Time or distance encoding by hippocampal neurons via heterogeneous ramping rates

Domain: neuroscience
Published: 2025-12-17
Editorial outcome: major_revision

Abstract
Tonavigatetheirenvironmentseffectively,animalsfrequentlytracktime elapsedordistancetraveledwhileseekingfoodandavoidingthreats.The hippocampusisimplicatedinthisprocess,buttheneuralmechanismsremain unclear.Usingvirtualrealitytasksthatrequiremicetointegratetimeordis- tancetocollectareward,weidentifiedtwopreviouslyunknownfunctional subpopulationsofCA1pyramidalneurons.Bothsubpopulationsencodetime ordistanceviadistinctrampingdynamics.Thefirstsubpopulationexhibitsa rapid,synchronousriseinactivityuponmovement-initiatedintegration. Subsequently,individualneuronsrampdownatheterogeneousrates,creating progressivelydivergingfiringratesthatencodeelapsedtimeordistance. Closed-loopoptogeneticinactivationofsomatostatin-positive(SST)inter- neuronscounterintuitivelyreducedtherampingactivity,leadingmiceto prematurelyattemptrewardcollection,suggestingimpairedtime/distance estimation.Conversely,thesecondCA1subpopulationshowsopposite dynamics—aninitialrapidsuppressionfollowedbyagradualramp-up.Inac- tivatingparvalbumin-positive(PV)interneuronsdiminishedthisinitialsup- pression,resultingintransientattemptstocollectrewardnearintegration onset.Thesefindingsrevealparallelhippocampalcircuitsthatinitiateand maintaintimeordistanceencoding,controlledbyPVandSSTinterneurons, respectively,andprovideinsightsintotheneuralcomputationssupporting goal-directednavigation.

Introduction
Whennavigatingadimlylitpathatnight,weoftenrelyonoursenseof ratherthanexternalcues,and arethus termedinternallygenerated distancetraveledtodetermineourlocation.Thisprocess,knownas firing fields (IGFs)6–12. Collectively, neurons with IGFs activate in path integration, involves the hippocampus, which integrates self- sequencetoproduceaninternallygeneratedsequence(IGS),which motion information to estimate distance relative to recognizable encodesmoment-to-momentdistanceortimetoaidpathintegration.

Inwell-litenvironmentsrichwithcues,somehippocampalpyr- Recentresearchhasadvancedourunderstandingofthecircuit- amidal neurons, called place cells, preferentially activate at specific level mechanisms that govern place cell and IGF neuron activity, locations5.Yet,evenindimlylitenvironmentsorwhenenvironmental highlightingthecriticalroleoflocalinhibitoryinterneurons.Different cuesaresparseorabsent,neuronscanstillactivateatparticulardis- subtypes of interneurons are known to exhibit distinct temporal tancesortimepointsrelativetoalandmark.Thesedistance-ortime- dynamicsandtargetspecificsubcellularcompartmentsofpyramidal specific activity patterns are driven mainly by internalcomputation neurons13. For example, somatostatin-positive (SST) interneurons primarily inhibit dendrites, whereas parvalbumin-positive (PV) interneurons preferentially inhibit the perisomatic region13. This compartment-specific inhibition regulates synaptic integration and spike output14–17,whichinturnshapesthespatialandtemporalfiring patternsofplacecells18,19.Similarinhibitorymechanismslikelyapplyto neuronswithIGFs.

However,given that only a subset of active pyramidalneurons exhibitIGF,acriticalquestionarises:Howdothebroaderpopulation dynamics in CA1 support path integration, and how do inhibitory interneuronscontributetothesebroaderdynamicpatterns?

Inthisstudy,weuncoveredaneuralcodeinthehippocampalCA1 area,comprisingtwopreviouslyunknownfunctionalsubpopulations ofneuronsthattrackdistanceortimethroughtheirrampingactivity. Investigatinglocalinhibitoryinterneurons,wefoundthatSSTandPV interneurons18–20playeddistinctrolesinshapingtheactivityofthese subpopulations and influencing the accuracy of path integration. Together,ourstudyrevealstwoparallelneuralcircuitswithinCA1that complementeachothertoenabledistanceortimeintegration,offer- inginsightsintothecircuit-levelmechanismsunderpinningnavigation andmemoryencoding.

Results
Abehavioraltaskthatrequiresdistanceintegration generatesIGSs

Toinvestigateneuronaldynamicsduringpathintegration,wedevel- opedavirtualreality(VR)-basedbehavioraltaskthatrequiredhead- fixedmicetoestimatedistancebyaccumulatingself-motion—atask we refer to as the path integration (PI) task (Fig. 1a). In each trial, followingabriefvisualcue,theanimalhadtorun180cmthrougha constant gray VR background. This 180cm run established a cue- constantsegment.Toreceiveawaterreward,theanimalwasrequired to lick a designated port within a reward zone (Fig. 1b left; see Methods).

Trainedanimalsdisplayedstereotypicalrunningspeedprofiles and consistent licking patterns across trials (Fig. 1c left, d, e); notably,accurateperformancedependedonanintacthippocampus (Supplementary Fig. 1a–d). Animals typically initiated licking as they approached the end of the cue-constant segment (Fig. 1e; mean number of licks/cm from 100 to 180cm: 0.087±0.011. Mean±SEM and Wilcoxon rank-sum test are used if not explicitly stated), implying that they tracked distance to anticipate the reward.However,theconsistentrunningspeedprofilesacrosstrials madeitambiguouswhetheranimalswereintegratingoverdistance ortime.

ExtracellularrecordingsinCA1usingsiliconprobesrevealed that a subset of pyramidal neurons fired at specific distances duringthecue-constantsegment,expressinginternallygenerated firing fields (IGFs) (Fig. 1f right, h, and Supplementary Fig. 1e; 4.39±0.58% neurons; see Methods). These neurons collectively formedanIGSthatunfoldedastheanimalprogressedthroughthe cue-constantsegment(Fig.1g).

To confirm that IGS expression requires distance or time inte- gration,wecomparedthePItaskwithacontrolpassivetask.Inthe passivetask,animalshadtorunfor180cmwhileviewingthesamegray screens, but a water reward was automatically delivered atthe end withoutrequiringlicking(Fig.1bright).Tofurtherdiscouragedistance or time integration, we additionally introduced randomness in the start of the trial (see Methods). This passive task, which does not require animals to integrate over distance, replicates previously reportednon-memorytasks6,21.

Behaviorally, animals in the passive task ran at a mean speed similartothatinthePItask(Fig.1crightandd;Passive:34.41±1.80cm/ s,PI:34.73±1.23cm/s,p=0.63;seeMethods).However,therewasa significantdecreaseinpredictivelicksbeforerewarddelivery(Fig.1e; meannumberoflicks/cmfrom100to180cm:Passive:0.042±0.012, PI:0.087±0.011,p=0.012).

Correspondingly, CA1 pyramidal neurons in the passive task exhibitedsignificantlylowertrial-by-trialactivitycorrelation(Supple- mentary Fig. 1f; Passive: 0.006±7.9e−04 (536 neurons), PI: 0.033±5.2e−04(7828neurons),p=3.6e−94).Neuronsfiredthrough- outthecue-constantsegment(Fig.1fmiddle),andtheIGFwasabsent (Fig. 1h). These results collectively demonstrate that IGSs are pre- ferentially expressed during tasks that require distance or time integration.

ThesefindingsthusvalidatedthePItaskasasuitableparadigmfor investigating the internally generated hippocampal dynamics under conditionsofdistanceortimeintegration.

TheinitiationofanIGSalignswiththeself-initiatedonsetof running

InthePItask,theanimal’srunonset,ratherthanthevisualcueonset, likelyservedasthestartingpointofintegration(Fig.2a,b).Therewasa 1.15-seconddelay,onaverage,betweentheinitialvisualcueandrun onset (Fig. 2a–d; see Methods), suggesting that animals likely self- initiatedtheirrunningratherthanmerelyrespondingtothecue.

Aligning neuronal activity with run onset in each trial revealed neurons thatfired consistently atspecific time points (IGFs). These IGFswerelessevidentwhenaligningthesameactivitywithcueonset (Fig.2e).Furthermore,run-onsetalignmentledtoagreaterproportion ofneuronswithIGFs(Fig.2g;PI:9.46±0.72%neurons,comparedto Fig. 1h). These IGF neurons alsoshowed higher trial-by-trialactivity correlation(Fig.2h)andexhibitedhighertemporalinformationcon- tentcomparedtocue-onsetalignment(cueonset:0.29±0.0077bits/ spike,runonset:0.34±0.0091bits/spike,p=3.0e−05).Atthepopu- lation level, IGSs became sharper with run-onset alignment (Fig. 2f; population correlation: cue onset: 0.20±0.0086, run onset: 0.24±0.0076,p=1.9e−04).Finally,neuronswithIGFexhibitedmore pronouncedphaseprecession6,22,23(SupplementaryFig.2a,b),ahall- markoffiringfieldsinCA1.

Beyond IGF neurons, we examined how run-onset alignment affects the broader activity patterns of all recorded CA1 pyramidal cells. Indeed, aligning activity to run onset enhanced trial-by-trial activitycorrelationsthroughouttheCA1population(Supplementary Fig. 2c, d). We then considered a potential confound, whether this effectwasduetoaswitchinbrainstate(fromnon-thetatotheta)as animalstransitionedfromstoppingforrewardtostartingthenextrun. However,this is unlikely becausethe average pausetime was brief, duringwhichthetaoscillationslargelycontinueduninterruptedduring thisinterval(pausemeantime=1.88s,SupplementaryFig.4a–c;see Methods).

