Preparing and selecting actions with neural populations: toward cortical circuit mechanisms

Preparing

and

selecting

actions

with

neural

populations:

toward

cortical

circuit

mechanisms

Masayoshi

Murakami

and

Zachary

F

Mainen

Howthebrainselectsoneactionamongmultiplealternativesis

acentralquestionofneuroscience.Aninfluentialmodelisthat

actionpreparationandselectionarisefromsubthreshold

activationoftheveryneuronsencodingtheaction.Recent

work,however,showsamuchgreaterdiversityof

decision-relatedandaction-relatedsignalscoexistingwithothersignals

inpopulationsofmotorandparietalcorticalneurons.We

discusshowsuchdistributedsignalsmightbedecodedby

biologicallyplausiblemechanisms.Wealsodiscusshow

neuronswithincorticalcircuitsmightinteractwitheachother

duringactionselectionandpreparationandhowrecurrent

networkmodelscanhelptorevealdynamicalprinciples

underlyingcorticalcomputation.

Addresses

ChampalimaudNeuroscienceProgramme,ChampalimaudCentrefor

theUnknown,Lisbon,Portugal

Correspondingauthor:Mainen,ZacharyF

(zmainen@neuro.fchampalimaud.org)

CurrentOpinioninNeurobiology2015,33:40–46

ThisreviewcomesfromathemedissueonMotorcircuitsandaction

EditedbyOleKiehnandMarkChurchland

ForacompleteoverviewseetheIssueandtheEditorial

Availableonline3rdFebruary2015

http://dx.doi.org/10.1016/j.conb.2015.01.005

0959-4388/#2015TheAuthors.PublishedbyElsevierLtd.Thisisan

openaccessarticleundertheCCBY-NC-NDlicense(

http://creative-commons.org/licenses/by-nc-nd/4.0/).

Introduction

Howthebraindecidestoselectanactionamongmultiple alternativesisacentralquestionofneuroscience.Asfaras adecisionentailstheselectionofanaction,decisionsare tightlylinkedtoactionpreparation.Evidencefavorsthe ideathattheseprocessesoccurinparallelandmightshare neuralsubstrates:multiplepotentialactionsare simulta-neously‘prepared’inmotorrelatedareaswhileanaction isbeingselected.Selectionofactionsmaybeperformed throughcompetitionamongneuronspreparingfor avail-ableactions[1–3].Thus,thestudyoftheaction prepara-tion process will help to advance ourunderstanding of decision-makingandviceversa.

The field has made much progress in understanding singleneuronresponsepropertiesindifferentbehavioral paradigms in different brain areas. But we still lack an understandingofhowpopulationsofneuronswithinand

acrosscircuitsact togetherto performthecomputations requiredforadecision.Recentstudieshavehighlighted thedistributedandmixednatureoftherepresentationof decision-relatedandaction-related signalswithinneural populations.Thisprompts thequestionof how interac-tionsbetweentheseneuronscanperformcircuit compu-tationsandhowthedistributedsignalscanbedecodedin downstreamareas.Here,wereviewrecentstudies addres-singthesequestions and othersteps towardelucidating thecomputationsandcircuitdynamicsunderlying deci-sion-making.

Although the processes of decision-making and action preparationarethoughttotakeplaceatmultiplecortical and subcortical levels, including the basal ganglia, and superiorcolliculus[2,4],herewefocusmainlyoncortical areas, namelyprimarymotor and highermotor cortices, includingthefrontaleyefieldsin theprimateprefrontal cortex,and parietalassociation cortex.

Classical

view

of

action

preparation

and

decision-making

Oneoldandinfluentialhypothesisfortheneural mech-anism of action preparation is that it simply involves moderate pre-activation of the same neurons that are responsible for execution of the action being consid-ered or prepared. This view is consistent with the observationthat thelevel ofactivationduring prepara-tion is lower than required for actually executing the actionsandispositivelycorrelatedwithhowquicklyan animal responds to a cue to initiate the action [5,6]. Extendingthishypothesis,actionselectionor decision-making is performed through ‘competition’ between neurons responsibleforpreparingtheavailable actions [1]. Factors influencing a choice, such as sensory evi-denceinthecaseofperceptualdecisionorthevalueof options in the case of value-based decision, modulate the subthreshold activity and thereby bias the final choice.Theactionassociatedwithmostactiveneurons at the time of reporting a choice will be the selected action.

Notably,inataskwhereasubjectisfreetochoosewhen ready,‘pre-activation’signalscanindicatenotonlywhat actionto bechosenbut also when. Aneuronpreferring theeventuallychosenactionappearstograduallyincrease itsactivity atsubthreshold levelduring decision period andtheactivityreachesaconstantlevelofactivationjust before the action [2]. Such ‘ramp-to-threshold’ activity hasbeenseeninseveralspeciesandcontexts,including

timingbehavior[7],proactive movementin responseto sensorystimuli[8],andgivingupwaitinginawaitingtask [9].

