McKenna / Davis / Zornetzer Single Neuron Computation

1. Auflage 2014
ISBN: 978-1-4832-9606-7
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 644 Seiten, Web PDF

Reihe: Neural Networks: Foundations to Applications

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.

Autoren/Hrsg.

McKenna, Thomas M.

Davis, Joel L.

Zornetzer, Steven F.

Inhaltsverzeichnis

1;Front Cover;1
2;Single Neuron Computation;4
3;Copyright Page;5
4;Table of Contents;6
5;Contributors;10
6;Preface;14
7;PART I: COMPUTATION IN DENDRITES AND SPINES;16
7.1;Chapter 1. Electrotonic Models of Neuronal Dendrites and Single Neuron Computation;22
7.1.1;I. Introduction;22
7.1.2;II. Estimating the Electrotonic Structure of a Cell;23
7.1.3;III. The Dynamic Range of Computational Possibilities Exhibited by Neurons;31
7.1.4;IV. Synaptic Modification in Dendritic Spines;35
7.1.5;V. Summary;39
7.1.6;Acknowledgments;39
7.1.7;References;39
7.2;Chapter 2. Canonical Neurons and Their Computational Organization;42
7.2.1;I. Historical Background for the Complex Neuron;43
7.2.2;II. Development of the Computational Representation of the Complex Neuron;45
7.2.3;III. Strategies for Neuronal Modeling;49
7.2.4;IV. The Concept of the Canonical Neuron;50
7.2.5;V. Hierarchical Organization of Canonical Neurons in the Olfactory System;52
7.2.6;VI. The Cortical Pyramidal Neuron;60
7.2.7;Acknowledgments;69
7.2.8;References;70
7.3;Chapter 3. Computational Models of Hippocampal Neurons;76
7.3.1;I. Neuromorphometry;77
7.3.2;II. Electrotonic Structure;79
7.3.3;III. Computer Simulations;82
7.3.4;IV. Methods and Results;88
7.3.5;V. Summary and Conclusions;90
7.3.6;Acknowledgment;91
7.3.7;References;91
7.4;Chapter 4. Hebbian Computations in Hippocampal Dendrites and Spines;96
7.4.1;I. Introduction;96
7.4.2;II. Nodes and Neurons;97
7.4.3;III. Voltage Gradients in Dendrites and Spines;108
7.4.4;IV. Spatial Representation of Electrotonic Structure;117
7.4.5;V. Voltage-Dependent Synaptic Modification;121
7.4.6;VI. Self-Organization and Pattern Association;124
7.4.7;VII. Summary and Conclusions;128
7.4.8;Acknowledgments;128
7.4.9;References;128
7.5;Chapter 5. Synaptic Integration by Electro-Diffusion in Dendritic Spines;132
7.5.1;I. Introduction;132
7.5.2;II. Cable Model Predictions;133
7.5.3;III. Limitations of the Cable Model;134
7.5.4;IV. Electro-Diffusion Model Predictions;136
7.5.5;V. The Cable Model for Electro-Diffusion;146
7.5.6;VI. Discussion;148
7.5.7;Acknowledgments;152
7.5.8;References;152
7.6;Chapter 6. Dendritic Morphology, Inward Rectification, and the Functional Properties of Neostriatal Neurons;156
7.6.1;I. Introduction;156
7.6.2;II. Firing Pattern of Neostriatal Spiny Projection Neurons;158
7.6.3;III. Distribution of Synaptic Inputs on the Spiny Projection Neuron;158
7.6.4;IV. A Model of the Spiny Neuron;162
7.6.5;V. Input Resistance and Electrotonic Length of the Passive Model;163
7.6.6;VI. Effect of Fast Anomalous Rectification on Input Resistance and Time Constant;164
7.6.7;VII. If the Time Constant Is Not Constant, the Length Constant Is Not Either;171
7.6.8;VIII. Synaptic Integration in the Spiny Neuron;172
7.6.9;IX. Dendritic Spines and Synaptic Strength;173
7.6.10;X. Effect of Fast Anomalous Rectification on Synaptic Integration;177
7.6.11;XI. Implications for Neostriatal Function;180
7.6.12;Acknowledgments;183
7.6.13;References;183
