The NEURON book:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2006
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents only Contributor biographical information Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XIX, 457 S. graph. Darst. 24 cm |
| ISBN: | 0521843219 9780521115636 9780521843218 |
Internformat
MARC
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| 100 | 1 | |a Carnevale, Nicholas T. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The NEURON book |c Ted Carnevale, Michael Hines |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2006 | |
| 300 | |a XIX, 457 S. |b graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 630 | 0 | 4 | |a NEURON (Computer file) |
| 650 | 4 | |a Neurons |x Computer simulation | |
| 650 | 4 | |a Neural networks (Neurobiology) |x Computer simulation | |
| 650 | 0 | 7 | |a Nervenzelle |0 (DE-588)4041649-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computersimulation |0 (DE-588)4148259-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
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| 689 | 0 | 2 | |a Computersimulation |0 (DE-588)4148259-1 |D s |
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| 700 | 1 | |a Hines, Michael |e Verfasser |4 aut | |
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| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0661/2006277066-t.html |3 Table of contents only | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0733/2006277066-b.html |3 Contributor biographical information | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016995712&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016995712 | ||
Datensatz im Suchindex
| _version_ | 1804138366377132032 |
|---|---|
| adam_text | Table of contents
It is some systematized exhibition of the whale in his broad genera, that
I would now fain put before you. Yet is it no easy task. The classification of the
constituents of a chaos, nothing less is here essayed.
Preface xvii
Acknowledgments xix
1 A tour of the NEURON simulation environment 1
1.1 Modeling and understanding 1
1.2 Introducing NEURON 1
1.3 State the question 2
1.4 Formulate a conceptual model 3
1.5 Implement the model in NEURON 5
1.5.1 Starting and stopping NEURON 6
1.5.2 Bringing up a CellBuilder 6
1.5.3 Entering the specifications of the model cell 8
1.5.3.1 Topology 8
1.5.3.2 Subsets 10
1.5.3.3 Geometry 12
1.5.3.4 Biophysics 13
1.5.4 Saving the model cell 16
1.5.5 Executing the model specification 16
1.6 Instrument the model 18
1.6.1 Signal sources 18
1.6.2 Signal monitors 20
1.7 Set up controls for running the simulation 21
1.8 Save model with instrumentation and run control 21
1.9 Run the simulation experiment 23
1.10 Analyze results 24
References 30
2 The modeling perspective 32
2.1 Why model? 32
2.2 From physical system to computational model 33
2.2.1 Conceptual model: a simplified
representation of a physical system 33
vi Table of contents
2.2.2 Computational model: an accurate
representation of a conceptual model 33
2.2.3 An example 34
3 Expressing conceptual models in mathematical terms 36
3.1 Chemical reactions 36
3.1.1 Flux and conservation in kinetic schemes 37
3.1.2 Stoichiometry, flux, and mole equivalents 39
3.1.3 Compartment size 41
3.1.3.1 Scale factors 43
3.2 Electrical circuits 44
3.3 Cables 50
References 54
4 Essentials of numerical methods for neural modeling 55
4.1 Spatial and temporal error in discretized cable equations 56
4.1.1 Analytic solutions: continuous in time
and space 56
4.1.2 Spatial discretization 57
4.1.3 Adding temporal discretization 60
4.2 Numerical integration methods 62
