Cerebral Cortex
Models of Cortical Circuits
(Sprache: Englisch)
Thisisthefirstvolumeinthe CerelJral Cortexseriesdevotedtomathematicalmodels ofthecortex. Itwasmotivatedbytherealizationthatcomputationalmodelsof individualneuronsandensemblesofneuronsareincreasinglyusedinresearchon corticalorganizationandfunction....
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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.
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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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Inhaltsverzeichnis zu „Cerebral Cortex “
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.
Bibliographische Angaben
- 2012, Softcover reprint of the original 1st ed. 1999, XXI, 573 Seiten, Maße: 17,8 x 25,4 cm, Kartoniert (TB), Englisch
- Herausgegeben: Philip S. Ulinski
- Verlag: Springer, Berlin
- ISBN-10: 1461372232
- ISBN-13: 9781461372233
Sprache:
Englisch
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