Single Neuron Computation (eBook)

Joel L. Davis, Thomas M. McKenna, Steven F. Zornetzer (Herausgeber)

eBook Download: PDF

2014 | 1. Auflage
644 Seiten
Elsevier Science (Verlag)
978-1-4832-9606-7 (ISBN)

Infos

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.

Inhalt

Front Cover 1
Single Neuron Computation 4
Copyright Page 5
Table of Contents 6
Contributors 10
Preface 14
PART I: COMPUTATION IN DENDRITES AND SPINES 16
Chapter 1. Electrotonic Models of Neuronal Dendrites and Single Neuron Computation 22
I. Introduction 22
II. Estimating the Electrotonic Structure of a Cell 23
III. The Dynamic Range of Computational Possibilities Exhibited by Neurons 31
IV. Synaptic Modification in Dendritic Spines 35
V. Summary 39
Acknowledgments 39
References 39
Chapter 2. Canonical Neurons and Their Computational Organization 42
I. Historical Background for the Complex Neuron 43
II. Development of the Computational Representation of the Complex Neuron 45
III. Strategies for Neuronal Modeling 49
IV. The Concept of the Canonical Neuron 50
V. Hierarchical Organization of Canonical Neurons in the Olfactory System 52
VI. The Cortical Pyramidal Neuron 60
Acknowledgments 69
References 70
Chapter 3. Computational Models of Hippocampal Neurons 76
I. Neuromorphometry 77
II. Electrotonic Structure 79
III. Computer Simulations 82
IV. Methods and Results 88
V. Summary and Conclusions 90
Acknowledgment 91
References 91
Chapter 4. Hebbian Computations in Hippocampal Dendrites and Spines 96
I. Introduction 96
II. Nodes and Neurons 97
III. Voltage Gradients in Dendrites and Spines 108
IV. Spatial Representation of Electrotonic Structure 117
V. Voltage-Dependent Synaptic Modification 121
VI. Self-Organization and Pattern Association 124
VII. Summary and Conclusions 128
Acknowledgments 128
References 128
Chapter 5. Synaptic Integration by Electro-Diffusion in Dendritic Spines 132
I. Introduction 132
II. Cable Model Predictions 133
III. Limitations of the Cable Model 134
IV. Electro-Diffusion Model Predictions 136
V. The Cable Model for Electro-Diffusion 146
VI. Discussion 148
Acknowledgments 152
References 152
Chapter 6. Dendritic Morphology, Inward Rectification, and the Functional Properties of Neostriatal Neurons 156
I. Introduction 156
II. Firing Pattern of Neostriatal Spiny Projection Neurons 158
III. Distribution of Synaptic Inputs on the Spiny Projection Neuron 158
IV. A Model of the Spiny Neuron 162
V. Input Resistance and Electrotonic Length of the Passive Model 163
VI. Effect of Fast Anomalous Rectification on Input Resistance and Time Constant 164
VII. If the Time Constant Is Not Constant, the Length Constant Is Not Either 171
VIII. Synaptic Integration in the Spiny Neuron 172
IX. Dendritic Spines and Synaptic Strength 173
X. Effect of Fast Anomalous Rectification on Synaptic Integration 177
XI. Implications for Neostriatal Function 180
Acknowledgments 183
References 183
Chapter 7. Analog and Digital Processing in Single Nerve Cells: Dendritic Integration and Axonal Propagation 188
I. Introduction 188
II. Methods 189
III. Results 196
IV. Discussion 206
Acknowledgment 209
References 209
Chapter 8. Functions of Very Distal Dendrites: Experimental and Computational Studies of Layer I Synapses on Neocortical Pyramidal Cells 214
I. The Significance of Cortical Layer I 214
II. The Synaptic Response to Activation of Horizontal Layer I Afferents 217
III. Computational Model of a Layer V Cell: Determination of Parameters 222
IV. Steady-State and Transient Responses of the Modeled Pyramidal Neuron 230
V. Efficacy and Mechanisms of Synaptic Inputs to Layer I 236
VI. Summary 239
Acknowledgments 240
References 240
PART II: ION CHANNELS AND PATTERNED DISCHARGE, SYNAPSES, AND NEURONAL SELECTIVITY 246
Chapter 9. Ionic Currents Governing Input–Output Relations of Betz Cells 250
I. Introduction 250
II. Persistent Sodium Current 254
III. Sodium-Dependent Potassium Current 256
IV. Calcium-Dependent Potassium Currents 258
V. Calcium-Dependent Cation Current 263
VI. Slow Inward Cation Current 264
VII. Voltage-Gated Potassium Currents 266
VIII. Conclusions 269
References 271
Chapter 10. Determination of State-Dependent Processing in Thalamus by Single Neuron Properties and Neuromodulators 274
I. Introduction 274
II. Electrophysiological Properties of Thalamic Neurons 276
III. Neuromodulation of Thalamic Neuronal Activity 282
IV. Computational Simulation of Thalamic Neuronal Activity 284
V. Functional Implications of Multistate Neuronal Activity 300
VI. Conclusions 303
Acknowledgments 303
References 303
