).Becausesuchmodelsdonotincorporateaspectsofcommonsensecognitive231

knowledgeabouttheworldotherthanwhatwasusedtolabelimagesfortraining,232

theyconstituteasuitablestandardbenchmarkandnullhypothesistocontrast233

againstforanystudythataimstoinvestigateanytypeofhigh-levelinformation234

encoding(Kreiman,2017).235

Withsomeexceptions,thisfamilyofmodelshasbeenlessconcernedwiththe236

rolesoftop-downinfluencesonventralvisualcortexresponses.Yet,thereis237

extensivedatadocumentinghowtop-downsignalscanmodulateneuronalactivity238

invision.Forexample,spatialattentioncanenhanceneuronalresponsesthroughout239

visualcortex(DesimoneandDuncan,1995;ReynoldsandHeeger,2009).Top-down240

influencesarealsomanifestedintheformofmodulationbytaskdemandsand241

expectations(GilbertandLi,2013). 242

Ofnote,thisfamilyofmodelsdoesnotexplicitlyincorporateanytypeof243

linguisticorsemanticencodingintheirdesign.Themodelsaretypicallytrainedto244

learntoseparateimagesthatwerelabeledasbelongingtodifferentclasses.For245

example,aninvestigatormaylabel1,000imagesaspineapples,andlabelanother246

setof1,000imagesaselephants.Themodelmaybetrainedviasupervisedlearning247

toseparatethosetwogroupsofimagesandthealgorithmscitedabovecandoa248

remarkablejobinlabelingimages,includingextrapolatingtonovelpicturesof249

pineapplesandelephants.Wenextturnourattentiontoaskwhetherthisabilityto250

assigncategorylabelsindicatesanytypeofsemanticrepresentation.251

252

Category-selectiveresponsesdonotimplysemanticencoding253

Inmanytypicalneuroscienceexperiments,investigatorsmaypresentimages254

containingobjectsbelongingtodifferentcategories(Desimoneetal.,1984;255

LogothetisandSheinberg,1996;Sugaseetal.,1999;Vogels,1999;Kreimanetal.,256

2000b;Freedmanetal.,2001;Thomasetal.,2001;SigalaandLogothetis,2002;257

Hungetal.,2005;QuianQuirogaetal.,2005;Tsaoetal.,2006;Kianietal.,2007;258

Meyersetal.,2008;Liuetal.,2009;KourtziandConnor,2011;Mormannetal.,259

2011).Throughoutinferiortemporalcortex,andeveninareasofthemedial260

temporallobeandpre-frontalcortex,investigatorshavereportedselectiveneuronal261

responseswithhigherfiringrateselicitedbysomegroupsofstimulicomparedto262

others.Dothesedifferentialresponsesindicateanytypeofsemanticencoding?263

Tobeclearaboutwhatthisquestionmeans,wereturntothedefinitionof264

semanticsasalinguistrepresentationconcernedwithmeaning.Weunderstandthis265

definitiontoimplythatmeaningindicatesanabstractrepresentation,beyondwhat266

ispurelycapturedbythestimulusfeatures.Asystemoralgorithmthat267

comprehendssemanticinformationshouldbeabletocapturethelinkbetween268

lemonsandpineapples,anditshouldbeabletodiscernthatatennisballis269

functionallyclosertoatennisracquet,eventhoughitlooksmoresimilartoalemon.270

Toinvestigatewhetherdistinctneuronalresponsestodifferentgroupsof271

stimulireflectsemanticencoding,weturntothenullhypothesisforvisual272

representationsoutlinedintheprevioussection,namely,computationalmodelsof273

objectrecognition.ConsiderthemodelarchitectureshowninFigure1A,consisting274

ofaninputimageconveyedtoacascadeof3convolutionallayers(Conv1-Conv3)275

andafullyconnected(fc)layerthatclassifiesinputimagesintooneof6possible276

categories.Thereare6fcunitsthatindicatetheprobabilitythattheimagebelongs277

toeachofthe6categories.Thisisclearlyafarcryfromstate-of-the-artmodelsthat278

includehundredsoflayersandcategorizehundredsofimages.Thedetailsofthe279

architecturearenottoorelevant;otherarchitecturesincludingstate-of-the-art280

computervisionmodelswouldproducesimilarresultstotheonesshownbelow.We281

deliberatelykeepitsimpleforillustrationpurposes,andtoprovidesourcecodethat282

caneasilyberanonanymachine(seelinksattheendoftheChapter).Thismodel283

wastrainedviaback-propagationusingimagesfrom6categoriesintheImageNet284

dataset(Russakovskyetal.,2014):biologicalcells(synsetnumbern00006484),285

Labradordogs(synsetnumbern02099712),fireants(synsetnumbern02221083),286

sportscars(synsetnumbern04285008),roses(synsetnumbern04971313),andice287

(synsetnumbern14915184).Examplesoftheseimagesareshowninthetoppartof288