4.2.1 Forward Euler: simple, inaccurate and
unstable 62
4.2.1.1 Numerical instability 64
4.2.2 Backward Euler: inaccurate but stable 66
4.2.3 Crank-Nicholson: stable and more accurate 68
4.2.3.1 Efficient handling of nonlinearity 70
4.2.4 Adaptive integration: fast or accurate,
occasionally both 72
4.2.4.1 Implementational considerations 73
4.2.4.2 The user s perspective 75
4.2.4.3 Local variable time step method 80
4.2.4.4 Discrete event simulations 83
4.3 Error 83
4.4 Summary of NEURON S integration methods 86
4.4.1 Fixed time step integrators 86
4.4.1.1 Default: backward Euler 86
4.4.1.2 Crank-Nicholson 86
4.4.2 Adaptive integrators 87
Table of contents vii
4.4.2.1 CVODE 88
4.4.2.2 DASPK 88
References 88
5 Representing neurons with a digital computer 90
5.1 Discretization 90
5.2 How NEURON separates anatomy and biophysics
from purely numerical issues 92
5.2.1 Sections and section variables 92
5.2.2 Range and range variables 93
5.2.3 Segments 95
5.2.4 Implications and applications of this strategy 96
5.2.4.1 Spatial accuracy 96
5.2.4.2 A practical test of spatial accuracy 97
5.3 How to specify model properties 98
5.3.1 Which section do we mean? 98
5.3.1.1 Dot notation 99
5.3.1.2 Section stack 99
5.3.1.3 Default section 100
5.4 How to set up model topology 101
5.4.1 Loops of sections 101
5.4.2 A section may have only one parent 101
5.4.3 The root section 101
5.4.4 Attach sections at 0 or 1 for accuracy 102
5.4.5 Checking the tree structure with
topology() 102
5.4.6 Viewing topology with a Shape plot 103
5.5 How to specify geometry 103
5.5.1 Stylized specification 104
5.5.2 3-D specification 105
5.5.3 Avoiding artifacts 107
5.5.3.1 Beware of zero diameter 107
5.5.3.2 Stylized specification may be
reinterpreted as 3-D specification 108
5.6 How to specify biophysical properties 111
5.6.1 Distributed mechanisms 111
5.6.2 Point processes 112
5.6.3 User-defined mechanisms 113
5.6.4 Working with range variables 114
5.6.4.1 Iterating over nodes 114
I
viii Table of contents
5.6.4.2 Linear taper 115
5.6.4.3 How changing nseg affects range
variables 115
5.7 Choosing a spatial grid 117
5.7.1 A consideration of intent and judgment 118
5.7.2 Discretization guidelines 121
5.7.2.1 The djambda rule 122
References 126
6 How to build and use models of individual cells 128
6.1 Graphical user interface vs. hoc code:
which to use, and when? 128
6.2 Hidden secrets of the GUI 129
6.3 Implementing a model with hoc 130
6.3.1 Topology 130
6.3.2 Geometry 132
6.3.3 Biophysics 133
6.3.4 Testing the model implementation 133
6.3.5 An aside: how does our model implementation
in hoc compare with the output of the
CellBuilder? 135
6.4 Instrumenting a model with hoc 139
6.5 Setting up simulation control with hoc 139
6.5.1 Testing simulation control 141
6.6 Evaluating and using the model 141
6.7 Combining hoc and the GUI 141
6.7.1 No NEURON Main Menu toolbar? 142
6.7.2 Default section? We ain t got no default
section! 142
6.7.3 Strange Shapes? 144
6.7.3.1 The barbed wire model 144
6.7.3.2 The case of the disappearing section 148
6.7.4 Graphs don t work? 151
6.7.5 Conflicts between hoc code and GUI tools 152
6.8 Elementary project management 154
6.8.1 Iterative program development 155
Reference 156
7 How to control simulations 157
7.1 Simulation control with the graphical user interface 157
7.2 The standard run system 159
Table of contents ix
7.2.1 An outline of the standard ran system 160
7.2.1.1 fadvanceO 160
7.2.1.2 advance () 161
7.2.1.3 stepO 161
7.2.1.4 steprunO and continuerun () 162
7.2.1.5 run() 163
7.3 Details of f advance () 164
7.3.1 The fixed step methods: backward Euler and
Crank-Nicholson 165
