Source: https://catalog.uttyler.edu/cgi-bin/koha/opac-detail.pl?biblionumber=1035668
Timestamp: 2019-04-23 04:00:51+00:00

Document:
Neural Networks : Foundations to Applications : Single Neuron Computation.
Contributor(s): Davis, Joel L | Zornetzer, Steven F.
Front Cover -- Single Neuron Computation -- Copyright Page -- Table of Contents -- Contributors -- Preface -- PART I: COMPUTATION IN DENDRITES AND SPINES -- Chapter 1. Electrotonic Models of Neuronal Dendrites and Single Neuron Computation -- I. Introduction -- II. Estimating the Electrotonic Structure of a Cell -- III. The Dynamic Range of Computational Possibilities Exhibited by Neurons -- IV. Synaptic Modification in Dendritic Spines -- V. Summary -- Acknowledgments -- References -- Chapter 2. Canonical Neurons and Their Computational Organization -- I. Historical Background for the Complex Neuron -- II. Development of the Computational Representation of the Complex Neuron -- III. Strategies for Neuronal Modeling -- IV. The Concept of the Canonical Neuron -- V. Hierarchical Organization of Canonical Neurons in the Olfactory System -- VI. The Cortical Pyramidal Neuron -- Acknowledgments -- References -- Chapter 3. Computational Models of Hippocampal Neurons -- I. Neuromorphometry -- II. Electrotonic Structure -- III. Computer Simulations -- IV. Methods and Results -- V. Summary and Conclusions -- Acknowledgment -- References -- Chapter 4. Hebbian Computations in Hippocampal Dendrites and Spines -- I. Introduction -- II. Nodes and Neurons -- III. Voltage Gradients in Dendrites and Spines -- IV. Spatial Representation of Electrotonic Structure -- V. Voltage-Dependent Synaptic Modification -- VI. Self-Organization and Pattern Association -- VII. Summary and Conclusions -- Acknowledgments -- References -- Chapter 5. Synaptic Integration by Electro-Diffusion in Dendritic Spines -- I. Introduction -- II. Cable Model Predictions -- III. Limitations of the Cable Model -- IV. Electro-Diffusion Model Predictions -- V. The Cable Model for Electro-Diffusion -- VI. Discussion -- Acknowledgments -- References.
Chapter 6. Dendritic Morphology, Inward Rectification, and the Functional Properties of Neostriatal Neurons -- I. Introduction -- II. Firing Pattern of Neostriatal Spiny Projection Neurons -- III. Distribution of Synaptic Inputs on the Spiny Projection Neuron -- IV. A Model of the Spiny Neuron -- V. Input Resistance and Electrotonic Length of the Passive Model -- VI. Effect of Fast Anomalous Rectification on Input Resistance and Time Constant -- VII. If the Time Constant Is Not Constant, the Length Constant Is Not Either -- VIII. Synaptic Integration in the Spiny Neuron -- IX. Dendritic Spines and Synaptic Strength -- X. Effect of Fast Anomalous Rectification on Synaptic Integration -- XI. Implications for Neostriatal Function -- Acknowledgments -- References -- Chapter 7. Analog and Digital Processing in Single Nerve Cells: Dendritic Integration and Axonal Propagation -- I. Introduction -- II. Methods -- III. Results -- IV. Discussion -- Acknowledgment -- References -- Chapter 8. Functions of Very Distal Dendrites: Experimental and Computational Studies of Layer I Synapses on Neocortical Pyramidal Cells -- I. The Significance of Cortical Layer I -- II. The Synaptic Response to Activation of Horizontal Layer I Afferents -- III. Computational Model of a Layer V Cell: Determination of Parameters -- IV. Steady-State and Transient Responses of the Modeled Pyramidal Neuron -- V. Efficacy and Mechanisms of Synaptic Inputs to Layer I -- VI. Summary -- Acknowledgments -- References -- PART II: ION CHANNELS AND PATTERNED DISCHARGE, SYNAPSES, AND NEURONAL SELECTIVITY -- Chapter 9. Ionic Currents Governing Input-Output Relations of Betz Cells -- I. Introduction -- II. Persistent Sodium Current -- III. Sodium-Dependent Potassium Current -- IV. Calcium-Dependent Potassium Currents -- V. Calcium-Dependent Cation Current -- VI. Slow Inward Cation Current.