Insomesense,thelevelofactivityoframp-to-threshold neuronsdynamicallyreflectshow‘close’theanimalisto executing the action. But in a task where the sensory accumulationandmovementpreparationwere dissociat-ed, theactivity of many neuronsin lateral intraparietal area (LIP)reflectedsensoryaccumulationmore so than motorpreparation[10].Theinterpretationofwhat exact-lytheseneuronsencode(e.g.,movement intention/prep-aration,sensoryaccumulationorsaliencyofthestimulus) isstillamatterofdebate[11,12].Ratherthanfocusingon thequestionof‘whatisrepresented’byaparticulararea or setof neurons,it maybemore productiveto address thisissuebyunderstandingthecausalroleoftheactivity oftheseneurons:howtheactivityisdecodedorreadout bythedownstreamareasandeventuallyusedfor behav-ior.

Multiplexed

representation

of

decision-related

and

action-related

signals

More recently, theview that decision-related or move-mentpreparatoryactivitysimplyconsistsofa subthresh-old, scaled-down, version of action-related activity has beenquestioned.Thatsuchactivitypatternsmightbea special case of a much broader class of single neuron correlatesofdecision-makingstartedtobecomeevident onceresearchersbegantosampleneuronsinarelatively unbiased manner, with fewer constraints in selecting neurons.Inthecontextofactionpreparation,Churchland etal.[13]observedastrikingmismatchbetweenpreferred movementdirectiontuningduringthepreparationversus themovementphaseswithinindividualneuronsin mon-keyprimarymotorandpremotorcortex.Similarly,inthe context of perceptual decision-making, several groups reported that signals correlated with sensory evidence and predictive of choicein theposterior parietalcortex and frontal eye field are not as simple as might be imagined. First, they occur not only in the form of classicallyobservedrampingactivitybutalsoinavariety ofdifferenttemporalpatterns[14,15,16,17]. Further-more,decisionsignalscanbemixedwith decision-unre-lated signals withinindividual neurons [14,17].Thus, reading out decision-related signals requires de-mixing themfromothersignals[18,19,20].Thesefindingscall for taking into account a broader range of neuronal responses and askingmore complicated but interesting questions: how do decision-related neurons exhibiting differentdynamics,rampingandotherwise,interactwith eachother,andhow arethesesignalsreadoutin down-stream areasto causeanaction?

Reading

out

population

neural

activity

To understand how the variety of responses in cortical populationscontributetobehavior,weneedtounderstand

themechanismofdecodingpopulationactivity.Inturn,to gaininsightsonthedecodingmechanisms,itisimportant tounderstandhowthesignalsaredistributedacross popu-lationsofcorticalneurons.

Whensignalsaredistributedacrossneurons,itislikelyto be useful to look at population activity from a multi-dimensionalperspective[19,21].Thepopulationactivity of any numberof neurons atagiven timepoint canbe represented as a single point in a multi-dimensional space, and the population activity pattern over time as atrajectoryinthisspace.Giventhattheneural trajecto-riesnormallydonotspantheentirespace,dimensionality reductioncanbeusedtoextractimportantfeaturesofthe

data[19,20,22].Inotherwords,populationactivitycanbe

projected onto certain axes, which may be more inter-pretable andmeaningful.Importantly,suchaprojection alsocorrespondstotakingaweightedsumofthe popula-tion of neurons’ activity, an operation that might be consideredas anelementarykindof neuralreadout.

Amulti-dimensionalapproachhasproventobeusefulin the study of decision-making and action preparation

[15,23–25,26,27]. Applying a multi-dimensional

ap-proach to a study of action preparation, Kaufman et al. [26] proposed a simple population decoder of motor cortex activity that produces a readout resembling the activityofdownstreamareas(e.g.,muscleactivity)during the movement period. Interestingly, when observed through thesame decoder, motor cortex output is sup-pressed during the preparatory period (i.e., when the action iswithheld),eventhoughindividualneurons can be as active as during movement period. This happens becausepopulationtrajectoryduringthepreparatory pe-riod lies in a subspace orthogonal to the movement subspace.Thisconstitutesanovelmechanismbywhich movementiswithheldduring preparatoryperiod,which doesnotrelyonnon-linearthresholdingmechanisms.

Inadecision-makingtask,populationactivitycanalsobe collapsedtoarelativelyfewdimensionsthatcarry infor-mation about future choice.Mante et al. [15] applied multi-dimensionalanalysistorecordingsfromthefrontal eyefieldofmonkeysperformingataskinwhichdecisions aremadebasedoneitherthecolororthemotiondirection of avisualstimulus,dependingonthecontextcue pre-sentedatthebeginningofeachtrial.Althoughindividual neurons could display complex mixed selectivity, they foundasingleeffectivechoiceaxisthatwascommonto both color and motion contexts.This suggests that the same readout mechanism canbe used in two different contextsinordertoappropriatelychooseactions accord-ingto thesensory evidence.