7.7;Chapter 7. Analog and Digital Processing in Single Nerve Cells: Dendritic Integration and Axonal Propagation;188
7.7.1;I. Introduction;188
7.7.2;II. Methods;189
7.7.3;III. Results;196
7.7.4;IV. Discussion;206
7.7.5;Acknowledgment;209
7.7.6;References;209
7.8;Chapter 8. Functions of Very Distal Dendrites: Experimental and Computational Studies of Layer I Synapses on Neocortical Pyramidal Cells;214
7.8.1;I. The Significance of Cortical Layer I;214
7.8.2;II. The Synaptic Response to Activation of Horizontal Layer I Afferents;217
7.8.3;III. Computational Model of a Layer V Cell: Determination of Parameters;222
7.8.4;IV. Steady-State and Transient Responses of the Modeled Pyramidal Neuron;230
7.8.5;V. Efficacy and Mechanisms of Synaptic Inputs to Layer I;236
7.8.6;VI. Summary;239
7.8.7;Acknowledgments;240
7.8.8;References;240
8;PART II: ION CHANNELS AND PATTERNED DISCHARGE, SYNAPSES, AND NEURONAL SELECTIVITY;246
8.1;Chapter 9. Ionic Currents Governing Input–Output Relations of Betz Cells;250
8.1.1;I. Introduction;250
8.1.2;II. Persistent Sodium Current;254
8.1.3;III. Sodium-Dependent Potassium Current;256
8.1.4;IV. Calcium-Dependent Potassium Currents;258
8.1.5;V. Calcium-Dependent Cation Current;263
8.1.6;VI. Slow Inward Cation Current;264
8.1.7;VII. Voltage-Gated Potassium Currents;266
8.1.8;VIII. Conclusions;269
8.1.9;References;271
8.2;Chapter 10. Determination of State-Dependent Processing in Thalamus by Single Neuron Properties and Neuromodulators;274
8.2.1;I. Introduction;274
8.2.2;II. Electrophysiological Properties of Thalamic Neurons;276
8.2.3;III. Neuromodulation of Thalamic Neuronal Activity;282
8.2.4;IV. Computational Simulation of Thalamic Neuronal Activity;284
8.2.5;V. Functional Implications of Multistate Neuronal Activity;300
8.2.6;VI. Conclusions;303
8.2.7;Acknowledgments;303
8.2.8;References;303
8.3;Chapter 11. Temporal Information Processing in Synapses, Cells, and Circuits;306
8.3.1;I. Introduction;306
8.3.2;II. Physiological Models of Cellular PSPs;308
8.3.3;III. Physiological Modeling of Temporal Integrative Properties;316
8.3.4;IV. Discussion;326
8.4;Chapter 12. Multiplying with Synapses and Neurons;334
8.4.1;I. Introduction;334
8.4.2;II. Why Multiplications?;335
8.4.3;III. Multiplication: Biophysical Mechanisms;344
8.4.4;IV. Conclusion;358
8.4.5;Acknowledgment;359
8.4.6;References;359
8.5;Chapter 13. A Model of the Directional Selectivity Circuit in Retina: Transformations by Neurons Singly and in Concert;366
8.5.1;I. Introduction;366
8.5.2;II. Overview of Directional Selectivity and the Retina;367
8.5.3;III. A Model of DS Output of Amacrine Cell Dendrite Tips;371
8.5.4;IV. Predictions of the Model;377
8.5.5;V. Simulations of Morphometrically and Biophysically Detailed Amacrine Cell Models;379
8.5.6;VI. Intracellular DS Recordings with Local Block of Inhibition;384
8.5.7;VII. Development of DS: The Problem of Coordination of Asymmetries;388
8.5.8;VIII. Retinal Directional Selectivity: Exemplar of a Canonical Computational Mechanism?;390
8.5.9;IX. Conclusions;391
8.5.10;Acknowledgments;391
8.5.11;References;391
9;PART III: NEURONS IN THEIR NETWORKS;396
9.1;Chapter 14. Exploring Cortical Microcircuits: A Combined Anatomical, Physiological, and Computational Approach;400
9.1.1;I. Introduction;400
9.1.2;II. Abstraction of Single Cortical Neurons;402
9.1.3;III. Exploring Neuronal Interactions;410
9.1.4;IV. Conclusion;427
