Population dynamics in parietal cortex during evidence accumulation for decision-making The ...

153 Pages·2016·14.9 MB·English

Population dynamics in parietal cortex during
evidence accumulation for decision-making
Citation
Morcos, Ari Simon. 2016. Population dynamics in parietal cortex during evidence accumulation
for decision-making. Doctoral dissertation, Harvard University, Graduate School of Arts &
Sciences.
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Populationdynamicsinparietalcortexduringevidenceaccumulationfordecision-making
Adissertationpresented
by
AriSimonMorcos
to
TheDivisionofMedicalSciences
inpartialfulfillmentoftherequirements
forthedegreeof
DoctorofPhilosophy
inthesubjectof
Neurobiology
HarvardUniversity
Cambridge,Massachusetts
April2016
©2016 AriSimonMorcos
Allrightsreserved.
DissertationAdvisor: Dr. ChristopherD.Harvey AriSimonMorcos
Population dynamics in parietal cortex during evidence accumulation
for decision-making
Abstract
Cortical circuits combine new inputs with ongoing activity during a variety of behaviors,
including evidence accumulation during decision-making. However, the neural circuit
mechanisms underlying how populations of neurons perform the computations necessary
for this process and the dynamics which govern how neuronal populations change from
moment-to-moment during evidence accumulation remain unclear. Here, we trained mice
to perform several novel virtual-navigation decision tasks, including one which requires
the accumulation of multiple, discrete evidence cues. As mice accumulated evidence, the
posterior parietal cortex (PPC) transitioned between distinguishable and largely
uncorrelated activity patterns, often involving mostly different sets of active neurons from
moment-to-moment. These activity patterns contained task-relevant information
distributed across the neuronal population. Because animals make decisions on single
trials, we chose to analyze these activity patterns on a trial-by-trial basis. As single trials
unfolded, each event — whether a new evidence cue or a behavioral choice — modified
the dynamics of the PPC for seconds, even across trials. These events did not change the
tonic activity of a specific set of neurons; rather, each event altered the probabilities that
govern how one activity pattern transitions to the next, constraining the possible future
patterns of activity. Thus, representations of ongoing events were influenced both by the
sequence of previous evidence cues within the current trial and by the outcome of the
previous trial, thereby generating multiple distinguishable activity patterns for the same
level of accumulated evidence. These observations suggest that evidence accumulation
iii
does not rely upon the explicit competition between groups of neurons (as would be
predicted by winner-take-all models), but instead reflects dynamical properties of the PPC
thatmayinstantiateaformofshort-termmemoryconsistentwithreservoircomputing.
iv
Table of contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 General introduction and background . . . . . . . . . . . . . . . . . . 1
1.1 Drift-diffusionmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Singleneuroncorrelatesofevidenceaccumulationfordecision-making . . . 4
1.3 Sensory,motor,orboth? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Heterogeneityacrossneuronsandvariabilityacrosstrials . . . . . . . . . . . 8
1.5 Transientorpersistentactivitydynamics? . . . . . . . . . . . . . . . . . . . 10
1.6 Neuraltrajectoriesandmethodsforanalyzinghigh-dimensionalneuraldata . 12
1.7 Neuralalgorithmsformemoryanddecision-making . . . . . . . . . . . . . 14
1.7.1 Winner-take-allmodels . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.2 Reservoircomputing . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.8 Methodsforrecordingfrommultipleneuronssimultaneously . . . . . . . . . 18
1.9 Avirtualrealityparadigmformousebehavior . . . . . . . . . . . . . . . . . 20
2 Development of novel virtual-navigation decision-making
behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Avirtualrealitysystemformousebehavior . . . . . . . . . . . . . . . . . . 23
2.2.1 Detaileddescription . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Fixedassociationevidenceaccumulationtask . . . . . . . . . . . . . . . . . 25
2.3.1 Taskdescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 Trainingprocedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
v
2.3.3 Behavioralcharacterization . . . . . . . . . . . . . . . . . . . . . . . 29
2.4 Delayed-match-to-sampletask . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.1 Taskdescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.2 Trainingprocedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.3 Behavioralcharacterization . . . . . . . . . . . . . . . . . . . . . . . 40
2.5 Delayed-match-to-sampleevidenceaccumulationtask . . . . . . . . . . . . 41
2.5.1 Taskdescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.5.2 Trainingprocedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.5.3 Behavioralcharacterization . . . . . . . . . . . . . . . . . . . . . . . 44
2.6 Behavioralanalysissuite . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.7 Thoughtsontaskdesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3 History-dependent variability in population dynamics during
evidence accumulation in cortex . . . . . . . . . . . . . . . . . . . . . . 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2.1 Singleneuronresponsesduringevidenceaccumulation . . . . . . . . 55
3.2.2 Task-relevantinformationisdistributedacrossneuronalpopulations . 56
3.2.3 Clusteringneuronalactivitypatternsacrosstrials . . . . . . . . . . . . 63