Next,weinvestigatedwhetherthelackofIGFsduringthepassive taskwasduetomisalignmentwiththerunonsetorreflectedalackof integration. Even after run-onset alignment, IGFs remained nearly absent(Passive:0.19±0.19%ofneurons;Fig.2g),with onlyone IGF detected across all passive recordings (Supplementary Fig. 2g; see Methods).Toruleoutrunningspeedasaconfound,wefurthercon- firmedtheseresultsbyanalyzingPItaskrecordingswithrunningspeed profilesmatchedtothoseinthepassivetask(SupplementaryFig.2h–j; seeMethods).Consistentwiththeresultsabove,pyramidalneuronsin thepassive taskshowed significantlylowertrial-by-trial activitycor- relationscomparedtothePItask(SupplementaryFig.2e,f).Together, these findings further support the requirement for distance or time integration for IGF generation.

TofurtheraddresswhetherIGFneuronsencodedistanceortime, we employed a Generalized Linear Model (GLM) approach8 (see Methods).Foreachneuron,wemodeleditsfiringactivityusingpre- dictorsincludingtimeanddistancetraveledsincerunonset,running speed,andrecentspikinghistory.Byestimatingtheuniquecontribu- tion of each predictor, we found that a substantial proportion of neuronsweresignificantlymodulatedbytime(53.8%)andbydistance (56.4%) (Supplementary Fig. 3a). Many individual neurons showed significant contributions from both factors (42.1% conjunctively tuned),whileatthepopulationlevel,therewasnosignificantdiffer- enceintheoverallcontributionoftimecomparedtodistance(Sup- plementaryFig.3a).

In summary, run onset aligns the neuronal activity involved in path integration, likelymarkingtheinitiationoftheneuralcomputa- tionfordistanceortimeestimation.

AccurateintegrationiscorrelatedwiththeexpressionofIGSs

To investigate how IGS expression relates to task performance, we categorizedeachtrialaseither“good”or“bad”basedontheanimals’ locomotion patterns and reward outcomes. Good trials met three criteria: animals came to a complete stop before trial run onset (therebyensuring thisonsetmarkedthestartofintegration),main- tained largely uninterrupted running, and successfully obtained the reward(goodtrials:77.90±2.88%ofalltrials,seeMethods).Incon- trast,badtrialsfailedtomeetatleastoneofthesecriteria.Whilebad trialsmaintainedameanrunningspeedsimilartothatofgoodtrials (Fig.3a;good trials:48.12±0.84cm/s,badtrials:45.20±0.84cm/s), theyresultedinacomparativelylowerrewardrate(86.34±4.36%)than the100%ingoodtrials.

Examining anticipatory licking behavior revealed that, in bad trials,animals showed significantlymore licking in the early partof running(Fig.3b;meanlicks/cmbefore100cmdistance:goodtrials: 0.0056±0.0012,badtrials:0.013±0.0032;meanlicks/20msbetween 0to1stime:goodtrials:0.0081±0.0015,bad trials:0.042±0.005, p=1.31e−10).This behavior implied an inaccurate estimationofdis- tanceortime.

Correspondingly, neuron activity in bad trials showed clear impairments: IGF neurons exhibited reduced trial-by-trial activity correlation (Fig. 3d), and the IGSs were disrupted (Fig. 3c and Supplementary Fig. 5a). We confirmed this impairment through several control analyses: It persisted after random subsampling of goodtrialstomatchthenumberofbadtrials(SupplementaryFig.5b; see Methods) and when IGSs were plotted against distance rather than time (Supplementary Fig. 5c,d). Furthermore, to exclude the possibilitythattheIGSimpairmentinbadtrialswasduetoalackof properrunonset,wereanalyzedgoodandbadtrials,selectingonly thosewhereanimalsreachedacompletestopbeforetrialrunonset (10 animals, 13 recordings, good trials: 68.17±4.19% of all trials, rewardedbadtrials:79.54±7.24%).ThenewcriterialedtosimilarIGS impairment (Supplementary Fig. 6a–c). Together, these findings indicatethattheIGSexpressioniscloselylinkedtoaccuratedistance or time estimation, as reflected in both neuronal and behavioral measures.

Subpopulationsofpyramidalneuronsdisplaydistinctresponses aroundrunonset

GiventhatonlyasmallsubsetofCA1pyramidalneuronsexhibitedIGF inthePItask,weaimedtoinvestigatethedynamicsofallpyramidal neuronsasafunctionoftimeordistancefromrunonset.Wesortedall recordedneurons(7114neurons,28animals,including650IGFneu- rons) based on their change in firing rate around run onset. These changeswerequantifiedasthefiringrateratioR(Fig.3eandSupple- mentaryFig.5e;R=FR /FR ,whereFR wascalculatedfrom−1.5to aft bef bef −0.5sandFR from0.5to1.5srelativetorunonset).
aft

Thisapproachrevealedtwodistinctfunctionalsubpopulationsof pyramidalneurons.“PyrUp”neurons (37.2%ofthe neurons,R>3/2) rapidlyincreasedtheiractivityatrunonsetandthengraduallyramped down asthe animalapproached the rewardzone (Fig. 3f left, g, h), whereas “PyrDown” neurons (19.4%, R<2/3) initially reduced their activitybutthenrampeduptowardtherewardzone(Fig.3fright,i,j). Neuronsexhibitingintermediateratios(2/3≤R≤3/2)wereclassifiedas “PyrOther”(SupplementaryFig.7a).

To validate these functional subpopulations, we compared observedRvaluesagainstanulldistributiongeneratedbycircularly shiftingindividualtrialfiringrateprofiles(1000iterations).Neurons withRvaluesbeyondthe95thpercentileorbelowthe5thpercentileof this distribution were classified as PyrUp or PyrDown neurons, respectively. This approach confirmed the robustness of these sub- populations(SupplementaryFig.7b–d;seeMethods).

PyrUp and PyrDown neurons largely overlap in their laminar locationwithintheCA1pyramidallayer24,25(SupplementaryFig.7e,f; seeMethods),implyingthateachsubpopulationlikelycomprisesboth genetically and functionally distinct deep and superficial CA1 neurons24,26–29.

PyrUp/PyrDownresponsesreflectinternalintegration,not solelylocomotion

We next investigated whether PyrUp/PyrDown activity encoded internalcomputationsrelatedtotimeordistanceintegration,orifit merely reflected externalmotor variables such as running speed or licking.

First,wequantifiedthecontributionofvariousfactorstoPyrUp/ PyrDownactivityusingGLMs(seeMethods)8.Theseanalysesrevealed thatapproximatelyhalfofbothPyrUp(49.2%)andPyrDown(49.7%) neurons were significantly influenced by time, and a comparable proportionbydistance(PyrUp:50.5%;PyrDown:49.5%,Fig.4a,b).In contrast,themajorityofPyrUpandPyrDownneuronswerenotsig- nificantlymodulatedbyrunningspeed(notmodulated:PyrUp:63.9%; PyrDown: 64.6%), licking (not modulated: PyrUp: 69.2%; PyrDown: 71.2%;SupplementaryFig.8a,b),oracceleration(modulated:PyrUp: 4.2%; PyrDown: 3.8%; see Methods). Collectively, these GLM results suggest that PyrUp/PyrDown activity preferentially reflects internal integration processes rather than being simple correlates of motor outputs.

Second,weexaminedthetemporalrelationshipbetweenPyrUp/ PyrDown firing rates and speed profiles. Both PyrUp and PyrDown neurons reached peak activity significantly earlier than the peak in running speed (Supplementary Fig. 9c–h; median±SEM: PyrUp: 1.24±0.02s;PyrDown:1.21±0.03s;speed:1.69±0.08s;p=1.1e−7for PyrUpvs.speed;p=2.9e−8forPyrDownvs.speed,Wilcoxonrank-sum test).ThisanticipatoryfiringbeforethespeedpeaksuggeststhatPyrUp andPyrDownresponsesarenotmerelyadirectreadoutoflocomotion patterns.Moreover,theaccelerationprofileshowednoclearrelation- shiptoPyrUp/PyrDownresponses(SupplementaryFig.9a,b).

Third,weinvestigatedwhetherPyrUp/PyrDownactivitydirectly tracksvariationsinlocomotionspeed.Wecategorizedtrialsbyhigh (>45cm/s)andlow(35–45cm/s)averagespeeds(2199PyrUpneurons, 1246 PyrDown neurons, 84 recordings, 41 animals; see Methods). Despiteapparentdifferencesinrunningspeed,thefiringratesofboth PyrUpandPyrDownneuronsremainedlargelystable(Fig.4c–f;with PyrUpactivitystabilityevidentwithinthefirstthreeseconds).

To further reveal potential speed modulation at distinct time points, we characterized the time-resolved correlation between runningspeedandtheinstantaneousfiringrateineachneuron.We observedonlyweakcorrelations,withatransientincreaseprimarily duringthefirstsecondafterrunonset(SupplementaryFig.13a–d; see Methods). Yet, even this transient increase was significantly weakerthanthatobservedinsimultaneouslyrecordedinterneurons (Supplementary Fig. 13e), a population known for strong speed modulation30,31. These results support that PyrUp/PyrDown activity is only weakly and transiently modulated by locomo- tionspeed.

Fourth, we evaluated whether PyrUp/PyrDown responses are context-dependent,specificallytiedtothetrial-startrunonset(TRO) that marks the beginning of integration, rather than being generic responses to any run onset. We analyzed neuronal activity around spontaneous run onset (SRO), which occasionally occurred in the middleofatrial(17animals,32sessions,2902neurons;seeMethods).

Behaviorally,animalsdidnotexhibitaclearrewardzoneovershooting afteraSRO(percentofrewardedtrials:trialswithSR:92.98±1.93%, trialswithoutSR(TR):96.17±1.63%,p=0.07),andlickpatternsintrials with spontaneous runs remained unchanged (mean lick rates: 30- 100cm: SR: 0.011±0.0026 licks/cm, TR: 0.011±0.0027 licks/cm, p=0.83;100–140cm:SR:0.044±0.0070licks/cm,TR:0.054±0.0085 licks/cm, p=0.48; 140–180cm: SR: 0.13±0.010 licks/cm, TR: 0.14±0.013licks/cm,p=0.54).Theseresultssuggestedthatmid-trial stopsdidnotresettheongoingtimeordistanceintegration,andSRO wasunlikelytomarkanewintegrationstart.