7.3.2 Adaptive integrators 171
7.3.2.1 Local time step integration with
discrete events 173
7.3.2.2 Global time step integration with
discrete events 179
7.4 Incorporating Graphs and new objects into the
plotting system 179
References 181
8 How to initialize simulations 183
8.1 State variables and STATE variables 183
8.2 Basic initialization in NEURON: f initialize () 185
8.3 Default initialization in the standard ran system:
stdinit() and init() 187
8.3.1 INITIAL blocks in NMODL 188
8.3.1.1 Default vs. explicit initialization
of STATES 190
8.3.1.2 Ion concentrations and equilibrium
potentials 190
8.4 Examples of custom initializations 195
8.4.1 Initializing to a particular resting potential 195
8.4.2 Initializing to steady state 197
8.4.3 Initializing to a desired state 198
8.4.4 Initializing by changing model parameters 199
8.4.4.1 Details of the mechanism 200
8.4.4.2 Initializing the mechanism 202
Reference 206
9 How to expand NEURON S library of mechanisms 207
9.1 Overview of NMODL 207
9.2 Example 9.1: A passive leak current 208
9.2.1 The NEURON block 210
x Table of contents
9.2.2 Variable declaration blocks 211
9.2.2.1 The PARAMETER block 212
9.2.2.2 The ASSIGNED block 212
9.2.3 Equation definition blocks 213
9.2.3.1 The BREAKPOINT block 213
9.2.4 Usage 214
9.3 Example 9.2: A localized shunt 214
9.3.1 The NEURON block 215
9.3.2 Variable declaration blocks 215
9.3.3 Equation definition blocks 216
9.3.3.1 The BREAKPOINT block 216
9.3.4 Usage 217
9.4 Example 9.3: An intracellular stimulating
electrode 217
9.4.1 The NEURON block 218
9.4.2 Equation definition blocks 218
9.4.2.1 The BREAKPOINT block 218
9.4.2.2 The INITIAL block 219
9.4.3 Usage 219
9.5 Example 9.4: A voltage-gated current 220
9.5.1 The NEURON block 222
9.5.2 The UNITS block 222
9.5.3 Variable declaration blocks 222
9.5.3.1 The ASSIGNED block 222
9.5.3.2 The STATE block 223
9.5.4 Equation definition blocks 223
9.5.4.1 The BREAKPOINT block 223
9.5.4.2 The INITIAL block 224
9.5.4.3 The DERIVATIVE block 225
9.5.4.4 The FUNCTION block 226
9.5.5 Usage 227
9.6 Example 9.5: A calcium-activated,
voltage-gated current 228
9.6.1 The NEURON block 230
9.6.2 The UNITS block 231
9.6.3 Variable declaration blocks 231
9.6.3.1 The AS SIGNED block 231
9.6.3.2 The STATE block 232
9.6.4 Equation definition blocks 232
9.6.4.1 The BREAKPOINT block 232
Table of contents xi
9.6.4.2 The DERIVATIVE block 232
9.6.4.3 The FUNCTION and
PROCEDURE blocks 232
9.6.5 Usage 233
9.7 Example 9.6: Extracellular potassium
accumulation 233
9.7.1 The NEURON block 235
9.7.2 Variable declaration blocks 236
9.7.2.1 The PARAMETER block 236
9.7.2.2 The STATE block 236
9.7.3 Equation definition blocks 236
9.7.3.1 The BREAKPOINT block 236
9.7.3.2 The INITIAL block 236
9.7.3.3 The DERIVATIVE block 237
9.7.4 Usage 237
9.8 General comments about kinetic schemes 238
9.9 Example 9.7: Kinetic scheme for a
voltage-gated current 240
9.9.1 The NEURON block 242
9.9.2 Variable declaration blocks 242
9.9.2.1 The STATE block 242
9.9.3 Equation definition blocks 243
9.9.3.1 The BREAKPOINT block 243
9.9.3.2 The INITIAL block 243
9.9.3.3 The KINETIC block 243
9.9.3.4 The FUNCTIONJTABLEs 244
9.9.4 Usage 245
9.10 Example 9.8: Calcium diffusion with buffering 245
9.10.1 Modeling diffusion with kinetic schemes 246
9.10.2 The NEURON block 250
9.10.3 The UNITS block 250
9.10.4 Variable declaration blocks 250
9.10.4.1 The ASSIGNED block 250
9.10.4.2 The STATE block 250
9.10.4.3 LOCAL variables
declared outside of equation
definition blocks 251
9.10.5 Equation definition blocks 251
9.10.5.1 The INITIAL block 251
9.10.5.2 PROCEDURE factors () 252
I