Figure1B.Themodelwasabletoseparatethestimuluscategories:top-1289

performanceinacross-validatedsetwas78%(wherechanceis16.7%).A2D290

representationoftheactivationstrengthofthe6fcunitsatthetopofthemodelin291

responsetoeachoftheimagesisshowninFigure1B,usingadimensionality292

reductiontechniquecalledtSNEwhichmapsthe6dimensionaloutputvectoronto293

twodimensionsforvisualizationpurposes(vanderMaatenandHinton,2008).The294

colorsrepresentthe6differentcategories,whichclusterintooverlappingyet295

distinctgroups.Forexample,theimagesbelongingtothe“ice”category(pink)296

mostlyclusteredonthebottomleftwhileimagesbelongingtothe“rose”category297

(blue)mostlyclusteredonthetopinFigure1B.298

299

Wefurtherexaminedtheresponsesofeachofthe6fcunitstoallthe~8,000300

images(Figure1C).Forexample,intheleftmostcolumn,eachcirclecorrespondsto301

theactivationoffcunit1inresponsetooneoftheimages.Theverticaldottedlines302

separateimagesfromthe6differentcategories.Asexpected,basedonthewaythe303

modelwastrained,eachofthefcunitsshowedspecializationandrespondedmost304

stronglytooneoftheimagecategories.Forexample,fcunit1showedhigher305

activationonaveragetotheimagesfromthe“cell”category(red)comparedtoall306

theothercategories.Theresponseswerenotall-or-noneandshowedaconsiderable307

degreeofoverlapbetweencategories.Forexample,certainimagesofice(lastsetof308

images)yieldedstrongeractivationforfcunit1thansomeoftheimagesofcells309

(firstsetofimages,comparedthetwocircleshighlightedbythetwoarrowsforfc310

unit1inFigure1C).Thefcunitsarecategoryunitsparexcellence:byconstruction,311

theiractivationdictateshowthemodelwilllabelaparticularimage.Yet,the312

distributionoftheiractivationpatternsshowsconsiderableoverlapacross313

categoricalborders.Eventhoughthemodeldoesadecentjobatseparatingthe6314

imagecategories,themodeldoesnotseemtohaveanynotionofsemantics.A315

zoomedinpictureofapinkcarmaywellbemisclassifiedasarose.Andthediverse316

andstrangepatternsofcellshapescanoftenbemisconstruedtoindicateiceorants.317

Theproblemintermsofsemanticsisnotwiththemodelperformanceitself.Deeper318

modelsandmoreextensivetrainingcanleadtohigherperformance.Toerris319

algorithmic,afterall.Thepointhereisthatthemodelhasnosenseofabstract320

meaning,beyondthesimilarityofshapefeatureswithinacategoryrepresentedbyits321

units.322

323

Wecanstillrefertofcunit5asa“roseunit”forsimplicity.Whatwemeanby324

a“roseunit”isaunitthatismorestrongly--butnotexclusively--activatedby325

imagesthatcontainvisualshapefeaturesthatarecommoninthesetofrosesin326

ImageNet.Theunitdoesnotknowanythingsemanticaboutrosesandcanshow327

highactivationforimagesfromothercategoriesandalsolowactivationforimages328

containingroses,dependingonthevisualshapefeaturespresentintheimage.329

330

Acomparisonthatpervadestheliteratureisthedistinctionbetweenimages331

labeledas“humanfaces”andimageslabeledas“houses”.Wouldthemodelin332

Figure1Abeabletodiscriminatehumanfacesversushouses?Onemightimagine333

thatthemodelshouldnotbeabletodistinguishhumanfacesfromhousesbecause334

themodelwasnevertrainedwithsuchimages.Evenifoneweretotrytoarguethat335

themodelhassomesortofconcrete,asopposedtoabstract,understandingofthe336

meaningofcells,sportscars,roses,etc.,themodelshouldhavenoknowledge337

whatsoeverabouthumanfacesorhouses.Inotherwords,byconstruction,the338

modelhasnosemanticinformationaboutfacesorhouses.Ifthemodelcanstill339

separatefacesfromhouses,thenanysuchseparationcannotbebasedonsemantic340

knowledge.ToevaluatewhetherthemodelinFigure1Acanseparatepicturesof341

facesversushouses,weconsideredtwoadditionalcategoriesofimages:faces342

(synsetnumbern09618957),andhouses(synsetnumbern03545150).We343

extractedtheactivationpatternsofthe6fcunitsofthemodelinresponsetoeachof344

thosehumanfaceandhouseimageswithoutanyre-training(i.e.,themodelwas345

trainedtolabelthe6categoriesinFigure1Bandwemerelymeasuredthe346

activationinresponsetothesetwonewcategories).WeusedanSVMclassifierwith347

alinearkerneltodiscriminatepicturesofhumanfacesversushousesbasedonthe348

activityofthe6fcunits.Inotherwords,weaskedwhethertherepresentationgiven349