Correspondingly, both PyrUp/PyrDown responses were sig- nificantlyattenuatedatSROcomparedtoTRO,despitesimilarmean firingrates(SupplementaryFig.10a,b;PyrUp:SRO:1.86±0.054Hz, TRO: 1.85±0.050Hz, PyrDown: SRO: 1.77±0.081Hz, TRO: 1.60±0.065Hz).Thisattenuationpersistedevenaftermatchingrun- ningspeedsbetweenSROandTROruns(SupplementaryFig.10c–e; 13 animals, 21 sessions, 1731 neurons; see Methods) and after sub- sampling TRO runs to match the number of SRO (Supplementary Fig.10d).Notably,thesespeed-matchedSROrunswerenotjustbrief movements;theirdurationwas2.81±0.05s(mean±SEM),compared to TRO runs of 3.83±0.04s. Thus, PyrUp/PyrDown responses are context-dependentandspecificallyelicitattherunonsetthatinitiates timeordistanceintegration.

Fifth,todemonstratethatPyrUpandPyrDownresponsesdonot obligatorilydependonlocomotion,wedesignedanimmobiletask.In thistask,animalsremainedstationary32butwererequiredtoestimatea 4-second time period while viewing gray screens (Supplementary Fig.11a,b;seeMethods).Eveninthisimmobilestate,similarpropor- tions of PyrUp and PyrDown neurons emerged (Supplementary Fig. 11d–k; 5 animals, 15 sessions, 748 neurons, PyrUp-Imm: 36.56±3.74%, PyrDown-Imm: 18.75±2.80%). Consistent with self- initiated time estimation, neuronal activity in this task aligned well withthelastlickoftheprevioustrial,ratherthanthevisualcue(Sup- plementary Fig. 11d–j), and IGF neurons were also present (Supple- mentary Fig. 11c; 6.72±1.56% neurons). Thus, PyrUp/PyrDown responsesoccur during tasksrequiringtimeor distanceintegration across distinct behavioral states, including both locomotion and immobility.

Finally,toprovidefurtherevidencethatPyrUpresponsescontain timeinformation,weappliedBayesiandecoding33.Thisanalysis,per- formedonthesingle-trialpopulationactivityofPyrUpneuronswith- outIGFs,effectivelydecodedtimepassage(SupplementaryFig.12a–d; seeMethods).

Collectively, these data supported that PyrUp/PyrDown activity encodestimeordistance,ratherthansimplyreflectinglocomotion.

PyrUp/PyrDownresponsespotentiallypredictreward- anticipatorybehavior

We next probed whether PyrUp/PyrDown dynamics are correlated withtaskperformance.

First, PyrUp/PyrDown responses around run onset were more pronouncedingoodtrialscomparedtobadtrials(Fig.3g–j),despite similar overall firing rates (measured from −1 to 4seconds, PyrUp: good trials: 1.76±0.032Hz, bad trials: 1.72±0.061Hz, p=0.19, Pyr- Down: good trials: 1.49±0.042Hz, bad trials: 1.50±0.087Hz, p=0.27). This result implies that the strength of their run onset responsescorrelateswiththeaccuracyoftimeordistanceestimation.

Second, we probed the correlation between PyrUp/PyrDown dynamicsandtheanimal’sinternalestimateofrewardtiming.InthePI task,anticipatorylickingservesasabehavioralproxyfortheanimal’s expectationofreward,whichiscontingentontimeordistanceinte- gration. We grouped trials based on the timing ofthe animal’s first anticipatorylick(earlylick:<2.5svs.latelick:>3.1s,492PyrUpand327 PyrDownneurons,18recordings,15animals;seeMethods).Although speedprofilesdifferedbetweenthesegroups,PyrUpactivityremained stable in the first 2seconds, and PyrDown neurons showed no sig- nificant changes overall(Fig. 4g–j). However, the PyrUp decay time constant increased significantly in late lick trials (PyrUp: early lick: 1.89±0.05svs.latelick:2.00±0.05s,p=0.036;Fig.4i).ThePyrDown rise timeconstant showed a similar, but not statistically significant, trend(PyrDown:earlylick:2.34±0.22s;latelick:3.02±0.26s;p=0.27; Fig.4j).Thesechangesinthedecay/riserateindicatedthatthePyrUp/ PyrDown dynamics adapt to the animal’s internal estimate of when sufficient time or distance has elapsed. This consistency between neuronaldynamicsandbehaviorsuggeststhatthedecay/riseratesof PyrUp/PyrDownresponsesmaypredictreward-anticipatorybehavior.

PyrUpandPyrDownneuronsemployatwo-phasecoding mechanism

HavingestablishedthatPyrUpandPyrDownneuronsencodetimeor distance and are not merely locomotor correlates, we next char- acterizedtheircodingstrategy.UnlikethesequentialactivationofIGF neurons tuned to specific time points, PyrUp neurons exhibited a collectivepeakatsimilartimesshortlyafterrunonset(Fig.4k).Their activity then decayed at heterogeneous, neuron-specific rates. This observationledustoproposeatwo-phaseintegrationmechanism.

Inthistwo-phaseintegrationmechanism,thefirstphaseischar- acterizedbyarapid,synchronizedincreaseinactivityacrossthePyrUp population.ThestabilityofPyrUpactivityduringthisperiodacross earlyandlatelicktrials,itsspecificitytothetrialstartrunonset,andits increasedspeedmodulationtogethersuggestthatthisphaseprimarily functionsasasynchronizedsignalthatmarkstheinitiationofthetrial’s integrationprocess.

Followingthisinitialphase,thesecondphaseischaracterized by a slower, heterogeneous decay of activity within the PyrUp population.WefoundthatthedecayratesofPyrUpneuronswere not random; neurons with faster decay rates tended to exhibit higheractivitychangesaroundrunonset(higherRvalues,Fig.4k,i) andhadlowerbaselinefiringbeforerunonset(correlationbetween FR anddecaytimeconstant:r=0.26,p=1.18e-41).Asimilarcod- bef ingmechanismwasobservedinPyrDownneurons:thosewithfaster rise rates leading to the reward tended to have lower R values (Fig.4m,n).

Totestthefunctionalrelevanceofthesetwo-phasedynamics,we analyzed neuronal activity during the passive task that does not requiretime/distanceintegration.Theresultsprovidedseverallinesof evidence supporting our proposed model. First, there was a differ- ential change in the proportion of PyrUp and PyrDown neurons (SupplementaryFig.14a,b).WhilethepercentageofPyrUpneurons remainedsimilarbetweentasks(160/423neurons,37.8%inpassivevs. 37.2% in PI), the percentage of PyrDown neurons was significantly reduced inthe passive task(47/423,11.1%in passive vs.19.4% in PI; Supplementary Fig. 14c, d). This change aligns with the significant reductioninpredictivelickingobservedinthepassivetask.

In contrast, although the proportion of PyrUp neurons was similar,theirdynamicsweresignificantlyalteredinthepassivetask, particularly in the second phase. PyrUp neurons exhibited sig- nificantlylongerdecaytimeconstants(SupplementaryFig.14g),and theirdecayrateswerenolongersignificantlycorrelatedwithchanges infiringresponsesatrunonset(SupplementaryFig.14e,f),thereby losingtheheterogeneityobservedinthePItask.Thisdifferencein decaytimeconstantpersistedwhenweanalyzedPItaskrecordings withmatchingspeedtothepassivetask(SupplementaryFig.2h,ifor speed and licking profiles after speed matching; R vs. decay time constant: correlation r=0.45, p=3.4e-24 (t-statistics); Tau: PI: 2.94±0.06s, Passive: 3.32±0.11s, p=1.9e−03). This result further supports that the observed PyrUp dynamics in the PI task do not merelyreflectlocomotion.Conversely,therisetimeconstantsofthe smallnumberofPyrDownneuronsremainedlargelyintactandwere significantly correlated with their response changes around run onset,similartothePItask(Rvs.decaytimeconstant:correlation r=−0.62, p=3.0e−06; Tau: PI: 2.30±0.07s, Passive: 3.40±0.59s, p=0.26).In addition,PyrUpneurons showed a significant delayin peak time in the passive task (Supplementary Fig. 14h), while Pyr- Down neurons’ trough time remained similar (Peak time: PI: 1.49±0.05s, Passive: 1.67±0.10s, p=0.14). The loss of hetero- geneous ramping and the delayed peak time in the absence of an integration demand provide evidence that PyrUp dynamics are a feature of the time/distance integration process. The differential changesinPyrUp/PyrDowndynamicsduringthepassivetasksuggest thatthesesubpopulationsmayplaydistinctfunctionalrolesduring time/distanceintegration.

Insummary,PyrUpandPyrDownneuronsexhibitasynchronized start response followed by activity increases or decreases with het- erogeneous, neuron-specific rates. This synchronized “start” may establish a reference point for subsequent time/distance encoding. Thiscodingmechanismisdistinctfromthesequential,point-by-point encodingcharacteristicofneuronswithIGFs.

Somatostatin-positiveinterneuronsdisplayslowramping activityaroundrunonset

To reveal the circuit-level mechanisms underlying PyrUp/PyrDown responses,wefirstexaminedlocalsomatostatin-positive(SST)inhibi- tory interneurons. These interneurons coordinate interactions betweenCA3andtheentorhinalcortexinputstoCA1pyramidalneu- rons,primarilybytargetingtheirdendrites13,18,19.

