Extraction of information from the dynamical activities of neural networks [Elektronische Ressource] / von David Rotermund

ersitätUniv

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vidDa

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dynamicalExtractionaofctivitiesoinformationfneuralfromnetwtheorks

VomFachbereichf¨urPhysikundElektrotechnik

derUniversit¨atBremen

zurErlangungdesakademischenGradeseines
DoktorderNaturwissenschaften(Dr.rer.nat.)

rtationesDisgenehmigte

nvoDipl.Phys.DavidRotermund

elmenhorstDaus

1.Gutachter:Prof.Dr.rer.nat.KlausPawelzik

2.Gutachter:Prof.Dr.rer.nat.AndreasKreiter

Eingereichtam:11.September2007

DatumdesKolloquiums:29.November2007

ii

Abstract

iii

Interactingwithourdynamicenvironmentrequirestoprocesshugeamountsofsensory
datainshorttime.Thisincomingstreamofinformationiscombinedwithinternal
states(e.g.memoriesorintentions)andresultsinactions.Thefundamentalmech-
anismsbehindthisfastinformationprocessingarestillnotunderstood.Evenhow
informationisstoredin,andtransmittedwithsequencesofactionpotentialsisstill
underheavydebate.Thisthesisprovidesnovelideastoaccomplishfastinformation
processing,tounderstandadaptivecodingstrategies,andtoperformunsupervisedon-
linelearningofnon-stationaryrepresentations.
Initsfirst,genuinelytheoreticalpart(chapter3-InformationProcessingSpikeby
Spike)thisthesisdevelopsanewconceptinthefieldoffastinformationprocessing
withsingleactionpotentials.Theframeworkisbasedonstochasticgenerativemodels
usingPoissonianspiketrainsasinput.Itiscapableofrealizingarbitraryinput-output
functions,updatinganinternalrepresentationwitheachincomingspike,forperform-
ingcomputationsasfastaspossible.
Leavingthosepurelytheoreticalconsiderationsbehind,thesecondpartofthisthesis
(chapter4-SelectiveVisualAttentioninV4/V1)investigatesprinciplesofadaptive
neuralcodinginrealdata,focusingonthequestionhowaninternalcorticalstate,
evokedbyselectivevisualattention,modifiesinformationprocessinginthebrain.In
collaborationwithmonkeyneuro-physiologistswestudiedtheinfluenceofattention
onthediscriminabilityofvisualstimulithroughtheirneuronalcorrelatesrecordedas
epiduralfieldpotentials.
Thefinalpartinthisthesis(chapter5-StabilizingDecodingAgainstNon-Stationaries)
takesustowardsamedicalapplicationforextractinginternalbrainstatesfromneu-
ronalactivities.Forcontrollingprostheticdeviceswithbrainsignals,reliablealgo-
rithmsforestimatingtheintendedactionsofapersonarerequired.Amethodwas
designedwhichallowstostabilisetheestimatorofaneuro-prosthesisagainstdisrup-
tionsfromnon-stationaritiesinthecharacteristicsofcodingtheintendedactions,and
fromchangesintheirrepresentationsinthemeasuredneuronalcorrelates.
Takentogether,thisthesispresentedthreenewcontributions:
Atheoreticalmethodofprocessinginformationspikebyspikeinafastandefficient
fashion.Thisstudyalsoshowedthatitissufficienttouseneurons,generatingPoisso-
nianspiketrains,forperformingfastandefficientinformationprocessing(Ernstetal.,
2007b).Anewmechanism,producedthroughselectivevisualattention,wasrevealedthatren-
dersinformationaboutdifferentvisualstimuli,representedinγ-bandoscillatoryac-
tivityofneuronalpopulations,moredistinctforanexternalobserverandprobably
forthebrainitself.Italsoshowedthatinternalstatesofthebraincanaltertheneu-
ronalactivitypatterninacomplexmanneranditdemonstratedthatthepowerofthe
γ-bandcontainssignificantinformationaboutvisuallyperceivedshapes(Rotermund
2007a).al.,te