9.1.5;Acknowledgments;428
9.1.6;Reference;428
9.2;Chapter 15. Evolving Analog VLSI Neurons;432
9.2.1;I. Introduction;432
9.2.2;II. Interface;433
9.2.3;III. Communication;435
9.2.4;IV. Neurons;438
9.2.5;V. Synapses;441
9.2.6;VI. Neurons that Learn Sequence;447
9.2.7;VII. Summary;451
9.2.8;Acknowledgments;452
9.2.9;References;452
9.3;Chapter 16. Relations between the Dynamical Properties of Single Cells and Their Networks in Piriform (Olfactory) Cortex;456
9.3.1;I. Introduction;456
9.3.2;II. The Olfactory System as a Model Cerebral Cortical Sensory Network;458
9.3.3;III. Modeling Olfactory Cortex;459
9.3.4;IV. Functional Significance of Patterns of Dendritic Activation;471
9.3.5;V. Conclusion;476
9.3.6;Acknowledgments;477
9.3.7;References;477
9.4;Chapter 17. Synchronized Multiple Bursts in the Hippocampus: A Neuronal Population Oscillation Uninterpretable without Accurate Cellular Membrane Kinetics;482
9.4.1;I. Introduction;482
9.4.2;II. Synchronized Multiple Bursts (Afterdischarges) in Disinhibited Hippocampal Slices;484
9.4.3;III. Considerations on the Mechanisms of SMB;485
9.4.4;IV. Hypotheses as to the Biological Significance of SMB;490
9.4.5;V. Conclusion;490
9.4.6;References;491
10;PART IV: MULTISTATE NEURONS AND STOCHASTIC MODELS OF NEURON DYNAMICS;496
10.1;Chapter 18. Signal Processing in Multi-Threshold Neurons;500
10.1.1;I. Introduction;500
10.1.2;II. Representation of Neuronal Signals;501
10.1.3;III. Spike Codes in Neurons;502
10.1.4;IV. Multiple Thresholds in Neurons;503
10.1.5;V. Functional Significance of Multi-Threshold Neurons;506
10.1.6;VI. Summary;517
10.1.7;Acknowledgment;518
10.1.8;References;518
10.2;Chapter 19. Cooperative Stochastic Effects in a Model of a Single Neuron;522
10.2.1;I. Introduction;522
10.2.2;II. The Single Effective Neuron;526
10.2.3;III. Response to Weak Modulation: Stochastic Resonance;532
10.2.4;IV. Discussion;536
10.2.5;Acknowledgment;539
10.2.6;References;539
10.3;Chapter 20. Critical Coherence and Characteristic Times in Brain Stem Neuronal Discharge Patterns;544
10.3.1;I. Introduction;544
10.3.2;II. Temporal Complexity as a Characteristic of Normal Neuronal Behavior;545
10.3.3;III. Elemental Mechanics of Single-Neuron Activation;550
10.3.4;IV. Time Scaling and Entropies in Intermittent Neuronal Activities;555
10.3.5;V. Databases and Numerical Computations;559
10.3.6;VI. Interspike Interval Patterns;560
10.3.7;VII. Discussion;568
10.3.8;VIII. G(t) as a Global Characteristic Time;572
10.3.9;Acknowledgment;573
10.3.10;References;573
10.4;Chapter 21. A Heuristic Approach to Stochastic Models of Single Neurons;580
10.4.1;I. Introduction;580
10.4.2;II. First-Passage Times as Neural Firing Times;585
10.4.3;Acknowledgments;604
10.4.4;References;604
10.5;Chapter 22. Fractal Neuronal Firing Patterns;608
10.5.1;I. Introduction;608
10.5.2;II. Self-Similarity of Neuronal Firing Rates;612
10.5.3;IV. Fractal Dimension of the Firing Pattern;618
10.5.4;V. Alteration of the Firing Pattern Engendered by Stimulation;618
10.5.5;VI. Comparison of Auditory and Vestibular Firing Patterns;620
10.5.6;VII. Fractal Firing Patterns at Higher Auditory Centers;622
10.5.7;VIII. Neural Information Processing with Fractal Events;623
10.5.8;IX. Biophysical Origins of the Fractal Behavior;624
10.5.9;X. Identifying the Mathematical Point Process;624
10.5.10;Acknowledgments;641
10.5.11;References;641
11;Index;646