To identify SST interneurons, we used optogenetic tagging by transfecting the CA1 SST interneurons with channelrhodopsin-2 (ChR2)inSST-IRES-Creanimals(Fig.5aandSupplementaryFig.15b; 3animals).Meanwhile,weclassifiedputativeinhibitoryneurons15by their spike properties and relative locations to the pyramidal layer center13(SupplementaryFig.15aforclusteringoverviewandSupple- mentary Fig. 15c for spike properties of an example putative SST interneuron; see Methods). We then identified a cluster that best overlapped with the tagged neurons (12/24 tagged neurons, see Methods), exhibiting characteristics consistent with SST-expressing oriens-lacunosum moleculare (OLM) cells13 (Supplementary Fig.15d,e).

Withinthisidentifiedcluster,amajorityofneurons(55/95neu- rons, Fig. 5d, e), including half of the tagged cells (6/12 neurons, Fig. 5b, c), showed an increase in activity at run onset that subse- quentlydecayedastherewardapproachedinthePItask.Wetermed theseneurons“SST-Upneurons”.Inbadtrials,theSST-Upresponseat runonsetwasweakened,despitenochangeinthemeanfiringrate (Fig.5e;meanFR:goodtrials:17.52±1.42Hz,badtrials:17.84±4.19Hz, p=0.95),suggestingacorrelationwiththeaccurateestimationoftime ordistance.

PreviousevidencesuggeststhatOLMinterneuronsdirectlyinhibit entorhinal cortex inputs to CA1 pyramidal neurons, while simultaneously providing indirect disinhibition to their CA3 inputs through other inhibitory interneurons14,16,17 (Fig. 5f). We thus hypo- thesizethattheperiodduringwhichSST-Upneuronsdisplayedele- vatedfiringafterrunonsetlikelyfavorstheinfluenceofCA3inputs.

InactivatingSSTinterneuronsafterrunonsetimpairsPyrUp response,IGSs,andtaskperformance

TodeterminehowSSTactivityinfluencesPyrUp/PyrDownresponses, we utilized soma-targeted anion-conducting channelrhodopsins (stGtACR2) to hyperpolarize SST interneurons34. We recorded the neuronalactivityunilaterallyfromdorsalCA1usinganoptrode(Fig.5g top)whileperformingclosed-loopinactivationofSSTinterneuronsfor 3secondsimmediatelyafterrunonset(Fig.5gbottom;seeMethods). ThismanipulationeffectivelysilencedsomeputativeSSTcells(Sup- plementaryFig.15f,g).

Silencing SST interneurons significantly reduced the PyrUp responseafterrunonset(Fig.5h,i;6animals,24recordings,608/1721 neurons),butdidnotsignificantlyalterthePyrDownresponse(Fig.5j, k;359/1721neurons).

Furthermore,IGFneuronsshowedasignificantdecreaseintheir activitycorrelationwithcontroltrialswithoutachangeinoverallfiring rate(SupplementaryFig.15h,i;160/1721neurons;meanactivitycorr.: control: 0.22±0.0073, stimulation: 0.19±0.0071, p=0.0029; mean FR:control:1.99±0.11Hz,stimulation:1.97±0.12Hz,p=0.44).These inactivation-induced impairments on the PyrUp response and IGSs resembledtheresultsinbadtrials(Fig.3c,g).

To assess behavioral consequences, we conducted a separate experiment in which we silenced SST interneurons bilaterally for 3secondsstartingatrunonset,inadifferentcohortofanimals(Fig.5l; seeMethods).Inthestimulationsessions,animalsexhibitedsignificant impairment in task performance. Specifically, they showed a sig- nificantincreaseinearlylicksbefore100cm(Fig.5m,nleft;6animals), anincreaseinpredictivelicksbetween100-140cm(Fig.5nright),anda decreaseinrewardedtrials(Fig.5p).However,themeanrunningspeed didnotchangesignificantly(Fig.5o),andthespeedprofileshowed only a slight, brief reduction between 2-3seconds (Supplementary Fig.15j–l),indicatinglargelyintactlocomotion.Controlexperiments confirmedthattheseeffectswerenotduetolaserlightalone(Sup- plementaryFig.18f–j,p–t;seeMethods).

Overall, these findings demonstrate that elevated SST activity afterrunonsetisnecessaryforproducingthePyrUpresponseandIGSs (Fig.5f).ReducingSSTactivityimpairsthePyrUpresponseandIGSs, therebydisruptingtimeordistanceintegration.

InactivatingSSTinterneuronsclosetorewarddoesnotaffect PyrUpresponseortaskperformance

As SST activity declined near the reward, it is plausible that CA3 inputs to PyrUp neurons are gradually blocked (Fig. 5f). We thus hypothesize that SST interneurons may establish a critical time window,spanningfromrunonsettonearthereward,thatfacilitates the PyrUp response. Supporting this hypothesis, silencing SST interneurons for 3seconds starting at 120cm (Fig. 6a) had no sig- nificanteffectonPyrUpneuronsorPyrDownneurons(Fig.6b–e;6 animals, 18 recordings), and only a minorimpactonIGSs (Supple- mentary Fig. 16a–c; 161/1516 neurons; mean activity corr.: control: 0.23±0.0074, stimulation: 0.21±0.0074, p=0.021; mean FR: con- trol:1.71±0.088Hz,stimulation:1.71±0.099Hz,p=0.52).

Similarly,whenwebilaterallyinactivatedSSTinterneuronsafter 120cm, using the same cohortof animals as in Fig. 5l,the animals’ behavior did not alter (Fig. 6f–j). Therefore, SST interneurons are necessaryforaccuratetimeordistanceintegration,primarilywithin the time window from run onset to when animals approach the rewardzone.

To reconcile our finding that SST inactivation after run onset impairedIGSswithpreviousstudiesthatreportednocleardisruption in place field sequence in cue-rich environments19, we introduced visualcuesduringthecue-constantsegmentofthePItask(Fig.6k).In thiscue-richcontext,animalsmaintainedsimilarrunningspeedand licking profiles (Supplementary Fig. 16g, h), and PyrUp/PyrDown responsespersisted(Fig.6l–o),suggestingthattimeordistanceinte- gration remains a default strategy, supplemented by external cue guidance.WethenrepeatedSSTinactivationafterrunonset.Despitea shutdown of some putative SST cells, we observed no significant changesinneuronalsequencesorthePyrUpresponse(Fig.6l–oand SupplementaryFig.16d–f;3animals,9recordings;meanactivitycorr.: control:0.24±0.015,stimulation:0.20±0.014,p=0.062).Ourresults indicatethatSSTactivityisrequiredtosustainPyrUpresponsesdriven by internal time or distance estimation, but not those evoked by externalcues.

Parvalbumin-positivecellsdisplayrampingactivityaround runonset

Because SST inactivation did not significantly affect the PyrDown neurons,we asked whether parvalbumin-positive (PV) interneurons, whichprimarilytargettheperisomaticregionandregulatethespike timing and synchrony of pyramidal neurons35–37, may play a role in controllingtheshutdownofPyrDownneuronsatrunonset.

To identify PV cells, we used optogenetic tagging by trans- fectingCA1PVinterneuronswithChR2inPV-Creanimals(Fig.7aand Supplementary17a;7animals).Alargeproportionofthesetagged neurons overlapped with one putative inhibitory neuron cluster located primarily in the pyramidal layer (13/30 tagged neurons; Supplementary Fig. 15a and 17b (spike properties of an example putative PV interneuron)). Neurons within this cluster exhibited characteristics consistent with PV basket cells (Supplementary Fig.17c,d)13.

Withinthisidentifiedcluster,amajorityofneurons(75/114neu- rons,Fig. 7d, e), including most of the tagged cells(10/13 neurons, Fig.7b,c),exhibitedarapidincreaseinactivityaroundrunonset.We termedthese“PV-Upneurons”.Inbadtrials,thePV-Upresponsewas weakenedwithoutachangeinmeanfiringrate(Fig.7e;meanFR:good trials:20.16±1.29Hz,badtrials:18.27±2.15Hz,p=0.47).

WefurtherinvestigatedthetimingofthePV-Upresponserelative toPyrUp/PyrDownresponses.BothPV-UpandPyrUpneuronsshowed concurrentincreasesinnormalizedfiringratesaroundrunonset,with PV-Up neurons initiating their activity earlier than PyrUp neurons (SupplementaryFig.17eleft;timetoreach90%ofmaximumfiringrate within [−1 1.5]s window (t ): PyrUp: 0.77±0.01s, PV-Up: 90% 0.50±0.04s,p=7.53e-09).Incontrast,thePyrDownresponsestarted todecreaseonlyaftertheinitialriseofPV-Upresponseandreachedits minimumafterthepeakofPV-Upresponse(SupplementaryFig.17e right; t : PyrDown: 0.60±0.01s, p=6.62e-04 compared with PV- 90% Up).TheseresultsimplythatPV-Upneuronsmaypreferentiallyinhibit PyrDownneurons.

Parvalbumin-positiveinterneuronsarenecessarytostarttime/ distancecountingatrunonset

TodetermineifPVinterneuronspredominantlyevokeinhibitionon PyrDownneurons,weusedstGtACR2tosilenceCA1PVinterneurons immediatelyafterrunonsetfor3seconds(Fig.7g;seeMethods)34.This manipulation effectively silenced some putative PV cells (Supple- mentaryFig.17f,g).

Silencing PV interneurons caused a significant elevation in the PyrDownresponseafterrunonset(Fig.7j,k;3animals,6recordings, 108/528neurons),withaneffectcomparabletowhatwasobservedin badtrials(Fig.3i).Yet,neitherthePyrUpresponse(Fig.7h,i;205/528 neurons)norIGSs(SupplementaryFig.17h,i;39/528neurons;mean activitycorr.:control:0.27±0.019,stimulation:0.28±0.019,p=0.79; meanFR:control:1.96±0.18Hz,stimulation:2.23±0.23Hz,p=0.33) showedsignificantchanges.