bythe“cellunit”,the“Labradorunit,the“fireantunit”,the“sportscarunit”,the350

“roseunit”,andthe“iceunit”wassufficienttoseparateimagesofhumanfacesand351

houses.Theclassifierachievedaperformanceof86%(wherechanceis50%).That352

is,thepatternofactivationofthe6fcunits–whicharespecializedtodiscriminate353

cells,Labradordogs,fireants,sportscars,roses,andice–canwellseparatepictures354

ofhumanfacesfromhouses.A2Drenderingoftheactivationpatternsofthose6fc355

unitsbythehumanfacesandhousesisshowninFigure1D,depictingagainaclear356

butcertainlynotperfectseparationofthetwocategories.357

358

Asystemthathasnosemanticknowledgeaboutfacesorhousescanstill359

separatethetwocategoriesquitewell.Giventheabundantliteratureonstudies360

aboutfacesversushouses,itisworthfurtherscrutinizingthisresult.The361

photographsintheImageNetdatasetaretakenfromthewebandtherearea362

handfulofhumanfacesandhousesincludedinthe6categorieschosenhere.The363

smallnumberofhumanfacesandhousesarenotuniformlydistributedamongthose364

6categoriesandcouldintroduceasmallbias.Yet,removingthosefewhumanfaces365

andhousesdoesnotchangetheresults.Aficionadostotheideathathumanfaces366

constituteaspecialgroupmightarguethattheimagesofLabradordogsdocontain367

animalfacesandthereforethe“Labrador”fcunitmayhelptheclassifierseparate368

facesfromhouses.Toevaluatethispossibility,wecomputedthesignaltonoiseratio369

foreachofthe6fcunitsindiscriminatingfacesversushouses.Thebestfcunitwas370

unitnumber4(theonethatshowedstrongeractivationbyimagesofsportscars),371

closelyfollowedbyunitnumber5(roses).Theworstfcunitwasunitnumber3(fire372

ants),followedbyunitnumber1(cells).Inotherwords,theLabradorfcunitisnot373

theonethatcontributesmosttotheseparationofhumanfacesversushouses.The374

activationpatternoffcunitnumber4(sportscars)inresponsestohumanfacesand375

housesisshowninFigure1E.Thisfcunitshowedaclearseparationofthetwo376

imagecategories,respondingstrongertoimagesofhumanfaces(meanactivation=377

0.47±1.72)comparedtohouses(meanactivation=-1.54±1.18).Aspointedout378

earlierinconnectionwithFigure1C,thedistributionofresponsesforthetwo379

categoriesclearlyoverlapped.380

381

Nowconsideranexperimentwithactualneuronsstudyingtheresponsesto382

imagesoffacesversushouses.Recordingtheactivityofaneuronthatbehavedlike383

fcunit4,inanexperimentsimilartotheoneinFigure1E,aninvestigatormightbe384

temptedtoarguethattheneuronrepresentsthesemanticconceptoffaces.Yetfc385

unit4isclearlymorestronglytunedtoimagesofsportscars(Figure1C,fourth386

subplot):themeanactivationinresponsetosportscarswas4.59±2.27,whichis387

abouttentimeslargerthanthemeanactivationinresponsetohumanfaces(0.47±388

1.72).Thereisnothingparticularlyspecialaboutthisunit;infact,allfcunitsexcept389

unitnumber3(fireants)showedastatisticallysignificantdifferentiationbetween390

imagesofhumanfacesversushouses.Tofurtherdispelanydoubtsthatthe391

Labradorimagesareplayinganyroleinhere,weranaseparatesimulationwhere392

wetrainedthesamearchitectureinFigure1Afromscratchwithonly2fcoutput393

unitstodiscriminateimagesofdesks(synsetnumbern03179701)versusimagesof394

friedrice(synsetnumbern07868340).Thealgorithmachievedanaccuracyof98%395

(chance=50%).Thesetwofcunitscouldbedescribedasa“deskunit”anda“fried396

riceunit”.Thepatternofactivationofthosetwofcunitsinresponsetoimagesof397

humanfacesandhouses(withoutanyretrainingofthenetwork)wasableto398

distinguishthemwith73%accuracy.Thedeskunitshowedanactivationof2.49±399

1.55inresponsetoimagesofhumanfacesandanactivationof0.98±1.11in400

responsetoimagesofhouses,clearlydifferentiatingthetwocategories.Thefried401

riceunitshowedanactivationof-2.32±1.37inresponsetoimagesofhumanfaces402

versusanactivationof-1.13±1.11forimagesofhouses,clearlydifferentiating403

betweenthetwocategories.Insum,measuringhigheractivationforpicturesofone404

categoryversusothers(e.g.sportscarsversusrosesorfacesversushouses),inand405

ofitself,shouldnotbetakentoimplyanytypeofsemanticrepresentation.406

407

OnemaystillwanttomaintainthatthefcunitsinFigure1Aencodesome408

flavorofsemantics.Afterall,athresholdedversionoftheactivityofthoseunitsis409