Intasksinwhichasubjectcanfreelychoosethetimingof actions(e.g.,areactiontimetaskorawaitingtask), ramp-to-thresholdactivityhasbeenrepeatedlyfoundinmultiple

brainareas (Figure1a). However,thisnormally involves selectioncriteria thatmightexcludeneuronsthatdonot exhibitthe classicalramp-to-threshold activitybut could becrucially involvedin the decision process.If a multi-dimensional population approach is applied to datasets obtainedin thesetasks,athresholdfordecision commit-mentcanbedefinedasa‘thresholdhyperplane’in popu-lation activity (Figure 1b). By analyzing the activity of individualneuronsthatstronglycontributetothis thresh-oldhyperplane,wemightbeabletobetterunderstandthe contribution of each neuron to the decision and how downstreamcircuitscanreadoutthese signals.

Parket al. [18]approached theproblemof population representationandreadout ofpopulationactivitywitha different technique. Using a generalized linear model-basedstatisticalapproach,theyproposedamechanismto decode choice from population of LIP activity, which relied on biologically plausible leaky integrators and competitionmechanismsandcouldapproximatethe sta-tisticallyoptimal decodingmodel.

Thesestudieshaveadvancedourunderstandingof how decision-relatedandaction-relatedsignalsaredistributed across populationsof neuronsand how downstream cir-cuitsmightinprincipledecodethem.Toexamine read-out mechanisms directly, a critical step will be to determinewhattypesofsignalsareactuallytransmitted towhichdownstreamareas.Forexample,inmanystudies of decision-making, it is proposed or implied that the decision-related signal predictive of a future action is

enriched in the signal transmitted out of the cortical decision circuit (e.g., from layer 5 neurons). Although experimentally challenging, attempts to record activity transmitted through aspecific projection pathway have beenmadepreviously,forexampleforpathwaysfromthe frontaleye fieldsto thesuperior colliculus[28,29].But, thesetwostudieswerecontroversialintermsofwhether thereis[29]orisnot[28]aselectiveenrichmentofcertain typesofsignalssentfromthecortextosuperiorcolliculus. With recently developed genetic approaches to record and manipulate activity in specific pathways, we can anticipate that data relevant to this point will become morereadilyavailable[30,31,32,33,34].

Computation

within

a

cortical

circuit

Understanding the computations performed through interactionsamongstcorticalneuronsisoneof themost challenging problems of neuroscience. Cortical circuits consistofdifferentneuronalcelltypes(e.g., glutamater-gic versus GABAergic, parvalbumin-positive versus somatostatin-positive versus vasoactive intestinal poly-peptide-positive) distributed across several distinct layers. Eachneuron’s inputand output connection pat-ternsaresystematicallyorganizedaccordingtocell iden-tity determined by, for example, cell types and layer

[35,36].

Aninfluentialideaforthecircuitarchitectureunderlying perceptualdecision-makingisthatsensorysignalsarefed intointegrationcircuitswhoseactivitycrossinga thresh-oldlevelservesascommitmenttoadecision[37].More

Figure1 0 10 0 −10 0 10 PC1 PC2 PC3 −10 10 −1 0 2 0 40 80

Time from start waiting (s)

Firing rate (Hz) 0.4 1 2 Waiting time (s) Waiting time (s) (a) (b) 0.4 1 1.6

Current Opinion in Neurobiology

Levelsofapproachtostudydecision-makingandactionpreparation.(a)Single-neuroncorrelatesofdecisioncommitment.Anexampleof

ramp-to-thresholdtypeactivityofaneuronrecordedfromtheratsecondarymotorcortexduringspontaneousterminationofwaiting.Trialsaregrouped

accordingtothetimetakentogiveupwaiting,andspikedensityfunctionsarecalculatedforeachgroup.Differenttrialsarealignedonthestartof

waiting.Spikedensityfunctiontracesendattheendofwaiting.Notethattheactivityofthisneuroncrossesahypotheticalthresholdjustbefore

givingupwaiting(adaptedfromMurakamietal.[9_{]).(b)Populationperspectiveofdecisioncommitment.Populationdata(188neurons)recorded}

fromtheratsecondarymotorcortex[9_{]wasprojectedontothefirstthreeprincipalcomponents.Principalcomponentanalysiswasappliedto}

spikedensityfunctionsduringthewaitingperiodofeightdifferentsetsoftrials,groupedaccordingtowaitingtimesasindicatedbythecolorbar.

recently,asimilarcircuitmodelwasproposedfor sponta-neous action generation in which actions are generated without explicit sensory inputs [9,38]. Furthermore, mutual inhibition between neurons responsible for po-tential actions is thought to play an important role in competitionbetweendifferentpossibleactions[39].But theseideashavenotbeendirectlytestedandhowthese models could map onto actual cortical circuits is un-known. Tobuild andtest corticalcircuit models, afirst important step istocorrelate theresponsepropertiesof neurons with their identified cell-types and layers. A second, even more demanding, step is to establish the functional connectivity between neuronsand to under-standthepatternsofsignalpropagationwithinthecortical circuit[40,41,42].

Such an attempthasbeen made in pioneeringwork by Isomuraetal.[41],whocorrelatedtheresponseproperties ofneuronsduringactionpreparationandexecutionwith theircell-typesandfunctionalconnectivitybetween neu-rons.Theyfoundthatmostoftheinhibitoryinterneurons, someofwhichwereidentifiedwithjuxtacellularlabeling and immunohistochemistry, were active during move-ment periods, slightly after movement-related activity ofpyramidalneurons.Furthermore,whentheyexamined functional connectivity among neurons with different typesofresponseproperties,afewinstancesofrecurrent excitationbetweenpairsofpre-movementtypeneurons were found, suggestive that they might constitute ele-mentsof anintegratorcircuit.