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sttenCon1Introduction1
2TheoreticalandBiologicalBackground7
2.1Encodinginformationintosequencesofactionpotentials........7
2.2Reconstructinginformationfromsequencesofactionpotentials....12
2.2.1Probabilities.............................13
2.2.2Informationmeasuresandlossfunctions.............16
2.2.3Propabilitybasedestimators....................20
2.2.4Discriminationandclassification.................25
2.3Modelingofneurons............................35
2.3.1Measuringneuronalresponses...................36
2.3.2Integrate-and-fireneurons.....................37
2.4Learningandusing(neuronal)networks.................42
2.4.1Feedforwardnetworks.......................43
2.4.2Bayesiannetworks.........................46
2.4.3MonteCarlomethodsandexpectationmaximisationalgorithm49
2.4.4Reinforcementlearning......................54
3InformationProcessingSpikebySpike59
3.1Motivation..................................59
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3.2ASpike-BasedGenerativeModel.....................
3.2.1BasicModel.............................
3.2.2FromPoissontoBernoulliProcesses...............
3.2.3FromDeterministictoProbabilisticDecomposition.......
3.2.4EstimationandLearningSpikebySpike.............
3.2.5Simplifiedalgorithmwithbatchlearning.............
3.3Results....................................
3.3.1ASimpleExample.........................
3.3.2Pre-Processing,Training,andClassification...........
3.3.3Booleanfunctions..........................
3.3.4HandwrittenDigits.........................
3.3.5HierarchicalNetworks.......................
3.3.6Stepstowardbiologicalplausibility................
3.3.7Artificialandnaturalimages....................
3.4SummaryandDiscussion.........................

4SelectiveVisualAttentioninV4/V1
4.1Motivation..................................
4.2Thevisualsystem..............................
4.2.1Retina................................
4.2.2Pathwaystoandthroughthevisualcortex............
4.2.3Visualattention..........................
4.3ExperimentalSetting,PreparationsandMethods............
4.3.1Theexperimentalsetting......................
4.3.2DataPreprocessing.........................
4.3.3DiscriminatingStimuliwithSVMs................

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4.4Results....................................122
4.4.1Discriminatingshapes.......................122
4.4.2Improvementofclassificationperformancesthroughattention.127
4.4.3Stimulus-specificsignalsandcoding................132
4.4.4Attentioninducedstimulus-specificsignalschanges.......135
4.4.5AttentioneffectsinV1.......................143
4.4.6Modellingstimulus-specificsignals.................146
4.4.7DiscriminatingtheAttentionalCondition.............152
4.4.8AttentiononMorphingShapes..................157
4.5SummaryandDiscussion..........................164

5StabilizingDecodingAgainstNon-stationaries
5.1Motivation............................
5.2NeuronalandComputationalBackground..........
5.2.1Motorsystemandmovementsofarms........
5.2.2Errorsignalsinthebrain...............
5.2.3Braincomputerinterfaces...............
5.3Themodelforthesimulations.................
5.3.1NeuralEncodingofIntendedMovement.......
5.3.2EstimationofIntendedMovement...........
5.3.3NeuralEncodingofPerceivedError.........
5.3.4Adaptation.......................
5.3.5ChoiceofParameters..................
5.4ResultsfromtheSimulations..................
5.5ConclusionandSummary...................

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onclusionCandSummary6

AAdditionalBackground
A.1Modelingofneurons............................
A.1.1HodgkinandHuxleymodel....................
A.1.2McCullochandPittsneurons...................
A.2Propabilitybasedestimators........................
A.2.1Minimummeansquarederrorestimator.............
A.2.2Linearminimummeansquarederrorestimator.........
A.3Recurrentnetworks............................
A.3.1Hopfieldnetworks..........................
A.3.2Boltzmannmachines........................
A.3.3Liquidstatemachine........................
A.4Generativemodels.............................
A.4.1HiddenMarkovmodel.......................
A.4.2Helmholtzmachines........................

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BInformationprocessingspikebyspike233
B.1PatternPre-Processing..........................233
B.2TrainingProcedures............................234
B.3ClassificationandComputationProcedures...............234
B.4DetailsandParametersfortheComputationofBooleanFunctions..235
B.5DetailsandParametersfortheClassificationofHandwrittenDigits..235

CStabilizingdecodingagainstnon-stationaries
C.1Theestimatorforthevelocity.......................
C.2Parameteradaptation...........................

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1Chapter

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