Toassessbehavioralconsequences,wesilencedPVinterneurons bilaterallyatrunonsetinaseparatecohortofanimals(Fig.7l).Inthe stimulation sessions, animals exhibited an impairment mostly con- fined to the period shortly afterrun onset.Specifically,there wasa significantincreaseinearlylickswithin100cm(Fig.7m,nleft),butno changesbeyond100cm(Fig.7nright).Thepercentageofrewarded trials (Fig. 7p) and mean running speed (Fig. 7o) did not change. Controlexperimentsconfirmedthattheeffectswerenotduetolaser lightalone(SupplementaryFig.18a–e,k–o;seeMethods).

The timing of the behavioral impairment, which occursshortly after run onset, implies that the animals cannot accurately initiate integration. Thus, we posit that PV interneurons are necessary for shuttingdownPyrDownneuronsatrunonset(Fig.7f)andinitiating integrationovertimeordistanceatthebeginningofeachtrial.

Methods
Experimentalmodelandsubjectdetails

Mice. This study was based on both male and female mice (age > 8 weeks). Male mice were preferentially used in the running tasks becausetheywerefoundtoexhibitmoreconsistentrunningbehavior. Weusedfourmouselines:C57Bl/6J(JAX#000664),PV-IRES-Cre(JAX #017320),SST-IRES-Cre(JAX#013044)57,andAi14(JAX#007914)58.All procedureswereinaccordancewithprotocolsapprovedbytheInsti- tutionalAnimalCareandUseCommitteeatMaxPlanckFloridaInsti- tuteofNeuroscience.Micewerehousedina12:12reverselight:dark cycleandbehaviorallytestedduringthedarkphase.

Methoddetails

Virtual reality setups. For the virtual reality (VR) setups, a small treadmill(JaneliaDesign)waspositionedinfrontoftwovisualdisplays, suchthatafterhead-fixation,theeyesalignedwiththetwovisualdis- plays.CustomizedsoftwarewaswrittenusingPsychtoolboxtodisplay thevirtualenvironment.AnArduinocontrolboardwasusedtocontrol whentodisplaythevisualstimuli.Forthesmalltreadmill,theanimal’s speedwasmeasuredusinganencoderattachedtothebackwheelaxis. A microprocessor-based (Arduino) behavioral control system (the miniBCS board, designed atJanelia) interfaced with a MATLAB gra- phicaluserinterfacecontrolledthetrialstructure,thewatervalve,and theencoder.Aseparate lickportdetector(designedatJanelia)was usedtoconvertatouchonametallickportintoadigitalpulseandto sendtheinformationtotheminiBCSboard.Inanotherversionofthe VR setup,insteadof using two monitors, a curved screen wasused wherethevirtualrealityenvironmentwasprojectedontothecurved screenusingaprojector(AKASOMiniProjector,adesignkindlyshared byChristopherHarvey’slabatHarvardUniversity).Inthiscase,cus- tomized softwarewrittenwithUnitywasusedtodisplaythevirtual environment.

Inaddition,aseparatemicroprocessor(Arduino)interfacedwitha customizedMATLABgraphicaluserinterfacewasusedtooperatethe laser or laser diode on-off for the optogenetic perturbation experi- ments and to control the closed-loop manipulation of PV and SST interneurons.Behavioraldata weremonitored andrecordedaswell usingMATLABfunctions.

Behavioraltraining

Beforeanysurgerywasperformed,runningwheelswereaddedtothe homecages.At3–5daysaftertheheadbarorfiberimplantation,mice wereplacedonwaterrestriction(typically0.8-1mlperday).Aftereach trainingorrecordingsession,miceweresupplementedwithadditional watertoensurethesameamountofwaterintakeperday.Afterhabi- tuatingtheanimalstothetreadmillforatleast1-2days,animalswere trainedtorunhead-fixedonthetreadmill.

Inthepassivetask,thescreenswerekeptgraythroughouteach recording session. After the animal ran for 180cm while this visual stimulusstayedconstant,awaterrewardwasautomaticallydelivered. Afterthat,aninter-trial-interval(ITI)started.Thenexttrialstartedat leastafter0.5sandonlywhentheanimal’sspeedhadexceeded30cm/ sformorethan0.3sandthetimesincethelastlickhadexceeded0.3s. Therefore,therewasarandomITIofatleast0.5suntilthenexttrial started.ThisrandomITIwasdesignedtointroducevariabilityintrial initiation.Thisapproachaimedtodiscourageanimalsfromrelyingon afixeddistanceortemporalstructuretoanticipaterewarddelivery.It tookabout1-2weeksfortheanimalstorunsmoothlyonatreadmill andtoperformthistask.

InthePI task, a trial started with a visualgrating stimulus that lastedeither0.5sor1s,andstayedthesamewithineachsession.After that,agray-coloredstimulusturnedon.Theanimalwasrequiredto runforafixeddistance(180cm)whilethisvisualstimulusstayed constant,andwasthenrequiredtolickwithina40cm(insomeses- sions,80cm)unmarkedrewardzonetoreceiveawaterreward.The firstlickintherewardzonetriggeredareward,andsimultaneously,the screensturnedblackforeither0.5sor1s(keptconstantwithineach session).Ifnolick occurred in the reward zone,the screens turned blacktosignifytheerrorfor0.5-2s(keptconstantwithineachsession). Ittookabout2weeksfortheanimalstolearnthistask.

Intheimmobiletask,theanimalsatwithinaplastictube32.Each trialstartedwitha1svisualgratingstimulus,whichwasthenfollowed byagray-coloredstimulusthatlasted4s.Afterthat,therewasareward zonelasting2s.Iftheanimallickedwithintherewardzone,adropof waterrewardwastriggered,andthescreensturnedblackforarandom timebetween0.5-2.5s.Iftheanimalfailedtolickwithinthereward zone,thescreensturnedblackforarandomtimebetween0.5-2.5s.To traintheanimaltoperformthistask,theanimalwasfirsthabituatedto stayinthetube,andthenheadfixationwasgraduallyapplied.Initially, theanimalwasexposedtothecompletetrialstructurewitha2sdelay period. As mice learned to concentrate licking towards the reward zone,thedelayperiodwasgraduallyincreasedin0.5sincrementsuntil thefinal4sdelaywasreached.Thistasktookabout2weekstotrain.

Virusinjection,headbar,cannulaandfiberimplantation

Adultmice(2-4monthsofage)underwentasepticstereotaxicsurgery toimplantacustomlightweight3Dprintedheadbarunderisoflurane anesthesia (2–3% for induction, 1–1.5% for maintenance). Buprenor- phineSRLAB(0.5mg/kg,SC)orbuprenorphine(0.1mg/kg,SC),and MeloxicamSR(5mg/kg,SC)wereadministeredimmediatelyafterthe surgery.Foracuteextracellularrecordings,aftertrainingtoperform behavior tasks, craniotomies were performed, which were centered aroundanteroposterior2.1mmfrombregma,mediolateral+/−1.7mm fromthemidlinetotargetdorsalCA1region.Meanwhile,agroundwire was attached to the headbar, and a thin layer of silver paint (GC ELECTRONICS 22-0023-0000) was applied to the surface of the headbarfornoisereductionduringtherecording.Theskullwascov- ered using KwikSil (World Precision Instruments) and was only removedduringrecordings.

For extracellular recordings with optogenetic manipulation of interneurons,AAV1_hSyn1-SIO-stGtACR2-FusionRed(Addgene105677, 2.1e+12after10folddilution)wasusedforoptogeneticinactivation, and AAV5-EF1a-double-floxed-hChR2(H134R)-mCherry-WPRE-hGHpA (Addgene20297,titer1.4e+13)wasusedforoptogeneticactivation. 50nlvirusperhemispherewasinjectedbilaterallyintheCA1regionof PV-IRES-Creand SST-IRES-Cre mice. The injection wasdone at least 3daysbeforetheheadbarimplantation,andatleast3weeksbeforethe recording. The following coordinates were used for viral injections: 2.1mmfrombregma,mediolateral+/−1.7mmfromthemidline,and 1.24mm dorsoventral from the brain surface. The injection system comprised a pulled glass pipette (broken and beveled to 15-20 μm insidediameter;Drummond,3-000-203-G/X),backfilledwithmineral oil(Sigma).Afittedplungerwasinsertedintothepipetteandadvanced todisplacethecontentsusingamanipulator(Drummond,NanojectII orNanojectIII).Retractionoftheplungerwasusedtoloadthepipette with the virus. The injection pipette was positioned onto a Kopf manipulator.

For optogenetic experiments without extracellular recordings, the viral injection procedure was the same as described in the last paragraph.Atleast3daysaftertheviralinjection,opticalfibers(core diameterof200um)werechronicallyimplantedbilaterallytotarget theCA1regionusingthefollowingcoordinates:2.1mmfrombregma, mediolateral +/−1.7mm from the midline, and 0.9mm dorsoventral from the brain surface.

Formuscimolinfusionexperiments,guidecannulas(26Gauge) wereimplantedbilaterallyabovetheCA1region(coordinates:2.1mm from bregma, mediolateral ±1.7mm from the midline and 1.1mm dorsoventral from the brain surface) during the head bar surgery. Dummycannulas(33Gauge)ofthesamelengthastheguidecannulas wereinsertedintotheguidecannulas.

Optogeneticswithoutextracellularrecordings

Foreachoptogeneticsession,acontrolsession(~40trials)wasper- formed.Itwasthenfollowedbyastimulationsession(~100trialsin total),wherethephotostimulationwasdeployedeverythirdtrial.After that,therewasapost-stimulationcontrolsession(~40trials).Topre- vent mice from distinguishing photostimulation trials from control trials through stimulation light, a masking blue light (470nm LEDs (Thorlab)) was on throughout the sessions. For both stGtACR2 and ChR2,weuseda473nmlaser(NingboLaseverInc.).Thelaserpower usedwas<=5mW.Duringthestimulation,100Hzlightpulseswere usedtoreducetheheatingeffectofaconstantlaserlight.Theduty cyclewasvariedtoadjustthelightpower.