In the context of decision-making, it is proposed that differentpools of recurrently-connected excitatory neu-ronscompetewitheachotherthroughmutualinhibition

[39,43],whichprovidesneuralsubstrateof

decision-mak-ing. Consistent with this model, although not a direct proof, it hasbeen observed that neuron pairs from the ‘samepool’exhibitpositivecorrelationsandneuronpairs fromthe‘differentpools’exhibitnegativecorrelationsin theiractivityfluctuationsacrosstrials[9,19,44]. Exam-iningfurtherthesourcesofcorrelationswillbecrucialin pursuingthishypothesis.

Recent advances in optogenetic and pharmacogenetic approachesprovide morepowerful meanstorecord and manipulate specific elements of neural circuits [45,46]. Theseapproacheshavealreadyproventobepowerfulin understandingcircuitcomputationsinsensoryandother systems[34,47,48].Kvistianietal.appliedthisapproach toastudyofforagingdecisionandfoundcell-typespecific functional differentiation of inhibitory interneurons in anterior cingulate cortexof mice[48].While parvalbu-min-positive interneurons were activated upon leaving reward sites,narrow-spikingsomatostatin-positive inter-neurons were active during reward approach. These studies strongly suggest that the diversity of response profilesseenin‘blind’recordingsmayinpartbedictated

byanunderlyingcircuitlogicrelatedtothecomputations beingperformed.Itwillnodoubtbeextremelyrevealing, albeit painstaking, to systematically map this logic for tasks involvingdecision-makingandactionpreparation.

As mentioned in the previous sections, recent studies have suggested that multiple signals are often mixed within individual neurons. Meaningful and potentially simple neural dynamics can be hidden in interactions among populationsoftheseneuronsin amannerthatis not intuitively obvious. In such scenarios, it may be useful,if notnecessary, touse model-based approaches to help to analyze dynamics of population activity and relate it to possible circuit computations. While this approach may bestill in itsinfancy, it has beenshown thatrecurrent networkmodelscanreproduceimportant featuresofpopulationneuralactivityandanimalbehavior

[15,19,49,50]. Recurrent networks can be built in a

numberofdifferentways.Theycanbebuiltbyexplicitly designing acomputation[39],butthey canalsobe con-structedwithoutexplicitlyspecifyingcomputation,either byfittingdynamicsofpopulationneuraldatacompressed in low dimensions [19,25], by constraining what the network should dobut nothow to do it[15],or even

Figure2 i j wij vik k τ = −ri(t) +

Σ

Σ

Current Opinion in Neurobiology

Twocomplementaryapproachestocircuit-levelunderstandingof

decision-makingandactionpreparation.(Left)Schematicillustrationof

recordingsofcorticalneuronactivity.Recordingtheactivityofneurons

togetherwithidentificationofcell-typesofrecordedneuronsfrom

awake,behavinganimalswillcharacterizeresponsepropertiesof

differentneurontypes.(Right)Schematicillustrationofarecurrent

networkmodelandanexamplerecurrentmodelequation.Inthe

equation,ri(t)denotesactivityofamodeluniti,ak(t)denotesactivityof

anexternalinputunitk,wijdenotesconnectionstrengthfromaunitj

toauniti,vikdenotesconnectionstrengthfromanexternalinputunit

ktoauniti,andtdenotesdecaytimeconstantofmodelunit.

Recurrentnetworkmodelsareusefulinsimulatinglargenumberof

neuronsandanalyzingthedynamicsofpopulationactivity.Detailed

informationgainedfromexperimentalapproacheshelpstoconstrain

corticalnetworkmodels.Inturn,networkmodelshelptoexplain

byusing an independent principle [50]. By analyzing thesemodels,onecanextractthedynamicalstructurein thenetworkthatiscriticalforperformingagiventaskby analyzinglocallylineardynamicalsystem[51].For exam-ple,intherecurrentnetworkmodelofManteetal.[15], differentcontinuousattractors appear for different con-texts,enablingintegrationofsensorystimuliina context-dependent manner. Local dynamics near the attractor selectthetask-relevantsensoryfeaturestobeintegrated, whileignoringtheirrelevantfeatures.

Althoughrecurrent networkmodelsare provingto bea powerful tool to analyze cortical computation, they re-maintobevalidatedandrefinedbymoredetailed exper-imental data. Recurrent network models do not yet normally take into account cortical circuit architecture (e.g.,layerandcelltypes)orcorrelationstructureamong neurons(butsee[52]).Asmentionedabove,thesetypes ofinformationare becomingmorereadily available.We expectthatacombinationofexperimentalapproachesto provideinformationaboutresponsepropertiesand inter-actions of cortical circuit elements and theoretical approaches to build models incorporating these data and understand principles for cortical computation will befruitful(Figure 2).