3.2.4 Temporallystructuredtrial-trialvariability . . . . . . . . . . . . . . . 72
3.2.5 AmemoryofpasteventsismaintainedinPPCpopulationactivityover
secondsandacrosstrials . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.2.6 Anovelmodelforevidenceaccumulationbasedonhistory-dependent
dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.3 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.4 Authorcontributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4 Discussion and future experiments . . . . . . . . . . . . . . . . . . . . 91
4.1 History-dependentpopulationdynamics . . . . . . . . . . . . . . . . . . . . 91
4.2 Importanceofsingle-trialpopulationanalyses . . . . . . . . . . . . . . . . . 93
4.3 Inconsistencieswithwinner-take-allmodels . . . . . . . . . . . . . . . . . . 94
vi
4.4 AgeneralrulefordynamicsinthePPC . . . . . . . . . . . . . . . . . . . . 96
4.5 Developingneuronalimplementationsofhistory-dependentdynamics . . . . 98
4.6 Additionalexperimentaltests . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.7 Recreatinghistory-dependentdynamicsinsilicowithrecurrentneuralnetworks102
5 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2 Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2.1 Surgicalprocedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2.2 Two-photonmicroscopedesign . . . . . . . . . . . . . . . . . . . . . 107
5.2.3 Imagingdataacquisition . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2.4 Pre-processingofimagingdata . . . . . . . . . . . . . . . . . . . . . 108
5.3 Dataanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.1 Generalanalysisprocedures . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.2 Choiceselectivityforindividualneurons . . . . . . . . . . . . . . . . 113
5.4 Classifiers(withoutclustering) . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.4.1 Generalprocedures . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.4.2 Two-classclassifications . . . . . . . . . . . . . . . . . . . . . . . . 114
5.4.3 Multi-classclassifications . . . . . . . . . . . . . . . . . . . . . . . . 115
5.4.4 Classifiersbuiltbyadding-insubsetsofneurons . . . . . . . . . . . . 116
5.4.5 Classificationofcuesequences . . . . . . . . . . . . . . . . . . . . . 116
5.5 Analysisofactivityinhigh-dimensionalstatespace . . . . . . . . . . . . . 117
5.5.1 Factoranalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.2 Relationshipbetweenpairwisetrial-trialdistancesbeforeandaftercue
presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.3 Relationship between pairwise trial-trial distances and population
activityvectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.6 Clusteringmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.6.1 Pre-processingforclustering . . . . . . . . . . . . . . . . . . . . . . 119
5.6.2 Clusteringviaaffinitypropagation . . . . . . . . . . . . . . . . . . . 120
5.6.3 Transitionprobabilitiesbetweenclusters . . . . . . . . . . . . . . . . 121
vii
5.6.4 Clustering based on all time points together (rather than epoch-by-
epochclustering) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.7 Classifiersbasedonactivityincluster-space . . . . . . . . . . . . . . . . . . 122
5.7.1 Classificationoftheclusteridentityatpastandfutureepochsbasedon
theclusteridentityatthecurrentepoch . . . . . . . . . . . . . . . . . 122
5.7.2 Classification of past and future cluster identities with simulated
pseudo-populations . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.7.3 Classificationofpastandfutureclusteridentitiesbasedonbehavioral
data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.7.4 Classificationofbehavioralerrorsbasedonclusteridentity . . . . . . 125
5.8 Analysisoftheoverlapofactiveneuronsacrossclusters . . . . . . . . . . . 126
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
viii
Listing of figures
2.1 Schematicofvirtualrealitybehaviorsystem. . . . . . . . . . . . . . . . . . 23
2.2 Afixedassociationevidenceaccumulationtaskinvirtualreality. . . . . . . . 25
2.3 Behavioral training procedure for the fixed association evidence
accumulationtask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 Behavioral characterization of the fixed association evidence accumulation
task. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5 Adelayed-match-to-sampletaskinvirtualreality. . . . . . . . . . . . . . . . 34
2.6 Behavioraltrainingprocedureforthedelayed-match-to-sampletask . . . . . 36
2.7 Adelayed-match-to-sampleevidenceaccumulationtaskinvirtualreality. . . 41
2.8 Behavioral training procedure for the delayed-match-to-sample evidence
accumulationtask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.9 Behavioral characterization of the delayed-match-to-sample evidence
accumulationtask. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.10Behavioralanalysissuite. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1 Exampleimagingfieldofviewandactivitytraces. . . . . . . . . . . . . . . 54
3.2 Summaryofdatasetsanalyzed. . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 Distributedrepresentationoftask-relevantinformationacrossPPCneurons. . 57
3.3 (Continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4 MeanpopulationactivitypatternsinPPCforallcellsandselectivecells. . . . 59
3.5 Analyses of single neuron- and population-level representations of
task-relevantfeatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.6 Clusteringneuronalactivityacrosstrialstorevealstrial-to-trialvariability. . . 65
3.6 (Continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.7 Characterizationofbehavioralandneuronalpatternsacrossclusters. . . . . . 69
ix