Introduction to Dynamic Modeling of Neuro-Sensory Systems

Introduction to Dynamic Modeling of Neuro-Sensory Systems.pdf......Page 1
Introduction to Dynamic Modeling of Neuro-Sensory Systems......Page 2
Preface......Page 5
Author......Page 10
Table of Contents......Page 12
1.1 Types of Neurons......Page 18
1.1.1 Motoneurons......Page 19
1.1.2 Vertebrate Peripheral Sensory Neurons......Page 21
1.1.3 Neuroendocrine Cells......Page 22
1.1.4 Interneurons......Page 23
1.2 Electrical Properties of Nerve Membrane......Page 25
1.2.1 The Source of UM Electrical Parameters......Page 28
1.2.2 Decremental Conduction on Dendrites: The Space Constant......Page 29
1.2.3 Active Membrane: The Nerve Spike......Page 32
1.2.4 Saltatory Conduction on Myelinated Axons......Page 34
1.2.5 Discussion......Page 38
1.3 Synapses: EPSPs and IPSPs......Page 39
1.3.1 Chemical Synapses......Page 41
1.3.2 Electrical Synapses......Page 45
1.3.3 epsps and ipsps......Page 46
1.3.4 Quantal Release of Neurotransmitter......Page 50
1.3.5 Discussion......Page 51
1.4 Models for the Nerve Action Potential......Page 52
1.4.1 The 1952 Hodgkin-Huxley Model for Action Potential Generation......Page 53
1.4.2 Properties of the Hodgkin-Huxley Model......Page 58
1.4.3 Extended Hodgkin-Huxley Models......Page 62
1.5 Chapter Summary......Page 70
Problems......Page 71
References......Page 0
Introduction......Page 80
2.1 The Generalized Receptor......Page 81
2.1.1 Dynamic Response......Page 82
2.1.3 Receptor Sensitivity......Page 83
2.1.4 A Model for Optimum Firing Threshold......Page 85
2.1.5 Simulation of a Model Receptor with a Continuously Variable Firing Threshold......Page 86
2.2 Chemoreceptors......Page 91
2.2.1 The Vertebrate Olfactory Chemoreceptor......Page 93
2.2.2 Olfaction in Arthropods......Page 96
2.3 Mechanoreceptors......Page 100
2.3.1 Insect Trichoid Hairs......Page 101
2.3.2 Insect Campaniform Sensilla......Page 104
2.3.3 Muscle Length Receptors......Page 105
2.3.4 Muscle Force Receptors......Page 115
2.3.5 Statocysts......Page 119
2.3.6 Pacinian Corpuscles......Page 124
2.3.7 Discussion......Page 125
2.4 Magnetoreceptors......Page 127
2.4.1 Behavioral Evidence for Magnetic Sensing......Page 128
2.4.3.1 The Magnetic Compass Analog......Page 130
2.4.3.2 A Hall Effect Analog......Page 131
2.4.3.3 A Lorentz Force Mechanism in Vertebrate Photoreceptors......Page 132
2.4.3.4 A Magnetosensory System Based on the Faraday Streaming Effect......Page 133
2.4.4 Discussion......Page 135
2.5.1 Ampullary Receptors......Page 136
2.5.2 Weakly Electric Fish and Knollenorgans......Page 139
2.5.3 Discussion......Page 141
2.6 Gravity Receptors of the Cockroach, Arenivaga sp.......Page 142
2.6.1 Hartman’s Methods......Page 144
2.6.2 Hartman’s Results......Page 145
2.6.4 Willey’s Methods......Page 149
2.6.5 Willey’s Results......Page 150
2.6.6 A Tentative Model for PCP Unit Narrow Sensitivity......Page 153
2.7 The Dipteran Haltere......Page 158
2.7.1 The Torsional Vibrating Mass Gyro......Page 159
2.8 The Simple “Eye” of Mytilus edulis......Page 164
2.8.1 Eye Morphology......Page 165
2.8.2 Physiology of the Eye......Page 167
2.8.3 Discussion......Page 168
2.9 Chapter Summary......Page 169
Problems......Page 170
3.1 Necessary Attributes of Small- and Medium-Scale Neural Models......Page 177
3.2 Electronic Neural Models (Neuromimes)......Page 179
3.3 Discussion......Page 181
Introduction......Page 182
4.1 Simulation of Synaptic Loci......Page 183
4.1.1 A Linear Model for psp Generation......Page 184
4.1.2 A Model for epsp Production Based on Chemical Kinetics......Page 186
4.1.3 A Model for a Faciltating Synapse......Page 188
4.1.4 A Model for an Antifacilitating Synapse......Page 189
4.1.5 Inhibitory Synapses......Page 191
4.1.6 Discussion......Page 194
4.2 Dendrites and Local Response Loci......Page 196
4.2.1 The Core-Conductor Transmission Line......Page 197
Example 4.2-1......Page 200
4.3.1 IPFM......Page 203
4.3.2 RPFM......Page 205
Example 4.3-1......Page 206
4.3.3 Modeling Adaptation......Page 209
4.3.5 Discussion......Page 210
4.4 Theoretical Models for Neural Signal Conditioning......Page 211
4.4.1 The T-Neuron......Page 212
4.4.2 A Theoretical Band-Pass Structure: The Band Detector......Page 214
4.4.3 Discussion......Page 218
4.5.1 The Basic RI System......Page 219
4.5.2 Szentagothai’s RI Circuit......Page 225