sufficienttoprovideacategoricalimagelabel.Furthermore,thoseunitsarecapable410

ofacertaindegreeofabstractioninthesensethattheycanlabelnovelimagesthat411

themodelhasneverseenbeforeintothose6categories.Suchaversionofsemantics412

couldperhapsbebestdescribedasconcretevisualshapesemantics,asopposedto413

someabstractversionofsemanticsthattranscendsvisualfeatures.414

415

Whatarethepreferredstimuliforvisualneurons?416

WhatdothosefcunitsinFigure1Aactuallywant?Thatis,whattypesof417

imageswouldtriggerhighactivationinthosefcunits?Weknowalreadyfrom418

Figure1Cthatimagesofcellsleadtohighactivationinfcunit1,imagesof419

Labradorsleadtohighactivationinfcunit2,etc.Therefore,itseemsreasonableto420

arguethatfcunit1“wants”imagesofcells,fcunit2“wants”imagesofLabradors421

andsoon.Onemightevengoontodescribefcunit2asa“Labradorunit”,aswe422

havebeendoing.Butisitpossiblethatthereexistotherimagesthatleadtoeven423

higheractivationofthosefcunits?Toinvestigatethisquestion,weusedtheAlexnet424

model(Krizhevskyetal.,2012),pre-trainedontheImageNetdataset(Russakovsky425

etal.,2014).Weconsideredtwooftheoutputunits(layerlabeledfc8inAlexnet).426

Thesameanalysescanbeperformedforanyotherlayerbutwefocusonthe427

classificationlayerbecausethisisthestagethatwouldpresumablycontainthe428

highestdegreeofcategoricalinformation.Forillustrationpurposes,weshowthe429

activationoffc8unitnumber209(Figure2A)andfc8unitnumber527(Figure2B)430

inresponsetofourcategoriesofstimuli:Labradors,fireants,desksandsportscars.431

Asexpectedbasedonthewaythatthemodelwastrained,the“Labradorunit”(unit432

209)showedlargeractivationforimagescontainingLabradorscomparedtothe433

otherimages(Figure2A).Similarly,the“Deskunit”(unit527)showedlarger434

activationforimagescontainingdeskscomparedtotheotherimages(Figure2B).435

ThisistheequivalentoftheresultspresentedinFigure1C.Next,weusedthe436

“DeepDream”algorithmtogenerateimagesthatleadtohighactivationforthosefc437

units(Mordvintsevetal.,2015).Essentially,theDeepDreamalgorithmusesthe438

networkinreversemode.Insteadofgoingfrompixelstothefeaturerepresentation439

inagivenunitinthenetwork,DeepDreamgoesfromthefeaturerepresentationina440

givenunitbacktopixels,generatingimagesasitsoutput,andoptimizingthose441

imagesineachiterationtoelicitahighactivationinthechosenunit.Using442

DeepDreamtogenerateimagesthatleadtohighactivationforthe“Labradorunit”443

producedtheimageshowninFigure2C.Theactivationstrengthofthe“Labrador444

unit”inresponsetotheimageinFigure2Cyieldedtheactivationstrengthshownby445

thegreenandbluesymbolsinFigure2A,dependingonthesize(thebluesymbol446

correspondstothesameexactsizeasalltheotherimages).TheimageinFigure2C447

thustriggeredhigheractivationthananyofthe1,846photographsofLabradors448

(eventhoughthosephotographswereusedtotrainthenetwork).Theimagein449

Figure2Ccouldwellbedescribedusingwordsbyahumanobserverascontaining450

multiplerenderingsofdistorted,sketchy,blurred,Labrador-likepatches.Similar451

resultsareshownforthe“Deskunit”inFigures2Band2D.Aftersomesquinting,it452

isalsopossibletodiscernsomeresemblancetodesk-likefeaturesinFigure2D,but453

itislessobvious.Insum,whatfcunitswantisanimagerenderingcomplexfeatures,454

featuresthatarenoteasilymappedontoEnglishwords,thoughtheycertainly455

resembleaspectsoftheactualphotographsusedtotrainthealgorithms.Thosefc456

unitsrespondmoststronglytoimagesthatcannotbeobviouslypredictedbythe457

labelsassignedtothem.Whileonemaystillwanttorefertothoseunitsas458

“Labradorunits”and“Deskunits”,itisclearthattherearemanyimagesthatwould459

notbelabeledasLabradorsordesksbyanyhumanobserver,andyettheytrigger460

higheractivationinthoseunits,evenhigherthanreal-worldphotographs461

containingthosecategories.462

Tosummarize,typicalNeuroscienceexperimentsarelimitedbyhowlongit463

ispossibletorecordfromaneuron.Investigatorsmustmakehardchoicesabout464

whichstimulitopresent.Thereisarichandexcitingliteraturewithmany465

experimentsshowingthatneuronalresponsescandiscriminateamongdifferent466

categoriesofstimuli.AsillustratedherebythecomputationalmodelsinFigures1467

