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Biomedical Sciences - Neuroscience | Cerebral Cortex - Models of Cortical Circuits

Cerebral Cortex

Models of Cortical Circuits

Series: Cerebral Cortex, Vol. 13

Ulinski, Philip S. (Ed.)

1999, XXI, 573 p.

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  • About this book

Thisisthefirstvolumeinthe CerelJral Cortexseriesdevotedtomathematicalmodels ofthecortex. Itwasmotivatedbytherealizationthatcomputationalmodelsof individualneuronsandensemblesofneuronsareincreasinglyusedinresearchon corticalorganizationandfunction. Thisis,inpart,becauseofthenowubiquitous presenceofpowerfulandaffordablecomputers. Suitablemachineswereformerly rareinresearchlaboratoriesandrequiredsubstantialprogrammingexpertisetobe usedinconstructingandusingneuronalmodels. However,computersarenow routinelyusedinallareasofneurobiologyandanumberofsoftwarepackagesallow scientistswithminimalcomputerscienceandmathematicalbackgroundstocon­ structseriousneuronalmodels. Asecondfactorleadingtotheproliferationof modelingstudiesisthedevelopmentoftechnologiesthatallowthekindsofdata collectionneededtodeveloprealisticmodelsofcorticalneurons. Characterization ofthekineticsofvoltage-andligand-gatedchannelsandreceptorshadbeenlim­ itedtorelativelylargeneurons. However,therapiddevelopmentofsliceprepara­ tions,patch-clampmethods,andimagingmethodsbasedonvoltage-sensitivedyes andintracellularcalciumindicatorshasresultedinasignificantdatabaseonthe biophysicalfeaturesofcorticalneurons. Thescopeofmodelingapproachestocorticalneuronsandfunctionsiswide anditseemednecessarytolimitthepurviewofthevolume. Thefocusisonattempts tounderstandthepropertiesofindividualcorticalneuronsandneuronalcircuitry throughmodelsthatincorporatesignificantfeaturesofcellularmorphologyand physiology. Noattemptwasmadetoincludemodelingapproachestounderstanding corticaldevelopmentandplasticity. Thus,workdealingwiththedevelopmentof oculardominancecolumnsandtheorientationselectivityofneuronsinvisualcortex isnotconsidered. Similarly,modelsdealingwiththecellularmechanismsunderlying long-termplasticityandwithapproachestolearningandmemorybasedonmodifica­ tionofHebbiansynapsesarenotconsidered. Relativelyabstractattemptstounder­ standhigherlevelandcognitiveprocessesbasedonneuralnetsrepresentasecond, majorareaofworkthatisnottreated. Modelsofcognitiveprocessesbasedon dynamicalsystemsmethodsinwhichnoattemptismadetoincludethebiophysical featuresofindividualneuronsarealsonotconsidered. vii viii Thetenmajorchaptersfallintothreegroups. Thefirstgroupdealswith compartmentalmodelsofindividualcorticalneurons. LyleBorg-Grahamprovides PREFACE anintroductiontothemethodsinvolvedinconstructingcompartmentalmodels andthenreviewstheexistingmodelsofhippocampalpyramidalcells. Becauseof theeffectivenessofhippocampalslicepreparations,theseneuronshavewell-ehar­ acterizedbiophysicalproperties. Thischapterillustrateshowcompartmentalmod­ elscanbeusedtosynthesizeexperimentaldataandprovideanintegrativeviewof thepropertiesofindividualneurons. PaulRhodescontinuesthethemebyfocusing ontheroleofvoltage-gatedchannelslocatedonthedendritesofcorticalneurons. Thisisanareainwhichtechnologicaladvancesinthevisualizationofneuronsin slicepreparationsbasedoninfraredmicroscopyhavegreatlyexpandedtheinfor­ mationavailableondendriticfunctioninjustafewyears. Thechapterbothreviews theexperimentaldataonactivedendriticconductancesandemphasizestheirpo­ tentialfunctionalroles. Thesecondgroupofchaptersdealwiththegenerationofreceptivefield propertiesofneuronswithinvisualcortex. Theyaddressissuesstemmingfromthe originalattempttounderstandhowthereceptivefieldpropertiesofneuronsincat andmonkeyprimaryvisualcortexaregeneratedbyinteractionsbetweengenicu­ lateafferentsandcorticalneurons. ThechapterbyFlorentinWorgotterevaluates modelsthathavebeenusedtoanalyzethegenerationofreceptivefieldproperties. RodneyDouglasandhiscolleaguesaddressaspecificsetofissuesdealingwiththe roleofintracorticalexcitationmediatedbypyramidalcellcollaterals. Animportant featureofthischapterisitsrelationtoattempttoconstructfabricatedcircuitsthat duplicatethefunctionsofcorticalcircuits. ThechapterbyPhilipUlinskifocuseson thegenerationofmotion-selectivepropertiesincorticalneurons. Itseekstoidenti­ tycellularmechanismsusedbyneuronsthatrespondpreferentiallytovisualstimuli movingwithparticularspeedsordirections. MatteoCarandiniandhiscolleagues discussthefeatureofcorticalneurons,knownasgaincontrol,thatallowsneurons torespondeffectivelytovisualstimulibypoolinginformationacrosspopulationsof corticalneurons. ThechapterbyHughWilsondealswiththereceptivefieldproper­ tiesofextrastriateareasandintroducesnewworkanalyzingface-selectiveneurons. Thefinalsetofchaptersconsidermodelsofensemblesofthalamicandcortical