Chapter 11. Temporal Information Processing in Synapses, Cells, and Circuits 306
I. Introduction 306
II. Physiological Models of Cellular PSPs 308
III. Physiological Modeling of Temporal Integrative Properties 316
IV. Discussion 326
Chapter 12. Multiplying with Synapses and Neurons 334
I. Introduction 334
II. Why Multiplications? 335
III. Multiplication: Biophysical Mechanisms 344
IV. Conclusion 358
Acknowledgment 359
References 359
Chapter 13. A Model of the Directional Selectivity Circuit in Retina: Transformations by Neurons Singly and in Concert 366
I. Introduction 366
II. Overview of Directional Selectivity and the Retina 367
III. A Model of DS Output of Amacrine Cell Dendrite Tips 371
IV. Predictions of the Model 377
V. Simulations of Morphometrically and Biophysically Detailed Amacrine Cell Models 379
VI. Intracellular DS Recordings with Local Block of Inhibition 384
VII. Development of DS: The Problem of Coordination of Asymmetries 388
VIII. Retinal Directional Selectivity: Exemplar of a Canonical Computational Mechanism? 390
IX. Conclusions 391
Acknowledgments 391
References 391
PART III: NEURONS IN THEIR NETWORKS 396
Chapter 14. Exploring Cortical Microcircuits: A Combined Anatomical, Physiological, and Computational Approach 400
I. Introduction 400
II. Abstraction of Single Cortical Neurons 402
III. Exploring Neuronal Interactions 410
IV. Conclusion 427
Acknowledgments 428
Reference 428
Chapter 15. Evolving Analog VLSI Neurons 432
I. Introduction 432
II. Interface 433
III. Communication 435
IV. Neurons 438
V. Synapses 441
VI. Neurons that Learn Sequence 447
VII. Summary 451
Acknowledgments 452
References 452
Chapter 16. Relations between the Dynamical Properties of Single Cells and Their Networks in Piriform (Olfactory) Cortex 456
I. Introduction 456
II. The Olfactory System as a Model Cerebral Cortical Sensory Network 458
III. Modeling Olfactory Cortex 459
IV. Functional Significance of Patterns of Dendritic Activation 471
V. Conclusion 476
Acknowledgments 477
References 477
Chapter 17. Synchronized Multiple Bursts in the Hippocampus: A Neuronal Population Oscillation Uninterpretable without Accurate Cellular Membrane Kinetics 482
I. Introduction 482
II. Synchronized Multiple Bursts (Afterdischarges) in Disinhibited Hippocampal Slices 484
III. Considerations on the Mechanisms of SMB 485
IV. Hypotheses as to the Biological Significance of SMB 490
V. Conclusion 490
References 491
PART IV: MULTISTATE NEURONS AND STOCHASTIC MODELS OF NEURON DYNAMICS 496
Chapter 18. Signal Processing in Multi-Threshold Neurons 500
I. Introduction 500
II. Representation of Neuronal Signals 501
III. Spike Codes in Neurons 502
IV. Multiple Thresholds in Neurons 503
V. Functional Significance of Multi-Threshold Neurons 506
VI. Summary 517
Acknowledgment 518
References 518
Chapter 19. Cooperative Stochastic Effects in a Model of a Single Neuron 522
I. Introduction 522
II. The Single Effective Neuron 526
III. Response to Weak Modulation: Stochastic Resonance 532
IV. Discussion 536
Acknowledgment 539
References 539
Chapter 20. Critical Coherence and Characteristic Times in Brain Stem Neuronal Discharge Patterns 544
I. Introduction 544
II. Temporal Complexity as a Characteristic of Normal Neuronal Behavior 545
III. Elemental Mechanics of Single-Neuron Activation 550
IV. Time Scaling and Entropies in Intermittent Neuronal Activities 555
V. Databases and Numerical Computations 559
VI. Interspike Interval Patterns 560
VII. Discussion 568
VIII. G(t) as a Global Characteristic Time 572
Acknowledgment 573
References 573
Chapter 21. A Heuristic Approach to Stochastic Models of Single Neurons 580
I. Introduction 580
II. First-Passage Times as Neural Firing Times 585
Acknowledgments 604
References 604
Chapter 22. Fractal Neuronal Firing Patterns 608
I. Introduction 608
II. Self-Similarity of Neuronal Firing Rates 612
IV. Fractal Dimension of the Firing Pattern 618
V. Alteration of the Firing Pattern Engendered by Stimulation 618
VI. Comparison of Auditory and Vestibular Firing Patterns 620
VII. Fractal Firing Patterns at Higher Auditory Centers 622
VIII. Neural Information Processing with Fractal Events 623
IX. Biophysical Origins of the Fractal Behavior 624
X. Identifying the Mathematical Point Process 624
Acknowledgments 641
References 641
Index 646

Produkt-Details

• Erscheint lt. Verlag: 19.5.2014
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Grafik / Design
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• ISBN-10: 1-4832-9606-7 / 1483296067
• ISBN-13: 978-1-4832-9606-7 / 9781483296067