4.5.3 A simple Burst Generator......Page 226
4.5.4 A Ring CPG Model with Negative Feedback......Page 228
4.6 Chapter Summary......Page 236
Problems......Page 237
Introduction......Page 248
5.1 Anatomy of the Arthropod Compound Eye Visual System......Page 249
5.1.1 Retinula Cells and Rhabdoms......Page 252
5.1.2 The Optic Lobes......Page 255
5.1.3 The Optics of the Compound Eye......Page 259
5.1.4 Discussion......Page 264
5.2 Spatial Resolution of the Compound Eye......Page 265
5.2.1 The Compound Eye as a Two-Dimensional, Spatial Sampling Array......Page 267
Example 5.2-1......Page 272
Example 5.2-2......Page 274
5.2.3 “Anomalous Resolution”......Page 278
5.2.4 A Model for Contrast Enhancement in the Insect Visual System......Page 280
5.2.5 A Hypothetical Model for Spatial Resolution Improvement in the Compound Eye by Synthetic Aperture......Page 283
Example 5.2-3......Page 292
5.3 Lateral Inhibition in the Eye of Limulus......Page 294
5.3.1 Evidence for Lateral Inhibition......Page 297
5.3.2 Modeling Lateral Inhibition as a Spatial Filter for Objects......Page 298
Example 5.5-1......Page 300
Example 5.5-2......Page 301
Example 5.5-3......Page 303
5.4 Feature Extraction by the Compound Eye System......Page 304
5.4.1 Feature Extraction by Optic Lobe Units of Romalea......Page 306
1. Multimodal Units......Page 307
2. Directionally Sensitive, Contrasting Edge Detectors......Page 308
5. Unmodulatable and Weakly Sensitive Units......Page 309
5.4.2 Feature Extraction by Optic Lobe Units in Flies......Page 310
5.4.3 Eye Movements and Visual Tracking in Flies......Page 313
5.4.4 Feature Extraction by Optic Lobe Units of Crustaceans......Page 321
5.4.5 Discussion......Page 325
5.5 Chapter Summary......Page 326
Problems......Page 327
6.1 Review of the Anatomy and Physiology of the Vertebrate Retina......Page 337
6.2.1 Early Work......Page 341
6.2.2 Directionally Sensitive Neurons in the Frog’s Brain......Page 344
6.3.1 The Pigeon Retina......Page 346
6.3.2 The Rabbit Retina......Page 349
6.4 Chapter Summary......Page 352
Introduction......Page 353
7.1.1 The Logic-Based, Spatiotemporal Filter Approach of Zorkoczy......Page 354
Example 7.1-1......Page 357
Example 7.1-2......Page 358
7.1.2 Analog Models for Motion Detection in Insects......Page 359
7.1.3 Continuous, Layered Visual Feature Extraction Filters......Page 367
7.1.4 Discussion......Page 373
7.2.1 The Continuous, One-Dimensional Spatial Matched Filter......Page 374
Example 7.2-1......Page 376
Example 7.2-2......Page 378
7.2.2 The Continuous, Two-Dimensional, Spatial Matched Filter......Page 379
7.2.3 Discussion......Page 380
7.3 Models for Parallel Processing: Artificial Neural Networks......Page 381
7.3.1 Rosenblatt’s Perceptron......Page 382
7.3.2 Widrow’s ADALINE and MADALINE......Page 384
7.3.3 Fukushima’s Neocognitron......Page 386
7.4 Chapter Summary......Page 390
Problems......Page 391
Review of Characterization and Identification Means for Linear Systems......Page 395
8.1 Parsimonious Models for Neural Connectivity Based on Time Series Analysis of Spike Sequences......Page 401
8.1.1 The JPST Diagram......Page 402
8.1.2 Discussion......Page 409
8.2 Triggered Correlation Applied to the Auditory System......Page 410
8.2.1 Development of an Expression for the Conditional Expectation, x+(t)......Page 412
8.2.2 Optimum Conditions for Application of the TC Algorithm......Page 415
8.2.3 Electronic Model Studies of TC......Page 418
8.2.4 Neurophysiological Studies of Auditory Systems Using TC......Page 420
8.2.5 Summary......Page 423
8.3 The White Noise Method of Characterizing Nonlinear Systems......Page 424
8.3.1 The Lee-Schetzen Approach to White Noise Analysis......Page 426
8.3.2 Practical Aspects of Implementing the Lee-Schetzen White Noise Analysis......Page 428
8.3.3 Applications of the White Noise Method to Neurobiological Systems......Page 429
8.4 Chapter Summary......Page 436
Introduction......Page 440
9.1 XNBC v8......Page 443
9.2 Neural Network Simulation Language, or NSL......Page 444
9.3 Neuron......Page 445
9.5.1 EONS......Page 447
9.5.4 Nodus 3.2......Page 448
9.6 Neural Modeling with General, Nonlinear System Simulation Software......Page 449
9.6.2 Simulink®......Page 450
9.7 Conclusion......Page 451
Appendix 1......Page 453
Appendix 2......Page 459
Appendix 3......Page 463
Bibliography and References......Page 466