neurons. ThechapterbyWilliamLyttonandElizabethThomasusesthetheoryof dynamicalsystemstoanalyzethetemporalrelationshipsbetweenthalamicand corticalneurons. Animportantfeatureoftheinteractionbetweenthalamusand cortexisthepresenceofoscillationsthatdependinpartuponthevoltage-gated conductancespresentonindividualneuronsandinpartonthestructureofthe overallnetwork. PaulBushcontinuesthisemphasisonoscillationsbydiscussinga modelthatdealswiththegenerationofsynchronizedoscillationsinvisualcortex. Oscillationsofthiskindhaveattractedsubstantialattentioninrecentyearsbecause oftheirpotentialroleincognitiveprocesses. Thelastchapter,byMichaelHasselmo andChristianeLinster,reviewstheirworkonmodelingpiriformcortex,emphasiz­ ingtheroleofcholinergicmechanismsinmodulatingtheactivityofcorticalneu­ rons. Anattempthasbeenmadethroughouttomakethevolumeaccessibleto readerswithminimalmathematicalbackgrounds. Thevolumethusbeginswitha shorthistoryofmodelsofcorticalneuronsandcircuitrythatintroducestheprinci­ palmodelingstyles. ThechaptersbyWorgotterandUlinskicontainmoreextensive ix introductionstosomeofthemodelingmethodsthathavebeenusedtostudyvisual cortex,andthemathematicallychallengedreaderwillfindthatthechapterby PREFACE LyttonandThomascontainsareadableintroductiontotheuseofdynamical systemstheoryinneurobiology. PhilipS. Ulinski EdwardG. Jones Chicago and Davis Contents Chapter 1 ModelingCorticalCircuitry:AHistoryandProspectus PhilipS. Ulinski 1. Introduction ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. LorentedeNothroughDynamicalSystemsModels. . . . . . . . . . . . . . . . . 2 2. 1. LorentedeNo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2. 2. CellAssembliesandNeuralNets. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. 3. DynamicSystemsModels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3. HodgkinandHuxleythroughNetworkModels. . . . . . . . . . . . . . . . . . . . . 11 3. 1. HodgkinandHuxley. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3. 2. WilfridRall. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3. 3. SoftwarePackages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3. 4. RealisticModelsofCorticalNetworks. . . . . . . . . . . . . . . . . . . . . . . . 14 4. Prospectus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 2 InterpretationsofDataandMechanismsforHippocampalPyramidal CellModels LyleJ Borg-Graham 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1. 1. NeuronModelEvolution-followingElectrophysiology. . . . . . . . . 19 1. 2. NeuronModelEvaluation-followingtheParameters. . . . . . . . . . 21 1. 3. WhyHippocampus? 21 1. 4. OrganizationofThisChapter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 xi xii 2. TheDatabaseforSingle-NeuronModels. . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2. 1. VoltageClampversusCurrentClamp. . . . . . . . . . . . . . . . . . . . . . . . 23 CONTENTS 2. 2. Single-ChannelversusMacroscopicCurrents. . . . . . . . . . . . . . . . . . 24 2. 3. TypeofPreparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2. 4. KineticandPharmacologicalDissection. . . . . . . . . . . . . . . . . . . . . . 25 2. 5. TemperatureDependence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2. 6. AgeDependence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2. 7. HippocampalSubfieldDependence. . . . . . . . . . . . . . . . . . . . . . . . . 27 2. 8. DifferencesinFiringPropertiesbetweenSharpversusPatch Recordings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. 9. TheMeasuredVoltage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Content Level » Research

Keywords » cell - cerebral cortex - cortex - neural mechanisms - neurons - perception

Related subjects » Neuroscience - Surgery

Table of contents 

1. Modeling Cortical Circuitry: A History and Prospectus; P.S. Ulinski. 2. Interpretations of Data and Mechanisms for Hippocampal Pyramidal Cell Models; L.J. Borg-Graham. 3. Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons; P.A. Rhodes. 4. Comparing Different Modeling Approaches of Visual Cortical Cell Characteristics; F. Wörgötter. 5. The Role of Recurrent Excitation in Neocortical Circuits; R. Douglas, et al. 6. Neural Mechanisms Underlying the Analysis of Moving Visual Stimuli; P.S. Ulinski. 7. Linearity and Gain Control in V1 Simple Cells; M. Carandini et al. 8. Non-Fourier Cortical Processes in Texture, Form, and Motion Perception; H.R. Wilson. 9. Modeling Thalamocortical Oscillations; W.W. Lytton, E. thomas. 10. Realistic Network Models of Synchronized Oscillations in Visual Cortex; P. Bush. 11. Modeling the Piriform Cortex; M.E. Hasselmo, C. Linster.

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