Conclusion

Decision-relatedandaction-related signalsappear tobe distributedin neuralpopulations with various temporal profilesandmixedwithunrelatedsignals,indicatingthe necessityofstudiesofpopulation decodingand interac-tionsamongpopulationsof neurons.Dimensionality re-duction techniques and other statistical approaches provide important tools to extract relevant information frompopulationsofneuronsandcanevensuggest possi-ble mechanisms for decoding by downstream circuits. Optogenetic approaches allow recording and manipula-tionofneuralactivitytransmittedtospecificdownstream areas,experimentsthatcanhelptotestputativedecoding strategiesandlinkdecision-relatedinformationtoanimal behavior.

Understandinghowpopulationsofneuronsinteract with-in a cortical circuit is even more challenging. For this purpose,recurrentnetworkmodelsaremadetoreproduce importantfeaturesof neuralpopulationdataand behav-ior. Analysis of these network models can be used to extractsimpledynamicalstructuresinapparentlydiverse populationdynamics.Tofurtherunderstandthe compu-tations performed in cortical circuits, existing cortical circuit architecture must be integrated into models of corticalnetworks.Forthispurpose,itiscrucialto contin-ue experimental efforts to provide details of neuronal activity together with cell-identity and functional con-nectivity,andtoincorporatesuchinformationintocortical networkmodels.

Conflict

of

interest

statement

Nothingdeclared.

Acknowledgements

WethankCindyPoo,EranLottem,GautamAgarwalforhelpfulcomments

onthemanuscript.ThisworkwassupportedbytheEuropeanResearch

CouncilAdvancedInvestigatorGrant(#250334,ZFM),TheSimons

CollaborationontheGlobalBrain(#325057,ZFM)andChampalimaud

Foundation(ZFM).

References

and

recommended

reading

Papersofparticularinterest,publishedwithintheperiodofreview,

havebeenhighlightedas:

 ofspecialinterest

ofoutstandinginterest

1. CisekP:Makingdecisionsthroughadistributedconsensus. CurrOpinNeurobiol2012,22:927-936.

2. GoldJI,ShadlenMN:Theneuralbasisofdecisionmaking.Annu RevNeurosci2007,30:535-574.

3. ChurchlandAK,DitterichJ:Newadvancesinunderstanding decisionsamongmultiplealternatives.CurrOpinNeurobiol 2012,22:920-926.

4. AndersenRA,CuiH:Intentionactionplanning,anddecision makinginparietal–frontalcircuits.Neuron2009,63:568-583.

5. LecasJC,RequinJ,AngerC,VittonN:Changesinneuronal activityofthemonkeyprecentralcortexduringpreparationfor movement.JNeurophysiol1986,56:1680-1702.

6. RiehleA,RequinJ:Thepredictivevalueforperformancespeed ofpreparatorychangesinneuronalactivityofthemonkey motorandpremotorcortex.BehavBrainRes1993,53:35-49.

7. LebedevMA,O’DohertyJE,NicolelisMAL:Decodingoftemporal intervalsfromcorticalensembleactivity.JNeurophysiol2008, 99:166-186.

8. MaimonG,AssadJA:Acognitivesignalfortheproactivetiming ofactioninmacaqueLIP.NatNeurosci2006,9:948-955.

9. 

MurakamiM,VicenteMI,CostaGM,MainenZF:Neural antecedentsofself-initiatedactionsinsecondarymotor cortex.NatNeurosci2014,17:1574-1582.

Ratsperformedawaitingtaskinwhichtheywererequiredtowaitfora

randomlydelayedcuetoobtainalargereward,butallowedtobailoutat

anytimetoobtainasmallreward.Authorsfoundtwotypesofneural

activitypredictiveofwaitingtimeinthesecondarymotorcortex.The

authorsproposethesetwotypescorrespondtoinputandoutputofan

integratorcircuitthatisinvolvedindecidingtimingofactions.Thisisalso

thefirststudyreportingramp-to-thresholdtypeneuronsinrodents.

10. BennurS,GoldJI:Distinctrepresentationsofaperceptual decisionandtheassociatedoculomotorplaninthemonkey lateralintraparietalarea.JNeurosci2011,31:913-921.

11. NewsomeWT,GlimcherPW,GottliebJ,LeeD,PlattML: Commenton‘‘Inmonkeysmakingvalue-baseddecisionsLIP neuronsencodecuesalienceandnotactionvalue’’.Science 2013,340:430.

12. LeathersML,OlsonCR:Inmonkeysmakingvalue-based decisionsLIPneuronsencodecuesalienceandnotaction value.Science2012,338:132-135.

13. ChurchlandMM,CunninghamJP,KaufmanMT,RyuSI, ShenoyKV:Corticalpreparatoryactivity:representationof movementorfirstcoginadynamicalmachine? Neuron2010, 68:387-400.

14. 

MeisterMLR,HennigJA,HukAC:Signalmultiplexingand single-neuroncomputationsinlateralintraparietalareaduring decision-making.JNeurosci2013,33:2254-2267.

