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

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Universität BremenExtraction of information from thedynamical activities of neural networksDavid RotermundSeptember 2007iiExtraction of information from thedynamical activities of neural networksVom Fachbereich fur¨ Physik und Elektrotechnikder Universit¨ at Bremenzur Erlangung des akademischen Grades einesDoktor der Naturwissenschaften (Dr. rer. nat.)genehmigte DissertationvonDipl. Phys. David Rotermundaus Delmenhorst1. Gutachter: Prof. Dr. rer. nat. Klaus Pawelzik2. Gutachter: Prof. Dr. rer. nat. Andreas KreiterEingereicht am: 11. September 2007Datum des Kolloquiums: 29. November 2007iiiiiAbstractInteracting with our dynamic environment requires to process huge amounts of sensorydata in short time. This incoming stream of information is combined with internalstates (e.g. memories or intentions) and results in actions. The fundamental mech-anisms behind this fast information processing are still not understood. Even howinformation is stored in, and transmitted with sequences of action potentials is stillunder heavy debate. This thesis provides novel ideas to accomplish fast informationprocessing, to understand adaptive coding strategies, and to perform unsupervised on-line learning of non-stationary representations.In its first, genuinely theoretical part (chapter 3 - Information Processing Spike bySpike) this thesis develops a new concept in the field of fast information processingwith single action potentials.

Sujets

• Informatik

Informations

• Publié par: universitat_bremen
• Publié le: 01 janvier 2007
• Nombre de lectures: 32
• Langue: English
• Poids de l'ouvrage: 10 Mo

Extrait

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

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

Eingereichtam:11.September2007

DatumdesKolloquiums:29.November2007

ii

Abstract

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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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