Computational Neuroscience

1. Auflage 2003
ISBN: 978-1-58488-362-3
Verlag: Chapman and Hall/CRC

Buch, Englisch, 656 Seiten, Format (B × H): 156 mm x 246 mm, Gewicht: 1025 g

Reihe: Chapman & Hall/CRC Computational Biology Series

How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.Computational Neuroscience: A Comprehensive Approach provides a unified treatment of the mathematical theory of the nervous system and presents concrete examples demonstrating how computational techniques can illuminate difficult neuroscience problems. In chapters contributed by top researchers, the book introduces the basic mathematical concepts, then examines modeling at all levels, from single-channel and single neuron modeling to neuronal networks and system-level modeling. The emphasis is on models with close ties to experimental observations and data, and the authors review application of the models to systems such as olfactory bulbs, fly vision, and sensorymotor systems.Understanding the nature and limits of the strategies neural systems employ to process and transmit sensory information stands among the most exciting and difficult challenges faced by modern science. This book clearly shows how computational neuroscience has and will continue to help meet that challenge.

Zielgruppe

Mathematicians, physicists, engineers, electrophysicists, and systems neuroscientists

Autoren/Hrsg.

Feng, Jianfeng

Fachgebiete

• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tierethologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
• Naturwissenschaften Biowissenschaften Biowissenschaften Neurobiologie, Verhaltensbiologie
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tierpathologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Physiologie

Inhaltsverzeichnis

A THEORETICAL OVERVIEWIntroductionDeterministic Dynamical SystemsStochastic Dynamical SystemsInformation TheoryOptimal ControlATOMISTIC SIMULATIONS OF ION CHANNELSIntroductionSimulation MethodsSelected ApplicationsOutlookMODELING NEURONAL CALCIUM DYNAMICSIntroductionBasic PrinciplesSpecial Calcium Signaling for NeuronsConclusionsSTRUCTURE BASED MODELS OF NO DIFFUSION IN THE NERVOUS SYSTEMIntroductionMethodsResultsExploring Functional Roles with More Abstract ModelsConclusionsSTOCHASTIC MODELING OF SINGLE ION CHANNELS Introduction Some Basic Probability Single Channel Models Transition Probabilities, Macroscopic Currents and Noise Macroscopic Currents and Noise Behaviour of Single Channels under Equilibrium Conditions Time Interval Omission Some Miscellaneous Topics THE BIOPHYSICAL BASIS OF FIRING VARIABILITY IN CORTICAL NEURONS Introduction Typical Input is Correlated and Irregular Synaptic Unreliability Postsynaptic Ion Channel NoiseIntegration of a Transient Input by Cortical NeuronsNoisy Spike Generation Dynamics Dynamics of NMDA ReceptorsClass 1 and Class 2 Neurons Show Different Noise SensitivitiesCortical Cell Dynamical Classes Implications for Synchronous FiringConclusions Generating Models of Single NeuronsIntroduction The Hypothalamo-Hypophysial System Statistical Methods to Investigate The Intrinsic Mechanisms Underlying Spike Patterning Summary and Conclusions GENERATING QUANTITATIVELY ACCURATE, BUT COMPUTATIONALLY CONCISE, MODELS OF SINGLE NEURONSIntroductionThe Hypothalamo-hypophysial SystemStatistical Methods to Investigate the Intrinsic Mechanisms Underlying Spike PatterningSummary and ConclusionsBURSTING ACTIVITY IN WEAKLY ELECTRIC FISH Introduction Overview of the Electrosensory SystemFeature Extraction by Spike BurstsFactors Shaping Burst Firing In Vivo Conditional Action Potential Back Propagation Controls Burst Firing In Vitro Comparison with Other Bursting NeuronsConclusionsLIKELIHOOD METHODS FOR NEURAL SPIKE TRAIN DATA ANALYSISIntroductionTheoryApplications ConclusionAppendix BIOLOGICALLY-DETAILED NETWORK MODELINGIntroductionCellsSynapses Connections Inputs Implementation Validation Conclusions HEBBIAN LEARNING AND SPIKE-TIMING-DEPENDENT PLASTICITY Hebbian Models of Plasticity Spike-Timing Dependent Plasticity Role of Constraints in Hebbian Learning Competitive Hebbian Learning Through STDPTemporal Aspects of STDPSTDP in a NetworkConclusionCORRELATED NEURONAL ACTIVITY: HIGH-AND LOW-LEVEL VIEWSIntroduction: the Timing GameFunctional Roles for Spike TimingCorrelations Arising from Common inputCorrelations Arising from Local Network InteractionsWhen Are Neurons Sensitive to Correlated Input? A Simple, Quantitative Model Correlations and Neuronal Variability ConclusionAppendix A CASE STUDY OF POPULATION CODING: STIMULUS LOCALIZATION IN THE BARREL CORTEXIntroduction Series Expansion Method The Whisker SystemCoding in the Whisker System DiscussionConclusionsMODELING FLY MOTION VISIONThe Fly Motion Vision System: An Overview Mechanisms of Local Motion Detection: The Correlation Detector Spatial Processing of Local Motion Signals BY Lobula Plate Tangential CellsConclusions MEAN-FIELD THEORY OF IRREGULARLY SPIKING NEURONAL POPULATIONS AND WORKING MEMORY IN RECURRENT CORTICAL NETWORKSIntroduction Firing-Rate and Variability of a Spiking Neuron with Noisy inputSelf-Consistent Theory of Recurrent Cortical CircuitsTHE OPERATION OF MEMORY SYSTEMS IN THE BRAINIntroduction Functions of the Hippocampus in Long-Term Memory Short Term Memory Systems Invariant Visual Object Recognition Visual Stimulus-Reward Association, Emotion, and MotivationEffects of Mood on Memory and Visual Processing MODELING MOTOR CONTROL PARADIGMSIntroduction: The Ecological Nature of Motor Control The Robotic Perspective The Biological Perspective The Role of Cerebellum in the Coordination of Multiple JointsControlling Unstable PlantsMotor Learning ParadigmsCOMPUTATIONAL MODELS FOR GENERIC CORTICAL MICROCIRCUITSIntroduction A Conceptual Framework for Real-Time Neural Computation The Generic Neural Microcircuit Model Towards a Non-Turing theory for Real-Time Neural ComputationA Generic Neural Microcircuit on the Computational Test StandTemporal integration and Kernel Function of Neural Microcircuit ModelsSoftware for Evaluating the Computational Capabilities of Neural Microcircuit ModelsDiscussionMODELING PRIMATE VISUAL ATTENTIONIntroductionBrain Areas Bottom-Up ControlTop-Down Modulation of Early VisionTop-Down Deployment of AttentionAttention and Scene UnderstandingDiscussion