and2,thesetypesofresponsesdonotimplyanytypeofsemanticencoding.Simple468

computationalmodelscanalsoyieldresponsesthatdistinguishdifferentcategories469

(Figure1B),thoseresponsesarenotall-or-none(Figure1C),category470

differentiationcanbedemonstratedusingunitsthatareknowntobeclearly471

semanticallyunrelatedtothosecategories(Figures1D-E),andcompleximagesthat472

donotdirectlymapontoanysemanticmeaningcantriggerhigheractivationin473

putativecategoricalunits(Figure2).474

475

Modelsversusrealbrains476

477

Thesedeepconvolutionalbottom-upcomputationalmodelscastadoubton478

claimsaboutsemanticencodingbasedoncategory-selectiveresponsesandprovide479

anullhypothesistocompareagainst.Yet,thesecomputationalmodelsareafarcry480

fromrealbiologicalsystemsinallsortsofwaysandthereforeitisfairtoquestionto481

whatextentwecanextrapolateconclusionsfromthesecomputationalmodelstothe482

typesofrepresentationsmanifestedbyrealneurons.Advocatesofsemanticswould483

rightlyarguethattheexercisesintheprevioussectionmerelyreflecttoymodels484

andthatitremainsunclearwhetherthesameobservationsapplytoactualneuronal485

recordingsfromrealbrains.Theobservationthatthesemodelscanreproduce486

certainaspectsofselectivityinneurophysiologicalrecordingsdoesnotimplythat487

onecanruleoutthepresenceofsemanticinformationinneuraldata.488

489

Althoughdeepconvolutionalmodelsarestillratherprimitiveandfailto490

incorporatemuchofthearchitectureandfunctionofbiologicalcircuits,recent491

studieshaveshownthatthesemodelscanexplainarelativelylargefractionofthe492

varianceinneuronalresponses(Yaminsetal.,2014;Maheswaranathanetal.,2018).493

Infact,category-selectiveresponsesfrombiologicalneuronsalsoshowthetypeof494

propertiesillustratedinFigure1(e.g.,(Vogels;Kreimanetal.;Sigalaand495

Logothetis;Hungetal.))andthereforethesamecautionarynotesshouldbeusedin496

interpretingneuronalselectivity.Furthermore,recentworkhasshownthatitis497

possibletogenerateeffectivestimuliforbiologicalneuronsinafashionsimilarto498

theprocedureillustratedinFigure2(Ponceetal.,2019).Theauthorsuseda499

proceduresimilartotheDeepDreamalgorithmdiscussedearliertogenerateimages500

whilerecordingneuronalresponsesusedtoguidetheevolutionofimagestriggering501

highfiringrates.Theresultingsetofsyntheticimagestriggeredactivationin502

biologicalneuronsthatwasasstrongasorinmanycasesevenstrongerthannatural503

stimuli,similartothesyntheticimagescreatedinFigure2.Inotherwords,for504

biologicalneuronsalongtheventralvisualcortex,thetypeofstimulithattrigger505

strongestactivationarenotrealworldobjectswithsemanticmeanings,butrather506

complexshapeswithfeaturessharedwithrealworldobjectsbutdistinctand507

abstractandwithoutanyobvioussemanticmeaning(Ponceetal.,2019).508

509

Absenceofcompellingevidenceforsemanticencodingdoesnotconstitute510

evidenceofabsenceofsemantics.Thefactthatwecannotconcludethatthereis511

abstractsemanticinformationbyobservingtheresponsestoagivencategory512

versusothersinthistypeofexperimentscertainlyshouldnotbeinterpretedto513

implythatsemanticinformationdoesnotexist.Thepointintheprevioussectionis514

thatmerelyshowingdifferentialpatternsofactivitybetweentwo(ormore)515

categoriesofstimuliismoreofareflectionaboutthechoiceofstimuliandaboutthe516

waytheimagesweregatheredratherthananymysteriousnotionofabstract517

meaning.Thefamilyofdeepconvolutionalnetworkmodelsshouldbeusedasanull518

hypothesisforanystatementconcerningtherepresentationofabstractmeaningin519

experimentsonvisualimages.Wecanthusdefineabstractsemanticencodingas520

visualdiscriminationsthatcannotbeaccountedforbythefamilyofnull521

computationalmodelsofvisualrecognition.522

523

Ratherthandiscussingpresenceorabsenceofsemanticinformationina524

binaryfashion,itisprobablymoreusefultoconsiderdifferentlevelsofabstraction525

andinvariance.Atthebottomlevelisthenotionoftemplatematching,i.e.aneuron526

thatrespondswhenaspecificcombinationofpixelsisshownwithinitsreceptive527

field.Increasingthedegreeofinvariance,wecanconsideraneuronthatresponds528

withapproximatelythesameintensitywhenthestimulusshowssmallchangessuch529