xii Table of contents j
9.10.5.3 The KINETIC block 252 :
9.10.6 Usage 254
9.11 Example 9.9: A calcium pump 255
9.11.1 The NEURON block 255
9.11.2 The UNITS block 256
9.11.3 Variable declaration blocks 256
9.11.3.1 The PARAMETER block 256
9.11.3.2 The ASSIGNED block 257
9.11.3.3 The CONSTANT block 257
9.11.3.4 The STATE block 257
9.11.4 Equation definition blocks 257
9.11.4.1 The BREAKPOINT block 257
9.11.4.2 The INITIAL block 258
9.11.4.3 The KINETIC block 259
9.11.5 Usage 260
9.12 Models with discontinuities 260
9.12.1 Discontinuities in PARAMETERS and
AS SIGNED variables 260
9.12.2 Discontinuities in STATES 261
9.12.3 Event handlers 263
9.13 Time-dependent PARAMETER changes 263
References 264
10 Synaptic transmission and artificial spiking cells 265
10.1 Modeling communication between cells 266
10.1.1 Example 10.1: Graded synaptic transmission 266
10.1.1.1 The NEURON block 268
10.1.1.2 The BREAKPOINT block 269
10.1.1.3 Usage 269
10.1.2 Example 10.2: A gap junction 271
10.1.2.1 Usage 272
10.1.3 Modeling spike-triggered synaptic
transmission: an event-based strategy 272
10.1.3.1 Conceptual model 273
10.1.3.2 The Net Con class 274
10.1.4 Example 10.3: Synapse with
exponential decay 277
10.1.4.1 The BREAKPOINT block 278
10.1.4.2 The DERIVATIVE block 278
10.1.4.3 The NET_RECEIVE block 278
10.1.4.4 Usage 278
Table of contents xiii
10.1.5 Example 10.4: Alpha function synapse 280
10.1.6 Example 10.5: Use-dependent synaptic
plasticity 281
10.1.6.1 TheNET_RECEIVEblock 283
10.1.7 Example 10.6: Saturating synapses 284
10.1.7.1 The PARAMETER block 287
10.1.7.2 The STATE block 287
10.1.7.3 The INITIAL block 287
10.1.7.4 The BREAKPOINT and
DERIVATIVE blocks 288
10.1.7.5 TheNET_RECEIVEblock 288
10.2 Artificial spiking cells 289
10.2.1 Example 10.7: IntFirel, a basic
integrate and fire model 290
10.2.1.1 The NEURON block 291
10.2.1.2 TheNET_RECEIVEblock 292
10.2.1.3 Enhancements to the basic
mechanism 292
10.2.2 Example 10.8: IntFire2, firing rate
proportional to input 297
10.2.2.1 Implementation in NMODL 298
10.2.3 Example 10.9: IntFire4, different
synaptic time constants 301
10.2.4 Other comments regarding artificial
spiking cells 304
References 305
11 Modeling networks 306
11.1 Building a simple network with the GUI 307
11.2 Conceptual model 308
11.3 Adding a new artificial spiking cell to
NEURON 309
11.4 Creating a prototype net with the GUI 311
11.4.1 Define the types of cells 311
11.4.2 Create each cell in the network 312
11.4.3 Connect the cells 315
11.4.3.1 Setting up network architecture 315
11.4.3.2 Specifying delays and weights 316
11.4.4 Set up instrumentation 318
11.4.5 Set up controls for running simulations 319
11.4.6 Run a simulation 322
xiv Table of contents
11.4.7 Caveats and other comments 322
11.4.7.1 Changing the properties of an
existing network 322
11.4.7.2 A word about cell names 323
11.5 Combining the GUI and programming 324
11.5.1 Creating a hoc file from the NetWork Builder 324
11.5.1.1 NetGUI default section 326
11.5.1.2 Network cell templates 326
11.5.1.3 Network specification interface 327
11.5.1.4 Network instantiation 328
11.5.2 Exploiting the reusable code 328
References 341
12 hoc, NEURON S interpreter 343
12.1 The interpreter 344
12.2 Adding new mechanisms to the interpreter 345
12.3 The stand-alone interpreter 346
12.3.1 Starting and exiting the interpreter 346
12.3.2 Error handling 348
12.4 Syntax 350
12.4.1 Names 350
12.4.2 Keywords 350
12.4.3 Variables 353
12.4.4 Expressions 354
12.4.5 Statements 355
12.4.6 Comments 355
12.4.7 Flow control 356
12.4.8 Functions and procedures 357
12.4.8.1 Arguments 358
12.4.8.2 Call by reference vs. call by value 359