ForinterneuroninactivationusingstGtACR2,thestimulationlas- lasted3seconds.Thestimulationcanbetriggeredat(1)therunningonset or(2)120cmlocationduringthecue-constantsegment.Therunonset stimulation wastriggered atthe firsttime point wherethe animal’s speedexceeded10cm/sforatleast200msecafterthetrialstart.The stimulation trial types were randomly selected for each stimulation session.Thebehavioranalysiswasperformedusingallthetrialsduring thestimulationsession.Onlyconsideringthestimulatedtrialsledto similarresults.

Asacontrol,insomerecordingsessions,theoptogeneticspatch cableswereonlylooselymatedwiththeimplantedfiberopticcannu- las, preventing light from propagating into CA1. In addition to this withinanimalcontrol,wealsoperformedcontrolexperimentswhere interneurons were infected with CAG-FLEX-tdTomato(AAV9) (Addgene,titer1.90E+12afterdilutiontoapproximateconcentration fortheinactivationexperiments).

Acuteextracellularelectrophysiology

Two days before the recording, the animal was acclimated to the recording condition: including turning on the microscope light, removingKwiksilthatcoveredtheskull,andaddingsalinetokeepthe craniotomywet.Onthedaybeforetherecording,acraniotomywas opened.Duringelectrophysiologicalrecordings,a64-channelsilicon probe(neuronexus,buzsaki64sp)wasslowlyloweredintothehippo- campal CA1 region. Data from all channels were filtered (0.3Hz to 10kHz),amplified(gain=400),andcontinuouslysampledat20kHz using the Amplipex system (16-bit resolution)59. Time stamps of behavioraleventsandelectrophysiologicalrecordingdataweresyn- chronized,recorded,andstoredonacomputer.

For acute extracellular recordings, craniotomies were centered aroundthedorsalCA1regionofthehippocampususingstereotaxic coordinates(AP:−2.1mmfromBregma;ML:±1.7mmfrommidline). Beforestartingarecording,probeswereloweredveryslowlyinto the brain under electrophysiological monitoring. The typical target depth from the brain surface to reach the CA1 pyramidal layer is approximately 0.95 to 1.40mm. However, this can vary slightly betweenanimals.Duringprobeadvancement,wecontinuouslymon- itored onlineelectrophysiologicalsignals,includinglocalfieldpoten- tials (LFPs) and multi-unit activity (MUA), across the different recordingchannelsoftheprobe.

TheCA1pyramidallayer(StratumPyramidale,SP)wasidentified based on a convergence of established electrophysiological land- marks,consistentwithcriteriadescribedinpreviousliterature(e.g., Mizuseki et al. (Nature Neuroscience, 2011)24): (1) Prominent theta oscillations: Strong and coherent theta-band activity (typically 6- 10Hz)intheLFP,particularlyevidentduringlocomotion.(2)Sharp- wave ripples (SWRs): The presence of high-frequency rippleoscilla- tions (100-250Hz) superimposed on larger sharp waves in the LFP, whicharemostprominentinCA1SPandareahallmarkofthislayer, especiallyduringquietwakefulnessorimmobility.(3)IncreasedMUA andspikebursts:AnoticeableincreaseinthedensityofMUAandthe occurrenceofcomplexspikebursts,characteristicofpyramidalneu- ronfiring,astheprobecontactstraversedSP.(4)Polarityinversionof sharp-waves/ripples:Akeyindicatoristhephaseorpolarityinversion ofsharp-waves(andsometimesthetawaves)acrosstheradialaxisof CA1.Welookedforthisinversionpatternacrosstheshanks/sitesofour high-densityprobes.Thesefeatureswereusedcollectivelytodeter- minethattheprobewaspositionedintheCA1pyramidalcelllayer.

Forextracellularrecordingswithoptogenetics,a64-channelsili- con probe with fibers mounted on the shanks (neuronexus, buzsa- ki64sp-OA64LP) was used. Photostimulation followed the same protocol as described in the optogenetics section above. The light source was a self-constructed laser diode array (6 diodes, 450nm, osram-os,PL450B).Eachdiodewascoupledtooneshankontheprobe. 2to5diodeswerecoactivatedduringthestimulation.Thestimulation laserpowerwas<1.2mWatthetipoftheprobe.

Foroptogenetictaggingexperiments,interneuronsweretagged using1mspulsesofbluelight.Spiketimesaroundeachpulsewere binnedwitharesolutionof1msandthesignificanceoflightresponses ina5mswindowafterthelightpulsewasassessedusingthestimulus- associated spike latency test (SALT)60. Neurons with a p<0.01 that respondedtoatleast40%oflightpulseswereidentifiedasPV-orSST- positive.30–40taggingpulseswereappliedperrecording.

Muscimolinfusion

Muscimolhydrobromide(Tocris-0289)wasdissolvedin0.9%saline. Thecontrolsolutionwassaline.Themuscimolinjectionswerecarried out as follows. First, an injection cannula was connected to a 10-μl Hamilton syringe through Tygon tubing (Tygon 720993) and filled with muscimol (1mgml−1) or saline (0.9%). Then the syringe was mounted into a microinjection pump (UMP3 with SYS-Micro4 con- troller).Atthebeginningoftheinjectionprocedure,thedummycan- nula was removed from the guide cannula and replaced by the injectioncannula(33Gauge),whichextended0.5mmdeeperintothe brainthantheguideanddummycannulae.Then300–500nlofmus- cimol or saline was slowly injected (100nl/min) into CA1 and the injectioncannulawasleftinplaceforanother3minaftertheinfusion wascompleted.Next,thedummycannulathatwascleanedwith alcohol and dipped insterilemineraloilwas inserted back intothe guidecannula.Duringtheinfusion,animalsperformedthePItaskwith waterautomaticallydeliveredattheendofeachtrial.Animalsdidnot showsignsofstressordiscomfortduringtheprocedure.Theanimals werereleasedfromthesetupaftertheinfusion,and∼30minafterthe completion of the infusion, animals were placed back onto the VR setup.Atypicalmuscimolinfusionrecordingwascomposedofone controlsessionbeforetheinfusion,andmultiplerecordingsessionsat 30min,1h,2haftertheinfusion.Sessionsafterinfusionwerecom- binedas“Musc”sessionsintheanalysis.

Spikesorting

Toidentifyspikes,weperformedoff-linespikesortingontherecorded filesfollowingpublishedmethodsusingKlusters61andKilosort62.Units werefurtherselectedbasedonthepercentageofspikesthatviolated refractoryperiodandthemahalanobisdistancefromotherunits.