asacomplexcellinprimaryvisualcortexanditsresponsestoanoptimallyoriented530

baratdifferentpositionswithinthereceptivefield.Increasingthedegreeof531

abstraction,wecanconsiderneuronsininferiortemporalcortexthatshow532

tolerancetosomeamountof2Drotationoftheirpreferredstimuliandneuronsthat533

respondtovisuallysimilarexemplarsfromagivencategorysuchastheones534

modeledintheprevioussection.Asignificantstepupwardsininvariancewouldbe535

tofindneuronsthatshowasimilarresponsetoatennisball,atennisracquet,a536

tenniscourt,atennisskirtandthewordWimbledon.Tothebestofmyknowledge,537

thereisnoevidenceyetforsucharepresentation.538

539

Insearchofabstractioninthebrain540

541

Whattypeofexperimentaldatawouldprovideevidenceinfavorofabstract542

semanticinformation?Returningtotheexamplesusedinthedefinitionof543

semantics,itwouldbenicetoshowneuronalresponsesthataresimilarforatennis544

ballandatennisracketandyetverydifferentbetweenatennisballandalemon.In545

otherwords,itwouldbenicetoshow(i)imagesthathaveasimilarvisual546

appearance(e.g.,atennisballandalemon)andyettheytriggerverydifferent547

responses,and(ii)imagesthatarevisuallydissimilar(e.g.,atennisballandatennis548

racket)andyettheytriggerverysimilarresponses.549

Anelegantstepinthisdirectionwascarriedoutbygeneratingmorphs550

betweensyntheticimagesofcatsanddogsandtrainingmonkeystobehaviorally551

separatethem(Freedmanetal.,2001).Theauthorscouldtitratethevisual552

similarityofthestimuliandseparatepurelyvisualshapefeaturesfromthetask-553

relevantcategoricaldifferentiationbetweenthem.Theauthorsdescribedthe554

activityofneuronsinpre-frontalcortexthatcorrelatedwiththecategorical555

distinctionsratherthanthevisualappearancedistinctionsbetweenstimuli.While556

pre-frontalcortexneuronsbetterreflectedsuchtask-dependentabstract557

information,neuronsininferiortemporalcortexalsoshowedevidenceforencoding558

thecategoricalboundaries(Meyersetal.,2008).Furthermore,monkeyscouldbe559

retrainedtochangetheirdefinitionofthecategoricalboundariesandpre-frontal560

cortexneuronsalteredtheirtuningtoreflectthenewcategoricaldefinitions561

imposedbythetaskdemands(Cromeretal.,2010).562

Anothersetofintriguingresultscomesfromneuralrecordingsinhuman563

epilepsypatients.Somepatientssufferingfrompharmacologicallyintractable564

epilepsyareimplantedwithelectrodesaspartoftheclinicalprocedureforpotential565

surgicalresectionoftheseizurefocus.Thisclinicalsituationprovidesarather566

uniqueopportunitytorecordthespikingactivityofneuronsinthehumanbrain,567

particularlyinareasofthemedialtemporallobeincludingthehippocampus,568

entorhinalcortex,parahippocampalgyrusandtheamygdala(Engeletal.,2005;569

Kreiman,2007;MukamelandFried,2012;Friedetal.,2014).Thislineofresearch570

hasgeneratedobservationsleadingtoclaimsaboutcategoricalinvariance(Kreiman571

etal.,2000b;Mormannetal.,2011).Therehavealsobeenresponsestospecific572

individualsortospecificlandmarks(QuianQuirogaetal.,2005).Thesestudiesare573

subjecttothesametypeofcaveatshighlightedintheprevioussection.However,in574

severalofthosecases,theinvariantresponsesweretriggeredbysetsofimagesthat575

wereverydifferentfromeachotherbasedonvisualinspection(therewasno576

quantitativedocumentationofvisualshapesimilaritybasedoncomputational577

models).Thesubjectivevisualdissimilarityofthosestimulisuggeststhatitwould578

bedifficulttoaccountforthoseresponsespurelybasedonthetypeofvisual579

similaritydescribedbythenullfamilyofstandardmodels.Particularlystrikingare580

thecaseswheretheneuronsrespondedtotheimageofaparticularindividualas581

wellastextversionoftheirname(QuianQuirogaetal.,2005),andcaseswherethe582

neuronsrespondedinaselectivefashionduringvisualimageryintheabsenceof583

anyvisualinput(Kreimanetal.,2000a;Gelbard-Sagivetal.,2008).Theactivityof584

humanmedialtemporallobeneuronstakenasawholethereforereflectsahigh585

degreeofabstraction.Interestingly,theseresponsestendtooccurratherlateinthe586

game,arisingsomewherebetween200and300millisecondsafterstimulusonset587

dependingonthespecificarea,whichisattheveryleast50-150millisecondsafter588