VII. Voltage-Gated Potassium Currents -- VIII. Conclusions -- References -- Chapter 10. Determination of State-Dependent Processing in Thalamus by Single Neuron Properties and Neuromodulators -- I. Introduction -- II. Electrophysiological Properties of Thalamic Neurons -- III. Neuromodulation of Thalamic Neuronal Activity -- IV. Computational Simulation of Thalamic Neuronal Activity -- V. Functional Implications of Multistate Neuronal Activity -- VI. Conclusions -- Acknowledgments -- References -- Chapter 11. Temporal Information Processing in Synapses, Cells, and Circuits -- I. Introduction -- II. Physiological Models of Cellular PSPs -- III. Physiological Modeling of Temporal Integrative Properties -- IV. Discussion -- Chapter 12. Multiplying with Synapses and Neurons -- I. Introduction -- II. Why Multiplications? -- III. Multiplication: Biophysical Mechanisms -- IV. Conclusion -- Acknowledgment -- References -- Chapter 13. A Model of the Directional Selectivity Circuit in Retina: Transformations by Neurons Singly and in Concert -- I. Introduction -- II. Overview of Directional Selectivity and the Retina -- III. A Model of DS Output of Amacrine Cell Dendrite Tips -- IV. Predictions of the Model -- V. Simulations of Morphometrically and Biophysically Detailed Amacrine Cell Models -- VI. Intracellular DS Recordings with Local Block of Inhibition -- VII. Development of DS: The Problem of Coordination of Asymmetries -- VIII. Retinal Directional Selectivity: Exemplar of a Canonical Computational Mechanism? -- IX. Conclusions -- Acknowledgments -- References -- PART III: NEURONS IN THEIR NETWORKS -- Chapter 14. Exploring Cortical Microcircuits: A Combined Anatomical, Physiological, and Computational Approach -- I. Introduction -- II. Abstraction of Single Cortical Neurons -- III. Exploring Neuronal Interactions -- IV. Conclusion -- Acknowledgments.
Reference -- Chapter 15. Evolving Analog VLSI Neurons -- I. Introduction -- II. Interface -- III. Communication -- IV. Neurons -- V. Synapses -- VI. Neurons that Learn Sequence -- VII. Summary -- Acknowledgments -- References -- Chapter 16. Relations between the Dynamical Properties of Single Cells and Their Networks in Piriform (Olfactory) Cortex -- I. Introduction -- II. The Olfactory System as a Model Cerebral Cortical Sensory Network -- III. Modeling Olfactory Cortex -- IV. Functional Significance of Patterns of Dendritic Activation -- V. Conclusion -- Acknowledgments -- References -- Chapter 17. Synchronized Multiple Bursts in the Hippocampus: A Neuronal Population Oscillation Uninterpretable without Accurate Cellular Membrane Kinetics -- I. Introduction -- II. Synchronized Multiple Bursts (Afterdischarges) in Disinhibited Hippocampal Slices -- III. Considerations on the Mechanisms of SMB -- IV. Hypotheses as to the Biological Significance of SMB -- V. Conclusion -- References -- PART IV: MULTISTATE NEURONS AND STOCHASTIC MODELS OF NEURON DYNAMICS -- Chapter 18. Signal Processing in Multi-Threshold Neurons -- I. Introduction -- II. Representation of Neuronal Signals -- III. Spike Codes in Neurons -- IV. Multiple Thresholds in Neurons -- V. Functional Significance of Multi-Threshold Neurons -- VI. Summary -- Acknowledgment -- References -- Chapter 19. Cooperative Stochastic Effects in a Model of a Single Neuron -- I. Introduction -- II. The Single Effective Neuron -- III. Response to Weak Modulation: Stochastic Resonance -- IV. Discussion -- Acknowledgment -- References -- Chapter 20. Critical Coherence and Characteristic Times in Brain Stem Neuronal Discharge Patterns -- I. Introduction -- II. Temporal Complexity as a Characteristic of Normal Neuronal Behavior -- III. Elemental Mechanics of Single-Neuron Activation.
IV. Time Scaling and Entropies in Intermittent Neuronal Activities -- V. Databases and Numerical Computations -- VI. Interspike Interval Patterns -- VII. Discussion -- VIII. G(τ) as a Global Characteristic Time -- Acknowledgment -- References -- Chapter 21. A Heuristic Approach to Stochastic Models of Single Neurons -- I. Introduction -- II. First-Passage Times as Neural Firing Times -- Acknowledgments -- References -- Chapter 22. Fractal Neuronal Firing Patterns -- I. Introduction -- II. Self-Similarity of Neuronal Firing Rates -- IV. Fractal Dimension of the Firing Pattern -- V. Alteration of the Firing Pattern Engendered by Stimulation -- VI. Comparison of Auditory and Vestibular Firing Patterns -- VII. Fractal Firing Patterns at Higher Auditory Centers -- VIII. Neural Information Processing with Fractal Events -- IX. Biophysical Origins of the Fractal Behavior -- X. Identifying the Mathematical Point Process -- Acknowledgments -- References -- Index.
Summary: This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real neurons is essential to the design of enhanced processor elements for use in the next generation of ANNs. The book covers computation in dendrites and spines, computational aspects of ion channels, synapses, patterned discharge and multistate neurons, and stochastic models of neuron dynamics. It is the most up-to-date presentation of biophysical and computational methods.
This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real neurons is essential to the design of enhanced processor elements for use in the next generation of ANNs. The book covers computation in dendrites and spines, computational aspects of ion channels, synapses, patterned discharge and multistate neurons, and stochastic models of neuron dynamics. It is the most up-to-date presentation of biophysical and computational methods.

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