Cerebral Cortex

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 movingwithparticularspee

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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 featureofthischapterisitsrelationtoattempttoconstructfabricat

Zusammenfassung
This volume is devoted to mathematical models of the cortex. It begins with a short history of models of cortical neurons and circuitry that introduces the principal modelling styles. An attempt has been made throughout the book to make it accessible to readers with minimal mathematical background.

Inhalt
1 Modeling Cortical Circuitry: A History and Prospectus.- 1. Introduction.- 2. Lorente de Nó through Dynamical Systems Models.- 3. Hodgkin and Huxley through Network Models.- 4. Prospectus.- 5. References.- 2 Interpretations of Data and Mechanisms for Hippocampal Pyramidal Cell Models.- 1. Introduction.- 2. The Database for Single-Neuron Models.- 3. Strategies for Single-Neuron Models.- 4. Anatomy and the Model: Data and Methods.- 5. The Linear Model: Data and Methods.- 6. Phenomenological Templates.- 7. Review of Hippocampal Models.- 8. Channel Models.- 9. Ionic Concentration Dynamics.- 10. Nonsynaptic Channels of Hippocampal Pyramidal Cells.- 11. HPC Sodium Channels.- 12. HPC Calcium Channels.- 13. HPC Potassium Channels.- 14. Nonspecific Cation and Chloride Currents.- 15. Simulations of HPC Properties with the Working Model.- 16. References.- 3 Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons.- 1. Introduction.- 2. Historical Perspective.- 3. Amplification of Synaptic Inputs.- 4. Compartment Model Simulations of Amplification.- 5. Effects of Dendritic Active Currents on EPSP Shape.- 6. The Effect of Dendritic Active Currents in Shaping the Intrinsic Firing Properties of Pyramidal Cells.- 7. Effects of Potassium Currents.- 8. Linking Firing of the Soma to Depolarization at Distal Synapses and the Implementation of Hebb's Hypothesis.- 9. Apologies.- 10. Concluding Observations.- 11. References.- 4 Comparing Different Modeling Approaches of Visual Cortical Cell Characteristics.- 1. Introduction.- 2. Foundations.- 3. Models of Cortical Orientation Specificity.- 4. Concluding Remarks.- 5.…