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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...

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. Woergoetter. 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.