Histology

MicewereperfusedtranscardiallywithPBSfollowedby4%PFA.Brains were post-fixed overnight and transferred to PBS before sectioning using a vibratome. Coronal 50 μm free-floating sections were pro- cessedusingstandardfluorescentimmunohistochemicaltechniques. PVimmunoreactivity(Swant,GP72;1:2000,guineapig).Theprimary antibody was visualized by Alexa Fluor 488 AffiniPureDonkey Anti- Guinea Pig IgG (H+L) (1:1,000, Jackson ImmunoResearch Labora- tories,706-545-148).SSTimmunoreactivity(Millipore,MAB354;1:250, rat).TheprimaryantibodywasvisualizedbyAlexaFluor488AffiniPure Donkey Anti-Rat IgG (H+L) (1:1,000, Jackson ImmunoResearch Labora- tories,712-545-153).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Heldman and colleagues present a compelling study that uses dense electrophysiological recordings in mice to investigate CA1 neural activity in a novel path-integration task adapted for a head-fixed preparation. In this task, mice are trained to run on a treadmill surrounded by screens, waiting for a cue to appear before running approximately 180 cm and licking to receive an otherwise hidden reward. The authors uncover a previously undescribed activity pattern in CA1 pyramidal neurons, where one subset increases firing following cue presentation at the onset of the run toward the goal (PyrUp), while another decreases (PyrDown). Importantly, they demonstrate that this activity pattern is not merely a reflection of task structure but instead correlates with cognitive demand, as evidenced by differences in firing rates between good and bad trials. They propose a mechanistic model in which PyrUp neurons initiate an integrative process (tracking distance or time), setting the stage for PyrDown neurons to guide accurate estimation of the goal location. Through elegant optogenetic manipulations targeting distinct interneuron subtypes (PV and SST), the authors establish causal relationships between these activity patterns and behavioral performance.\nOverall, this study is a tour de force. It integrates large-scale neural recordings, a cognitive task, and specific causal manipulations to reveal a previously uncharacterized circuit mechanism underlying distance estimation in path integration. The dataset is extensive and is combined with rigorous analyses and well-controlled experiments. Every potential logical gap is thoughtfully addressed, either experimentally or analytically.\nIt is evident that this manuscript has undergone substantial revisions and incorporates an impressive amount of additional data since its initial conception. I have no further data requests and only a minor analytical suggestion that could help clarify a small aspect of one result. The depth of the study (its strength) also means that the manuscript is dense, and I have a few minor suggestions to enhance clarity and readability. But these are primarily stylistic and should not impact the core findings. I have no major concerns regarding how data was collected processed and analyzed.\nComments:\n1. The authors have examined the anatomical distribution of PyrUp and PyrDown neurons (deep vs. superficial) as reported in Figure S2F, but I could not find a methods description of how depth was estimated. Was this based on CSD, SWR power, or another approach? A “blunt” depth estimation using these metrics may not always yield reliable results and I’d like to suggest using a relative anatomical estimation among neurons recorded from the same shanks, relying on a spike amplitude metric (we describe this approach in Geiller et al., 2017, Nature Communications). Maybe the authors will find it useful. This pairwise comparison method could reveal more subtle differences, given the small size of the CA1 cell layer in mice (~50 µm).\n2. This is subjective, but I suggest reducing the amount of information in the main figures, as the key message of each figure feels somewhat diluted. I found it difficult to parse the primary takeaways from individual panels. For example, the behavioral impact of interneuron manipulations is a critical finding but feels somewhat “buried” within the figures. Of course, the authors should feel free to disregard this suggestion if they strongly prefer their current structure.\n3. This is more a point of curiosity than a comment: in Figure 5M, the authors introduce cues on the track to shift the task from path integration to a goal-oriented behavior, yet they still observe PyrUp/PyrDown responses. Could the authors provide some insight into why this occurs? One possibility is that distance integration remains a default strategy because that is what the animals were initially trained to do?\n4. Regarding Figure 5N-S, have the authors checked whether the place cells detected in this condition are also IGS cells when cues are removed? I am not sure someone has ever looked at that?\nI am confident that this paper will be well-received by the community, and I strongly recommend its publication in Nature Communications.", "reviewer_2": "The authors describe time-cell-like spiking sequences in the mouse CA1 as well as two populations of pyramidal neurons - one that is excited and one that is inhibited - during a cued path integration task in mice. Given that these groups exhibit their peak excitation/inhibition at the same time (of peak speed?) and that they have variable decay/rebound rates, time can be decoded during path integration. By optogenetically inactivating SST or PV interneurons in transgenic mice during this behaviour, they then find that SST-silencing supressed the first subpopulation and PV-silencing suppressed the second one.\nThe manuscript contains an impressive amount of work (electrophysiology, behavioural tests, analysis, closed-loop optogenetics) with extended analyses and clear, polished figures. Unfortunately, the results and conclusions are not particularly novel or convincing. The main issue stems from the task design where locomotion motifs make it very difficult to dissociate time from distance or exclude the strong possibility that neuronal activation is simply triggered by locomotion and does not necessarily time- or distance-integrate, blurring most conclusions.\nThe manuscript also suffers from lack of cohesion, making it feel like 2-3 small papers combined in one. E.g. Fig 1 is about distance dependence leading to a major conclusion that firing fields encode distance. Yet practically the rest of the paper is about time encoding and interneuronal circuits. Moreover, the analysis on firing fields and spiking sequences is mostly abandoned from Fig 3 onwards, and it is not even mentioned in the abstract.\nMajor points:\n• Internally generated spiking sequences, triggered by run onset and integrating distance is not a novel observation. They have been described before by Villette, Malvache et al Neuron 2015. The authors should discuss their Fig1-2 analyses in relation to those findings.\n• The biggest issue with the manuscript is that time and distance cannot be easily separated because locomotion patterns seem to be very stereotyped. Even though the authors go to some lengths to rule out that locomotion is driving their findings, it is not convincing. For example, PyrUp/Down neurons may simply be correlated/anti-correlated with locomotion, respectively. Since locomotion is stereotypical peaking at a more-or-less fixed timepoint after run-onset, they all peak or dip at that timepoint after run-onset. This (anti)correlation is better in “good” trials because a good locomotion pattern is required there (it is a criterion for selecting “good” trials). In this case, neurons that are either correlated or anticorrelated with locomotion would not constitute “two previously unknown functional subpopulations of CA1 pyramidal neurons”.\nRelated to this, running speed is only plotted against distance in Fig 1 and only against distance. It would be essential, to show locomotion over time, to match with the firing rate plots. It is possible that locomotion and rates strongly correlate.\n• Given these facts, claims like “IGS that encodes the moment-to-moment running distance” seem unconvincing, since throughout the manuscript it never becomes clear whether distance or time is being encoded. If anything, sequences seem clearer when plotted over time rather than distance, but this is not adequately explored.\n• Total lack of any fields in the passive task is interesting and quite surprising and needs further examination. This result may be driven by 2 issues:\n1) It may be biased by a specific threshold the authors used to determine when a cell has a significant field. Looking into the Methods, they use a long list of criteria for field detection “based on visual inspection”, resulting in only 4% cells having IGFs! One of them is: “minimum mean trial-by-trial Spearman correlation of the firing rate profile > 0.15”. Looking into Fig 2H, it seems that even IGF cells have an average correlation of just under 0.15 when aligning to the cue. Unless I am reading this wrong, it could be that the 0.15 criterion is just right to exclude all cells in the passive task. These cells may still yield more noisy fields than in the IP task, but a different threshold would probably reveal many of them still having a field.\n2) Another issue is how firing rates are aligned across trials. Based on Fig 1D, in the passive task mice run with more homogeneous speeds, whereas they accelerate more slowly in the PI task after each reward. This is probably because the authors use a variable ITI defined as follows: “The next trial started at least after 0.5s and until the animal’s speed has exceeded 30 cm/s for more than 0.3s and the time since the last lick has exceeded 0.3s.”. It is unclear why these criteria have to be met to start counting distance travelled by the mouse but in any case, mice are already running when the trial is initiated (meaning distance starts being counted). Without a fixed “landmark” in the passive task (like the cue in the IP task) and with mice already running, using distance as a metric to find IGFs in the passive task is not a fair comparison to the IP task. Distance from what? Later, in Fig 2G, they check for IGFs from run-onset, but it is not clear if that run onset is within the trial or includes run-onset during the ITI. Run-onset after reward licking is probably a better way to search for fields in the passive task.\n• Authors claim that spiking sequences in “bad” trials are more noisy. This could simply be due to undersampling (only 12% of trials are “bad”). IGFs are typically noisy, requiring averaging across many trials to see the field, so using only a small sample of trials could lead to blurrier fields. The authors should repeat their comparisons after subsampling the “good” trials (with multiple iterations) to match the “bad” trials.\n• The authors show that they can decode time based on firing rate decay rates of PyrUp/Down. But any time series with fixed temporal profile can be used for time decoding. It does not convincingly show that these cells encode time, rather than just encode locomotion (see comment further up). The authors claim that spontaneous runs don’t yield similar responses, but that is probably because spontaneous runs do not share the stereotyped run motifs of regular runs. They maybe short, jerky or slow crawls (the locomotion traces are not shown in Fig S3A-B after all). The locomotion-dependence is supported by the analysis in Fig S3C: when comparing only spontaneous runs with matched-speed to regular runs, the firing rates become much closer, further supporting that locomotion may be underlying these responses.\nIn fact, it is not clear how matched-speed is defined. In the Methods it is stated: “We found recordings with matching running speed between the passive and PI tasks, and identified 4 animals (10 recordings in total) where the mean speed did not differ from the passive task recordings”. What is the statistical criterion behind “did not differ”? Please clarify.\n• The immobile task is not very clear either. Are mice supposed to not lick during the delay or can they be constantly licking throughout the 4 sec? Lick rates are not shown. If it is the latter case, how do the authors know they time integrate? It is also unclear why the authors align firing rates by the last lick of previous trials, rather than cue onset or offset, which is a specific temporal anchor in a trial.\nIn the immobile task, the increase/decrease of firing in PyrUp/Down seems to happen during the delay period and may be related to timing or simply triggered by the cue, or even correlated with licking. In either case, it still doesn’t convincingly exclude that locomotion triggers UP/DOWN responses in the normal task.\n• The authors use a method for identifying non-tagged putative SSTs and PVs. Even though their metrics look similar to those from corresponding tagged cells, it is unclear what they look like for other cell types (e.g. pyramidal cells). Why not just analyze tagged cells? Is it a sampling issue?\n• Silencing SSTs reduces PyrUp firing. What does it do to locomotion patterns? The authors should include a locomotion comparison (not just average speed across the entire trial).\n• I found no details in Methods on how CA1 cells were selected. How much was each probe lowered into the brain? How did the authors determine which channels were situated in CA1?\nMinor points:\n• The Results are separated into sections whose message bleeds into each other, making it hard to follow the manuscript structure. For example, the first section ends with conclusion: “Thus, IGSs preferentially occur during the task requiring distance integration”. Half way through the 2nd section, results imply that “engaging in distance integration is necessary for generating IGFs”. These analyses could form a single section. The same holds for the Figures and supplemental figures. Often, a supplemental is related to multiple main figures and vise versa. This makes following the analyses difficult.\n• Repetition: “In the PI task, the run onset potentially marked the starting point of integration in the PI task”\n• Figures not appearing in order in the text. E.g. Fig 4D referenced earlier than 4A-C and similarly elsewhere.