Whileamonkeyperformedarandomdotmotiondirectiondiscrimination

task,theauthorsrecordedfromneuronsinLIPneurons,includingones

conventionallyexcludedfrommanypreviousstudies.Thestudyreports

varioustemporaldynamicsofsignalscarryingdecision-related

these signals are mixed with decision-unrelated signals. The study

stimulatesfurtherstudyofhowsuchavarietyofsignalscontributesto

thedecisionprocess.

15. 

ManteV,SussilloD,ShenoyKV,NewsomeWT: Context-dependentcomputationbyrecurrentdynamicsinprefrontal cortex.Nature2013,503:78-84.

Monkeysdiscriminatedeitherthemotiondirection orthecolorofthe

randomdotsdependingonthecontextcue.AnalysisofPFCrecordings

frommonkeysperformingthetaskwascombinedwiththeanalysisofa

recurrent networkmodeltrainedtosolvethesametask,leadingtoa

proposedmechanismtolinksensorystimulitomotoroutputina

context-dependentmanner.Thisstudyprovidesaniceexampleofhowa

recur-rent networkmodel andanalysisoffixedpointsandlinear dynamics

aroundthemcanbeusedtomakesenseofpopulationneuralactivity.

16. HarveyCD,CoenP,TankDW:Choice-specificsequencesin parietalcortexduringavirtual-navigationdecisiontask. Nature2012,484:62-68.

17. 

RaposoD,KaufmanMT,ChurchlandAK:Acategory-freeneural populationsupportsevolvingdemandsduring decision-making.NatNeurosci2014,17:1784-1792.

Theauthorsanalyzedtheactivityofposteriorparietalcortexneuronsfrom

rats performinga visualand/orauditorydiscriminationtask,inwhich

subjectsreportedwhethertherateofstimuluspulseswerehigherorlower

thanapredeterminedthreshold.Theneuronswereselectiveforboththe

sensorymodalityofthestimulusandfortheupcomingchoice.

Impor-tantly, thesetwotypes ofselectivity wereintermixedwithin individual

neurons andno clustersofneurons interms ofhow thesignalsare

intermixedwerefound.

18. 

ParkIM,MeisterMLR,HukAC,PillowJW:Encodingand decodinginparietalcortexduringsensorimotor decision-making.NatNeurosci2014,17:1395-1403.

Theauthorsre-analyzedthedatarecordedintheprimatelateral

intrapar-ietalareaduringtherandomdotmotiondiscriminationtask[14],usinga

generalized linear model-based statistical approach. The encoding

model successfully predictedthefiringratesofsingle neurons using

thesensorystimulus,futuremotoroutputsandtheneuron’sspikehistory.

Usingtheencodingmodel,theyinturnperformeddecodingofupcoming

choicesfromobservedspiketrains,whichcouldbeapproximatedwitha

simpleandbiologicallyplausibledecodingmechanism.

19. MachensCK,RomoR,BrodyCD:Functional,butnot anatomical,separationof‘‘what’’and‘‘when’’inprefrontal cortex.JNeurosci2010,30:350-360.

20. KobakD,BrendelW,ConstantinidisC,FeiersteinCE,KepecsA, MainenZF,RomoR,QiX-L,UchidaN,MachensCK:Demixed principalcomponentanalysisofpopulationactivityinhigher corticalareasrevealsindependentrepresentationoftask parameters.2014:.arXiv.1410.6031v1.

21. 

RigottiM,BarakO,WardenMR,WangX-J,DawND,MillerEK, FusiS:Theimportanceofmixedselectivityincomplex cognitivetasks.Nature2013,497:585-590.

Theauthorsanalyzedneuraldatarecordedfromtheprefrontalcortexin

monkeysperformingataskinwhichthesubjectisrequiredtoremember

theidentityandorderoftwosequentiallypresentedobjects.Thememory

wastestedafteradelayperiodwitheitherarecognitiontaskorarecall

task.Theprefrontalcortexneuronsrecordedinthistaskshowedlinear

andnon-linearmixedselectivitytothetask-relatedfeature,suchasthe

task type, first cue, and second cue. Animal performancecould be

predictedby theeffective dimensionalityofthedata, becauseitwas

reducedinerrortrials.Thedimensionalitygainedbynon-linearselectivity

components of neuronal responses was crucial in predicting animal

performance.Thisindicatestheimportanceofnon-linearlymixed

repre-sentationoftaskvariablesinsolvingacomplextask.

22. CunninghamJP,YuBM:Dimensionalityreductionfor large-scaleneuralrecordings.NatNeurosci2014,17:1500-1509.

23. AfsharA,SanthanamG,YuBM,RyuSI,SahaniM,ShenoyKV: Single-trialneuralcorrelatesofarmmovementpreparation. Neuron2011,71:555-564.

24. AmesKC,RyuSI,ShenoyKV:Neuraldynamicsofreaching followingincorrectorabsentmotorpreparation.Neuron2014, 81:438-451.

25. ChurchlandMM,CunninghamJP,KaufmanMT,FosterJD, NuyujukianP,RyuSI,ShenoyKV:Neuralpopulationdynamics duringreaching.Nature2012,487:51-56.

26. 