12.4.8.3 Local variables 360
12.4.8.4 Recursive functions 360
12.4.9 Input and output 361
12.4.10 Editing 362
Reference 362
13 Object-oriented programming 363
13.1 Object vs. class 363
13.2 The object model in hoc 364
13.3 Objects and object references 364
13.3.1 Declaring an object reference 364
Table of contents xv
13.3.2 Creating and destroying an object 365
13.3.3 Using an object reference 366
13.3.3.1 Passing obj ref s (and objects)
to functions 366
13.3.4 Denning an object class 367
13.3.4.1 Direct commands 368
13.3.4.2 Initializing variables in an object 368
13.3.4.3 Keyword names 369
13.3.5 Object references vs. object names 370
13.3.5.1 An example of the didactic use
of object names 371
13.4 Using objects to solve programming problems 372
13.4.1 Dealing with collections or sets 372
13.4.1.1 Array of objects 372
13.4.1.2 List of objects 373
13.4.2 Encapsulating code 375
13.5 Polymorphism and inheritance 376
Reference 377
14 How to modify NEURON itself 378
14.1 A word about graphics terminology 378
14.2 Graphical interface programming 378
14.2.1 General issues 380
14.2.1.1 A pattern for defining a GUI
tool template 381
14.2.1.2 Enclosing the GUI tool in a
single window 383
14.2.1.3 Saving the window to a session 385
14.2.2 Tool-specific development 389
14.2.2.1 Plotting 389
14.2.2.2 Handling events 392
14.2.2.3 Finishing up 395
Appendix Al Mathematical analysis of IntFire4 399
A1.1 Proof that the estimate is never later than the
true firing time 401
Al.1.1 Part 1: If m^=sO, then m(t) remains 1 402
Al.1.2 Part 2: If m 0, (1 - m)lm
underestimates the firing time 404
xvi Table of contents
Appendix A2 NEURON s built-in editor 406
A2.1 Starting and stopping 407
A2.1.1 Switching from hoc to emacs 407
A2.1.2 Returning from emacs to hoc 407
A2.1.3 Killing the current command 407
A2.2 Moving the cursor 407
A2.3 Modes 408
A2.4 Deleting and inserting 408
A2.5 Blocks of text: marking, cutting, and pasting 408
A2.6 Searching and replacing 409
A2.7 Text formatting and other tricks 409
A2.8 Buffers and file I/O 409
A2.9 Windows 410
A2.10 Macros and repeating commands 411
References 411
Epilogue 412
Index 413
|
| adam_txt |
Table of contents
It is some systematized exhibition of the whale in his broad genera, that
I would now fain put before you. Yet is it no easy task. The classification of the
constituents of a chaos, nothing less is here essayed.
Preface xvii
Acknowledgments xix
1 A tour of the NEURON simulation environment 1
1.1 Modeling and understanding 1
1.2 Introducing NEURON 1
1.3 State the question 2
1.4 Formulate a conceptual model 3
1.5 Implement the model in NEURON 5
1.5.1 Starting and stopping NEURON 6
1.5.2 Bringing up a CellBuilder 6
1.5.3 Entering the specifications of the model cell 8
1.5.3.1 Topology 8
1.5.3.2 Subsets 10
1.5.3.3 Geometry 12
1.5.3.4 Biophysics 13
1.5.4 Saving the model cell 16
1.5.5 Executing the model specification 16
1.6 Instrument the model 18
1.6.1 Signal sources 18
1.6.2 Signal monitors 20
1.7 Set up controls for running the simulation 21
1.8 Save model with instrumentation and run control 21
1.9 Run the simulation experiment 23
1.10 Analyze results 24
References 30
2 The modeling perspective 32
2.1 Why model? 32
2.2 From physical system to computational model 33
2.2.1 Conceptual model: a simplified
representation of a physical system 33
vi Table of contents
2.2.2 Computational model: an accurate
representation of a conceptual model 33
2.2.3 An example 34
3 Expressing conceptual models in mathematical terms 36
3.1 Chemical reactions 36
3.1.1 Flux and conservation in kinetic schemes 37
3.1.2 Stoichiometry, flux, and mole equivalents 39
