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

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. Appendix.- 6. References.- 5 The Role of Recurrent Excitation in Neocortical Circuits.- 1. Introduction.- 2. Wiring Neocortical Circuits.- 3. CanonicalMicrocircuits.- 4. Units of Construction of the Basic Cortical Circuit.- 5. The Neuronal Components of Layer 4.- 6. Computation of Orientation.- 7. Noise and Restoration.- 8. References.- 6 Neural Mechanisms Underlying the Analysis of Moving Visual Stimuli.- 1. Introduction.- 2. A Primer of Basic Concepts.- 3. Neural Mechanisms: Mammals.- 4. Neural Mechanisms: Turtles.- 5. Conclusions and Future Directions.- 6. References.- 7 Linearity and Gain Control in VI Simple Cells.- 1. Introduction.- 2. The Linear Model of Simple Cells.- 3. Some Linear Properties of Simple Cells.- 4. Biophysics of the Linear Model.- 5. Some Nonlinear Properties of Simple Cells.- 6. The Normalization Model of Simple Cells.- 7. Testing the Normalization Model.- 8. Biophysical Plausibility of the Normalization Model.- 9. Conclusions.- 10. References.- 8 Non-Fourier Cortical Processes in Texture, Form, and Motion Perception.- 1. Introduction.- 2. Analysis of Texture Boundaries by Non-Fourier Mechanisms.- 3. Area V4 Neurons and Form Vision.- 4. Two-Dimensional Motion.- 5. Discussion.- 6. References.- 9 Modeling Thalamocortical Oscillations.- 1. Slow Thalamic Rhythms.- 2. Thalamus as Magnet for ModelingDynamics and Neural Systems.- 3. Early Modeling Predicted a Role for Inhibitory Phasing.- 4. Modeling the T Channel.- 5. Modeling the Low-Threshold Spike.- 6. The Basic Two-Neuron Network.- 7. The Search for Origins: Whence Spindling?.- 8. Synchrony and Spread of Network Activity.- 9. Summary and Conclusions.- 10. References.- 10 Realistic Network Models of Synchronized Oscillations in Visual Cortex.- 1. Introduction.- 2. Model Structure.- 3. Model Results.- 4. Conclusion.- 5. References.- 11 Modeling the Piriform Cortex.- 1. Introduction.- 2. Summary of Data Being Modeled.- 3. Modeling ofPhysiological Data.- 4. Modeling of Functional Hypotheses.- 5. Summary and Future Directions.- 6. References.