KaufmanMT,ChurchlandMM,RyuSI,ShenoyKV:Cortical activityinthenullspace:permittingpreparationwithout movement.NatNeurosci2014,17:440-448.

Thestudyproposesanovelmechanismforcontrollingwhetherornot

neuralsignalsaretransmittedtodownstreamtargetareas,withoutrelying

onathresholdorgatingmechanism.Theauthorsdefine‘output-potent’

readoutsasweightedsumsofpopulationactivitythatresemblesmostthe

activity ofadownstream areaduringthemovementperiod.Although

neuronsareactiveduringpreparatoryperiod,suchoutput-potent

read-outsofpopulationactivityarestronglyreducedbecausethepopulation

dynamicsisconstrainedtoanorthogonalsubspace.

27. ShenoyKV,SahaniM,ChurchlandMM:Corticalcontrolofarm movements:adynamicalsystemsperspective.AnnuRev Neurosci2013,36:337-359.

28. SommerMA,WurtzRH:Frontaleyefieldsendsdelayactivity relatedtomovement,memory,andvisiontothesuperior colliculus.JNeurophysiol2001,85:1673-1685.

29. SegravesMA,GoldbergME:Functionalpropertiesof corticotectalneuronsinthemonkey’sfrontaleyefield.J Neurophysiol1987,58:1387-1419.

30. ChenJL,CartaS,Soldado-MagranerJ,SchneiderBL, HelmchenF:Behaviour-dependentrecruitmentoflong-range projectionneuronsinsomatosensorycortex.Nature2013, 499:336-340.

31. LimaSQ,Hroma´dkaT,ZnamenskiyP,ZadorAM:PINP:anew methodoftaggingneuronalpopulationsforidentification duringinvivoelectrophysiologicalrecording.PLoSONE2009, 4:e6099.

32. 

TervoDGR,ProskurinM,ManakovM,KabraM,VollmerA, BransonK,KarpovaAY:Behavioralvariabilitythrough stochasticchoiceanditsgatingbyanteriorcingulatecortex. Cell2014,159:21-32.

This study useda combination of quantitative behavioral tasks and

pathwayspecific neuralmanipulation todemonstrate a roleoflocus

coeruleus(LC) noradrenergicinputtoanterior cingulatecortex(ACC)

incontrollingbehavioralchoicestrategy.Inthisstudy,ratsperformed

two-choicetasksagainst computer-simulatedcompetitors.Whilerats

usedhistory-dependentstrategieswhenfacedwithweakcompetitorsor

whenexplicitlytrainedtogenerateapatternofchoicesequence,they

couldswitchto‘stochastic’choicemodewhenchallengedwithastrong

competitor.ManipulatingLCnoradrenergicinputstoACC,theauthors

showedthatwhileactivationofthispathwaysuppressed

history-depen-dentstrategiesandenhancedastochasticbehavioralmode,inactivation

causedanoppositeeffect.

33. 

ZnamenskiyP,ZadorAM:Corticostriatalneuronsinauditory cortexdrivedecisionsduringauditorydiscrimination.Nature 2013,497:482-485.

The authors trained rats to perform an auditory discrimination task

requiring them to discriminate between low-frequency and

high-fre-quencysoundswithvariabledifficulty.Thechoicesofratswerebiased

whencorticostriatalneuronsintheauditorycortexwereoptogenetically

activated.Thedirectionofbiaswaspredictedfromthetuningspecificity

ofthestimulatedarea:stimulationofhighfrequency-tunedareabiased

thechoicetowardthechoiceportassociatedwiththehighfrequency

soundandviceversa.Interestingly,thissystematicbiaseffectwasseen

onlywhenthestriatum-projectingneuronswerestimulated.When

cor-ticalneuronswerenon-specificallyactivated,thebiaswasnotpredicted

fromthetuningspecificityofthestimulatedarea.

34. 

ZhangS,XuM,KamigakiT,HoangDoJP,ChangW-C,JenvayS, MiyamichiK,LuoL,DanY:Long-rangeandlocalcircuitsfor top-downmodulationofvisualcortexprocessing.Science 2014,345:660-665.

The authors usedoptogenetic manipulationof aspecific projection

pathway and specific cell-typestostudy thecircuit mechanismsof

top-downcorticalmodulationofvisualprocessingintheprimaryvisual

cortex(V1)byanteriorcingulatecortex(ACC).LocalactivationofACC

neuronaxonsatV1causedbothenhancementofvisualresponsesinthe

stimulatedareaandinhibitioninsurroundingareas.

Interneuron-sub-type-specificinactivationtogetherwithACCaxonstimulation

experi-mentsrevealedthatsurroundinhibitionwasmainlymediatedthrough

somatostatin-positive neurons, while activation was partlymediated

throughvasoactiveintestinalpeptide(VIP)-positiveneuronsvia

disin-hibition.

35. ShepherdGMG:Intracorticalcartographyinanagranulararea. FrontNeurosci2009,3:337-343.

36. DouglasR,MarkramH,MartinKAC:Neocortex.In Thesynaptic organizationofthebrain.EditedbyShepherdGM. NewYork: OxfordUniversityPress;2004:499-558.