theselectivevisualresponsesdescribedinbothmonkey(Eskandaretal.,1992;589

Keysersetal.,2001;Hungetal.,2005)andhuman(Liuetal.,2009)inferior590

temporalcortex.Additionally,bothhumans(Thorpeetal.,1996)andmonkeys591

(Fabre-Thorpeetal.,1998)canbehaviorallycategorizeimageswellbeforetheonset592

ofthoseresponses.Thus,thesemedialtemporalloberesponsesaremorelikelyto593

reflecttheencodingofemotionalinformationandtheformationofepisodic594

memories(bothofwhicharelikelytodependonsemanticencodingofinformation),595

ratherthanthevisualcategorizationperse.596

Accordingtothebroaddefinitionofsemanticsasaspectsofneuronal597

responsesthatcannotbeaccountedbythenullstandardmodelsofvisual598

recognition,multiplestudieshaveshowntaskdependentmodulationof599

neurophysiologicalresponsesthroughoutvisualcortex.Forexample,inanelegant600

study,neuronsinprimarycortexrespondeddifferentlytothesamestimuluswithin601

theirreceptivefielddependingonwhethertheinformationwasrelevantornotfor602

thecurrenttask(Lietal.,2004).Task-dependentexpectationscanalsomodulate603

responsesallthewaydowntoV1neurons(GilbertandLi,2013).Satisfyingsuch604

taskdemandscanbeconsideredanimportantaspectofabstractioninthesenseof605

consideringtheincominginputsinthecontextofcurrentgoals.606

607

Semanticsandtheleastcommonsense608

Commonsense,orgeneralsemanticknowledgeabouttheworld,ishardto609

find.Thedefinitionofsemanticsincludinglinguistic-likeinformation,atleasttaken610

literally,suggeststhatweshouldbelookingforahighlevelofabstraction,beyond611

whatcanbedescribedbycurrentvisualobjectrecognitionmodels.Onepractical612

issuetotacklesemanticsisthatitisdifficulttostudylanguageinnon-human613

animals.Strangely,thereareeveninvestigatorsthathaveclaimedthatlanguageis614

uniquetohumans(BerwickandChomsky,2015).Additionally,thereisminimaldata615

onsingleneuronresponsesinlanguageareasinthehumanbrain.Onemayimagine616

thatanylinguisticinformationfrommedialtemporallobestructures,fromtask-617

dependentrepresentationsinpre-frontalcortex,orfromlanguageareas,618

mayverywellpropagatebacktoventralvisualcorticalareasanditmightbe619

possibletodiscernthosetop-downsemanticinfluencesinvisualcortex.620

Inthespiritofstimulatingfurtherdiscussionsandfuturework,weconclude621

withabriefdesiderataofexperimentsandmodelstofurtherourunderstandingof622

whatvisualcorticalneuronsreallywantandtheroleofsemanticinformation.623

[1]Computationalmodelsshouldplayanintegralpartinthedesignofvisual624

experimentstoelucidatewhatneuronswant.Thefamilyofdeepconvolutional625

networkmodelsprovidesareasonablenullhypothesistostartwith.Themodelscan626

beusedtoquantifywhatfractionofneuronalresponsevariabilitycanbeexplained,627

butalsotogenerateimagesanddesigntheexperimentsthemselves.Asanexample628

ofthisapproach,Figure2illustratesawayinwhichamodelcangenerateimages629

thattriggerhighactivationinitsunitsanditwillbeinterestingtofurtherevaluate630

thislineofreasoninginneuronalrecordings.631

[2]Giventhelimitedamountofdatathatwecanacquireforagivenneurondespite632

laboriousandheroicexperiments,weshouldbeopentotheideathatwehaveyetto633

uncoverwhatneuronstrulywant.Ourunderstandinganddescriptionofthetuning634

propertiesofneuronsalongventralvisualcortexmayhavetobesignificantly635

revisited.Twoimportantrecentdevelopmentsmayaccelerateprogress:theadvent636

ofsophisticatedcomputationalmodelsthatcanprovidequantitativehypothesisfor637

testingbeyondclassicalexperimentaldesigns,andtheexperimentalpossibilityof638

holdingneuronalrecordingsforprolongedperiodsoftime(McMahonetal.,2014).639

[3]Taskdemandsseemtoplayacriticalroleindynamicallyshapingneuronal640

responsesbeyondthedimensionsthatarepurelydictatedbysensoryinputs.As641

oneexampleofarecentsurprisingfindinginthisdirection,neuronsinrodent642

primaryvisualcortexarestronglymodulatednotonlybythevisualinputsbutalso643

bythespeedatwhichtheanimalismoving(NiellandStryker,2010).Thereare644

plentyofopportunitiestofurtherinvestigatehowtop-downmodulationcan645

dynamicallyrouteinformationaccordingtothecurrentbehavioralgoals.646

[4]Touncoversemanticencoding,wewouldliketoensurethattheneuronal647