\n• “In addition to IGSs, CA1 pyramidal neurons generally exhibited improved trial-by-trial activity correlation when aligned to run onset (Figures S2H).” Fig S2H is probably a typo here as it doesn’t show that.", "reviewer_3": "In the current manuscript Heldman and colleagues train mice to perform a cued path integration task while recording sequential activity patterns in the hippocampal formation. I like the task. They find that the hippocampus generates fields anchored to particular distances/times from run onsets and that these internally generated fields (‘IGFs’) are not present when the animal runs down the same virtual environment with no visual cues (a passive version). They also observe two sub-populations of cells that either peak (PyrUp) or trough (PyrDown) in their mean firing activity following the start of the cue initiated run. In a series of optogenetic manipulations, they show that disruption of SST interneurons modulates the integrity of PyrUp ‘coding,’ while PV interneruons regulate the integrity of PyrDown responsive cells. This is an interesting story and many of the experimental results are quite compelling. That said, I have several concerns that should be addressed in a revised manuscript.\nThe tie between PyrUp and PyrDown cells and path integration computations was tenuous to me and I don’t feel like the connection between path integration, the cell response, and the behavior was ever super well explained. The authors should attempt to beef up this portion of the manuscript and potentially include a theoretical/computational model that would explain why this form of tuning would be useful for this computation.\nIt is not entirely clear why these cells respond this way or how they differentiate from other ramping cell responses reported in the past. Since they are so strongly anchored to the run onset, a better analysis of the relationship between movement behaviors (e.g. acceleration) and the responsivity of these neurons is warranted. This includes a substantially more refined analysis of ‘good’ and ‘bad’ trials which I found to be a bit circular and lacking (there are significantly more criteria that one could use to assess task performance than the three main metrics included in this analysis).\nThe difference between the IGF response on passive versus active versions of the task is interesting, but many prior papers have shown robust place fields in animals navigating in darkness that are not actively required to perform path integration (i.e. are just rewarded on either side of the track or are free foraging in an open environment). The different here is perplexing and makes one wonder whether the primary results would hold in non-VR tasks. While I do not think it is appropriate to ask the authors to show that their results would hold in freely moving animals, I have to admit I find myself wondering why they used a task with head-fixation. At the very least, the authors should discuss the possibility of impoverished vestibular information on their main results.\nRelatedly I would like to see more classic metrics of place field integrity explored here. For example, is spatial information disrupted in the passive task or just trial to trial reliability?\nAn explanation of the statistical test for classifying PyrUp and PyrDown cells should be included in the main manuscript since these classifications are so critical to the story."}, "author_response": "We sincerely thank the editor and reviewers for their time and expertise. We appreciate the detailed and constructive comments and the opportunity to significantly enhance our manuscript. In response, we have undertaken substantial revisions to improve clarity, strengthen our conclusions, and incorporate extensive new analyses. We have now expanded the number of main figures from 6 to 7, and the number of supplementary figures from 5 to 15. Key improvements include:\n• Provided converging evidence for PyrUp/PyrDown responses reflecting internal time or distance integration processes, rather than simply locomotion:\n  o PyrUp/PyrDown neuronal activity is modulated by time/distance rather than simply by locomotion revealed by Generalized Linear Model (GLM) analyses.\n  o PyrUp/PyrDown dynamics differ from speed profiles, and their responses peak earlier than running speed.\n  o PyrUp/PyrDown dynamics remain similar between trials with high versus low running speed.\n  o PyrUp/PyrDown neurons exhibit context-dependent responses, responding significantly stronger at the onset of trial-start runs than spontaneous runs occurring during a trial (new analyses included).\n  o PyrUp/PyrDown activity patterns persist in an immobile timing task, which requires temporal integration without locomotion (new analyses included).\n• Provided more evidence that PyrUp/PyrDown responses correlated with behavioral performance:\n  o PyrUp neurons decay more slowly in trials with later first anticipatory licks, a behavioral proxy for the animal's internal estimate of elapsed time or distance.\n• Clarified PyrUp/PyrDown two-phase coding model: We propose a two-phase coding scheme. This model describes a synchronized initiation phase, providing a reference point for integration, followed by a distance/time encoding phase characterized by heterogeneous decay/ramping rates.\n• Strengthened analysis of internally generated fields (IGFs):\n  o We clarified the criteria for IGF detection and provided further analysis comparing IGF presence and trial-by-trial activity correlation in path integration versus passive tasks.\n  o We expanded the discussion on the absence of IGFs in passive tasks, emphasizing the combined impact of reduced sensory (including vestibular) input in head-fixed virtual reality settings and task demands.\n• Improved manuscript cohesion and readability: We restructured the manuscript to improve the narrative flow and simplified both the main and supplementary figures to enhance readability.\n• Improved contextualization and novelty: We expanded discussions and comparisons with prior studies to articulate the novelty of our findings, particularly regarding the presence and absence of the IGSs in our behavioral tasks and the functional relevance of two-phase dynamics of PyrUp/PyrDown populations.\n• Refined methodological details: We provided additional information on depth estimation for neuron localization, cell-type classification, and methods for various analyses.\nOnce again, we thank the reviewers for their thoughtful comments, which have prompted numerous new analyses that have led to a significantly improved manuscript. These revisions reinforce our conclusion that PyrUp and PyrDown neurons represent an internally driven coding scheme supporting distance or time integration.\nWe hope this revised and improved manuscript will now be suitable for publication in Nature Communications.\nPoint-by-point responses to reviewers are included below, with reviewer comments retained in black and our responses added in blue. A revised manuscript with key revisions highlighted in red is included with the resubmission."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all of my and the other reviewer's concerns. I have no other concerns and recommend publication.", "reviewer_2": "The authors went to great lengths to carefully address all points I raised, as best as possible and in great detail, within the confounds of their existing experiments and datasets. I appreciate all the additional extensive analyses and clarifications. I feel like the claims have been strengthened and the manuscript structure is much improved. I recommend the manuscript for publication.", "reviewer_3": "The authors have mainly addressed my concerns but some of the newly included analyses have raised additional issues/comments outlined below.\nI agree with many of the concerns raised by reviewer 2, in particular the concerns about novelty. Ramping responses have been reported in CA1 (Ning, Bladon, and Hasselmo). I do not feel particularly convinced by the inclusion of a theoretical two-phase model regarding the role of UP and DOWN neurons in path integration nor their classification as distinct classes of neurons. There is no manipulation to test their theory nor a computational model showing that it is useful for self-localization via path integration.\nI do not believe that the new GLM truly addresses concerns related to the separation of time, distance, and movement coding. These variables all tightly co-vary in the task setup and accordingly it is unclear if the GLM is appropriate from an analytical perspective. I feel only a manipulation of the relationship between reward delivery and distance traveled would truly disintangle things.\nRelatedly, what are the GLM results on the passive task?\nThe authors suggest that there is a ‘temporal dissociation’ between peak firing and speed that indicates a decoupling of the two. I am not convinced by this argument - several studies have shown neurons can have temporally offset relationships to behavioral/sensory variables. At the very least this should be addressed in the discussion.\nI still feel that the good vs bad trial analysis is circular (though I appreciate that the authors have addressed whether the sampling could explain differences).\n“Despite clear differences in running speed, the firing rates of both PyrUp neurons and PyrDown neurons 5 remained largely stable, with PyrUp activity stability evident within the first three seconds (Figures 4C-4F).”\nSpeed-modulated cells in the extended hippocampal formation often saturate at higher speeds (Gois et al. 2018; Hinman et al., 2016) with greatest speed modulation happening at lower speeds. The results here are consistent with this and I don’t find this to be an especially compelling argument against pyramidal up or down responses being correlated/anticorrelated responses to the initial acceleration on the track.\nI don’t understand the logic of the SRO analysis. Why doesn’t the animal need to path integrate from the random stopping point to perform the task?\nI remain confused by the immobile task results and I do not have a grasp on why IGFs would emerge in the immobile task but not in the passive task. This is especially confusing if IGFs in the immobile task were better aligned to the last lick of the previous trial which should be the case for the passive condition. What is the authors explanation?\n“These changes in the decay/rise rate indicated that the PyrUp/PyrDown dynamics adjust to the animal's internal estimate of when sufficient time or distance has elapsed.”\nAgain, I don’t understand why there wouldn’t be anticipatory responses similar to Up/Down in the passive task - the animal can still track time to reward after the gray screen appears right?\nMinor comments.\n“Good trials met the following criteria: animals came to a complete stop before trial run onset, allowing this onset to mark the start of integration; animals maintained largely uninterrupted running; and successfully obtained the reward (77.90±2.88% of all trials, see Methods). In contrast, bad trials failed to meet at least one criterion. Despite maintaining a mean running speed similar to good trials (Figure 3A, good trials: 48.12±0.84cm/s, bad trials: 45.20±0.84cm/s), bad trials resulted in a comparatively lower reward rate of 86.34 ± 4.36%” - The percentage reward rate seem off given the context of this sentence.\nWhat is the relationship between late/early lick trials and high/low speed? My guess is that early licks systematically occur with deceleration - both metrics of the animals confidence?\nPlease include the figure legends with the figures in future revisions. Line numbers would also be useful."}, "author_response": "We sincerely thank the editor and reviewers for their time and expertise. We appreciate the detailed and constructive comments and the opportunity to significantly enhance our manuscript. In response, we have undertaken substantial revisions to improve clarity, strengthen our conclusions, and incorporate new analyses. We have now expanded the number of supplementary figures from 15 to 18.\nOur main revisions address the third reviewer's central concerns as follows:\n1. On Novelty and the Classification of PyrUp/PyrDown Neurons: To address the concern about novelty, we have clarified our contributions beyond prior work. Furthermore, we now provide multiple lines of evidence that PyrUp and PyrDown neurons are functionally distinct classes, including their differential modulation by specific interneuron subtypes and the distinct changes of these two populations in the passive task.\n2. On Causal Evidence for the Two-Phase Model: We acknowledge the reviewer's primary concern regarding the lack of direct manipulation to test our model. To address this, we now summarize key results from two manipulation experiments: phase-specific optogenetic silencing and a task in which we varied the required travel distance. These results provide strong evidence for the model and help disentangle the encoding of an internal, time/distance integration from simple locomotor response.\n3. On Dissociation from Motor Variables: We have conducted a new analysis to test the speed modulation of PyrUp/PyrDown neurons. A time-resolved correlation analysis clarifies that the PyrUp/PyrDown neurons were only weakly speed-modulated, with a significant increase in modulation during the first second after run onset. This result further supports that PyrUp/PyrDown dynamics do not merely reflect locomotion.\n4. On Clarifying Control Analyses and Task Logic: We have clarified the logic behind our control conditions. We now explicitly detail why the task design of the passive task intentionally eliminated a reliable \"start\" signal. We also performed a new analysis with relaxed criteria for our \"good vs. bad trial\" comparison, demonstrating that our findings are not an artifact of circular definitions.\nIn summary, the revised manuscript is substantially improved thanks to the reviewer's feedback. By incorporating new analyses, we now provide a stronger foundation for the novelty of the opposing ramping populations, their functional distinction, and the role of the two-phase dynamics in time/distance integration. We have detailed these changes in the point-by-point responses that follow.\nWe hope this revised and improved manuscript will now be suitable for publication in Nature Communications."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The author's have addressed my concerns. I appreciate their hard work, both in the original body of work and in their efforts to address my final comments. I commend them on an excellent addition to the field."}, "author_response": ""}]

Decision letter