37. MazurekME,RoitmanJD,DitterichJ,ShadlenMN:Arolefor neuralintegratorsinperceptualdecisionmaking.CerebCortex 2003,13:1257-1269.

38. SchurgerA,SittJD,DehaeneS:Anaccumulatormodelfor spontaneousneuralactivitypriortoself-initiatedmovement. ProcNatlAcadSciUSA2012,109:E2904-E2913.

39. WangX:Probabilisticdecisionmakingbyslowreverberationin corticalcircuits.Neuron2002,36:955-968.

40. FujisawaS,AmarasinghamA,HarrisonMT,Buzsa´kiG: Behavior-dependentshort-termassemblydynamicsinthemedial prefrontalcortex.NatNeurosci2008,11:823-833.

41. IsomuraY,HarukuniR,TakekawaT,AizawaH,FukaiT: Microcircuitrycoordinationofcorticalmotorinformationin self-initiationofvoluntarymovements.NatNeurosci2009, 12:1586-1593.

42. 

HirabayashiT,MiyashitaY:Computationalprinciplesof microcircuitsforvisualobjectprocessinginthemacaque temporalcortex.TrendsNeurosci2014,37:178-187.

Areviewofaseriesofstudiesfromtheauthors’groupstudying

micro-circuitcomputationusingmultiplesingle-unitrecordingsandfunctional

connectivityanalyses.Theauthorsperformedsimultaneousrecordings

frommultiple single-units inthetemporal cortexwhilemonkeys

per-formedanobjectassociationmemorytaskinwhichthesubjectsrecall

anddetectamemorizedpairofasamplevisualstimulus.Byanalyzing

directionoffunctionalconnectivitybetweenpairsofrecordedneurons

andresponsepropertiesofindividualneurons,theyexaminedsignalflow

andsignaltransformationinthecorticalmicrocircuits.Theyfound

direc-tedsignalflowbetweenneuronswithdifferentfunctionalproperties,

task-phase-dependentchangesinthedirectionofsignalflowacrosslayers

anddifferencesinfunctionalconnectivityacrosstemporalcorticalareas.

43. WongK-F,WangX-J:Arecurrentnetworkmechanismoftime integrationinperceptualdecisions.JNeurosci2006, 26:1314-1328.

44. CohenJY,CrowderEA,HeitzRP,SubravetiCR,ThompsonKG, WoodmanGF,SchallJD:Cooperationandcompetitionamong frontaleyefieldneuronsduringvisualtargetselection.J Neurosci2010,30:3227-3238.

45. LuoL,CallawayEM,SvobodaK:Geneticdissectionofneural circuits.Neuron2008,57:634-660.

46. RickgauerJP,DeisserothK,TankDW:Simultaneous cellular-resolutionopticalperturbationandimagingofplacecellfiring fields.NatNeurosci2014,17:1816-1824.

47. AtallahBV,BrunsW,CarandiniM,ScanzianiM: Parvalbumin-expressinginterneuronslinearlytransformcorticalresponses tovisualstimuli.Neuron2012,73:159-170.

48. 

KvitsianiD,RanadeS,HangyaB,TaniguchiH,HuangJZ, KepecsA:Distinctbehaviouralandnetworkcorrelatesoftwo interneurontypesinprefrontalcortex.Nature2013, 498:363-366.

Theauthorsrecordedfromneuronsinanteriorcingulatecortex(ACC)

whileamouseisperformingarewardforagingtask.Usingoptogenetic

cell-typeidentification ofrecorded neurons, theyfound afunctional

dissociationofdifferentinterneurontypes.Parvalbuminneuronswere

activeatrewardsiteexitandsomatostatinneuronswithnarrowspikes

are active at reward approach. This study illustrates the power of

optogenetictechniquestomapneuralresponsesontospecificcircuit

elements.

49. WangX-J:Neuraldynamicsandcircuitmechanismsof decision-making.CurrOpinNeurobiol2012,22:1039-1046.

50. 

HennequinG,VogelsTP,GerstnerW:Optimalcontrolof transientdynamicsinbalancednetworkssupportsgeneration ofcomplexmovements.Neuron2014,82:1394-1406.

The authors created a spontaneously stable recurrent network that

implementsaspring-loadedboxmodelofactionpreparationandaction

execution[13,27].Whenplacedinaproperinitialstateandreleasedfrom

there,thenetworkproduceslargetransientactivitywithvariabletemporal

activityprofilesduringtherelaxationperiodwithoutexternalinputs.The

resultanttemporalactivitypatternsarerichenoughtogeneratecomplex

movementpatternsoncecombinedproperly.Thissupportstheviewthat

theroleofpreparatoryperiodactivityisplacingthepopulationactivitytoa

properinitialstateforanintendedmovement.

51. SussilloD,BarakO:Openingtheblackbox:low-dimensional dynamicsinhigh-dimensionalrecurrentneuralnetworks. NeuralComput2013,25:626-649.

52. RenartA,delaRochaJ,BarthoP,HollenderL,PargaN,ReyesA, HarrisKD:Theasynchronousstateincorticalcircuits.Science 2010,327:587-590.