responsescannotbeexplainedbythenullfamilyofmodels.Aneuronencoding648

semanticinformationshouldshowasimilarresponsetoimagesthatsharemeaning649

butwhichhavenosimilarityintheirappearance.Additionally,suchaneuronshould650

showadifferentresponsetoimagesthatarevisuallysimilarbutdonotsharethe651

samemeaning.652

[5]Anotherimportantquestionforfutureresearchistoelucidatetheneuronal653

mechanismsofhowabstractioncanbelearnt.Therehasbeenextensivework654

showingthatvisualcorticalneuronscanchangetheirresponsesasaconsequenceof655

associationsformedbydifferentstimuli(Miyashita,1988;HiguchiandMiyashita,656

1996;Messingeretal.,2001;Suzuki,2007).Extendingsuchmechanismsmightlead657

totheformationofsemanticlinkssuchasthoseestablishedbythestatisticalco-658

occurrencesoftennisballs,racquets,courts,andskirts.659

660

Dataavailability661

AllthecodeusedtogenerateFigures1and2,isavailablefordownloadfrom:662

http://klab.tch.harvard.edu/resources/Categorization_Semantics.html663

WecannotprovidetheimagesusedintheexperimentsinFigures1and2.However,664

weprovidethesynsetidentificationnumbers,whichcanbeusedtofreelydownload665

alltheimagesfromthefollowingsite:666

http://image-net.org/667

668

669

670

FiguresCaptions671

672Figure1673

A.Simplemulti-layerconvolutionalnetworkconsistingofaninputlayer,3674convolutionallayersandafullyconnectedclassificationlayerthatclassifiesimages675intooneof6possiblecategories:cells,Labradors,fireants,sportscars,rosesandice676(exampleimagesfromthosecategoriesareshowninpartB).Thenetworkwas677trainedviabackpropagationtooptimizeclassificationofimagesbelongingtothose678

6categories.B.Dimensionalityreductionusingstochasticembedding(vander679MaatenandHinton,2008)oftheactivationpatternforthe6fclayerunitsfrompart680Ainresponsetoeachoftheimages.Thecolorofeachdotreflectstheimage681category.C.Activationstrengthforeachofthe6fcunitsinresponsetoallthe682images.Theimagecategoriesareseparatedbyverticaldottedlines.Theimages683fromthecategoryelicitingthestrongestactivationforeachofthefcunitsisshown684incolor,withthecolorsmatchingtheonesinpartB(e.g.,fcunit1showedstronger685activationtoimagescorrespondingtothecellcategory).D.Dimensionality686reductionusingstochasticembeddingoftheactivationpatternforthe6fclayer687unitsfrompartAinresponsetoimagesoffaces(red)orhouses(blue).Thenetwork688wasnottrainedtorecognizeeitherfacesorhouses.Yet,asupportvectormachine689classifierwithalinearkernelcouldseparatethetwocategories(emptycircles690representwronglyclassifiedimagesandfilledcirclesrepresentcorrectlyclassified691images).E.Activationpatternoffcunitnumber4(theoneshowingstrongest692responsestosportscars)inresponsetoalltheimagescontainingfaces(red)or693houses(blue).Thehorizontaldashedlineindicatestheaverageresponses.Allthe694parametersandsourcecodetogeneratetheseimagesareavailablein695http://klab.tch.harvard.edu/resources/Categorization_Semantics.html696697

698

699Figure2700

A.Activationofunitcorrespondingtochannel209inlayerfc8inAlexnet701(Krizhevskyetal.,2012)inresponseto1846imagesofLabradordogs(redcircles),702972imagesofants,1366imagesofdesks,and1165imagesofsportscars(black703circles).Theverticaldottedlinesseparatethedifferentimagecategories.This704neuralnetworkwastrainedviabackpropagationusing1000imagecategories,705includingthe4categoriesshownhere.Thechannelshownherecorrespondstothe706classificationunitforthelabel“Labradordog”;asexpected,activationforthose707imageswasgenerallylargerthanactivationforotherimages.B.SameasAforunit708correspondingtochannel527(Desk).C.ImagegeneratedusingDeepDreamfor709Alexnetchannel209inlayerfc8(Mordvintsevetal.,2015).D.Imagegenerated710usingDeepDreamforAlexnetchannel527inlayerfc8.TheimagesinC-Dledtothe711activationdenotedbythegreentrianglesinA-B.UponresizingtheimagesinC-Dto712bethesamesizeasalltheotherimagesinpartsA-B,thecorrespondingactivations713aretheonesshownbythebluesquaresinA-B.Alltheparametersandsourcecode714togeneratetheseimagesareavailablein715http://klab.tch.harvard.edu/resources/Categorization_Semantics.html716 717

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