3.1.3 Compartment size 41
3.1.3.1 Scale factors 43
3.2 Electrical circuits 44
3.3 Cables 50
References 54
4 Essentials of numerical methods for neural modeling 55
4.1 Spatial and temporal error in discretized cable equations 56
4.1.1 Analytic solutions: continuous in time
and space 56
4.1.2 Spatial discretization 57
4.1.3 Adding temporal discretization 60
4.2 Numerical integration methods 62
4.2.1 Forward Euler: simple, inaccurate and
unstable 62
4.2.1.1 Numerical instability 64
4.2.2 Backward Euler: inaccurate but stable 66
4.2.3 Crank-Nicholson: stable and more accurate 68
4.2.3.1 Efficient handling of nonlinearity 70
4.2.4 Adaptive integration: fast or accurate,
occasionally both 72
4.2.4.1 Implementational considerations 73
4.2.4.2 The user's perspective 75
4.2.4.3 Local variable time step method 80
4.2.4.4 Discrete event simulations 83
4.3 Error 83
4.4 Summary of NEURON'S integration methods 86
4.4.1 Fixed time step integrators 86
4.4.1.1 Default: backward Euler 86
4.4.1.2 Crank-Nicholson 86
4.4.2 Adaptive integrators 87
Table of contents vii
4.4.2.1 CVODE 88
4.4.2.2 DASPK 88
References 88
5 Representing neurons with a digital computer 90
5.1 Discretization 90
5.2 How NEURON separates anatomy and biophysics
from purely numerical issues 92
5.2.1 Sections and section variables 92
5.2.2 Range and range variables 93
5.2.3 Segments 95
5.2.4 Implications and applications of this strategy 96
5.2.4.1 Spatial accuracy 96
5.2.4.2 A practical test of spatial accuracy 97
5.3 How to specify model properties 98
5.3.1 Which section do we mean? 98
5.3.1.1 Dot notation 99
5.3.1.2 Section stack 99
5.3.1.3 Default section 100
5.4 How to set up model topology 101
5.4.1 Loops of sections 101
5.4.2 A section may have only one parent 101
5.4.3 The root section 101
5.4.4 Attach sections at 0 or 1 for accuracy 102
5.4.5 Checking the tree structure with
topology() 102
5.4.6 Viewing topology with a Shape plot 103
5.5 How to specify geometry 103
5.5.1 Stylized specification 104
5.5.2 3-D specification 105
5.5.3 Avoiding artifacts 107
5.5.3.1 Beware of zero diameter 107
5.5.3.2 Stylized specification may be
reinterpreted as 3-D specification 108
5.6 How to specify biophysical properties 111
5.6.1 Distributed mechanisms 111
5.6.2 Point processes 112
5.6.3 User-defined mechanisms 113
5.6.4 Working with range variables 114
5.6.4.1 Iterating over nodes 114
I
viii Table of contents \
5.6.4.2 Linear taper 115
5.6.4.3 How changing nseg affects range
variables 115
5.7 Choosing a spatial grid 117
5.7.1 A consideration of intent and judgment 118
5.7.2 Discretization guidelines 121
5.7.2.1 The djambda rule 122
References 126
6 How to build and use models of individual cells 128
6.1 Graphical user interface vs. hoc code:
which to use, and when? 128
6.2 Hidden secrets of the GUI 129
6.3 Implementing a model with hoc 130
6.3.1 Topology 130
6.3.2 Geometry 132
6.3.3 Biophysics 133
6.3.4 Testing the model implementation 133
6.3.5 An aside: how does our model implementation
in hoc compare with the output of the
CellBuilder? 135
6.4 Instrumenting a model with hoc 139
6.5 Setting up simulation control with hoc 139
6.5.1 Testing simulation control 141
6.6 Evaluating and using the model 141
6.7 Combining hoc and the GUI 141
6.7.1 No NEURON Main Menu toolbar? 142
6.7.2 Default section? We ain't got no default
section! 142
6.7.3 Strange Shapes? 144
6.7.3.1 The barbed wire model 144
6.7.3.2 The case of the disappearing section 148
6.7.4 Graphs don't work? 151
6.7.5 Conflicts between hoc code and GUI tools 152
6.8 Elementary project management 154
6.8.1 Iterative program development 155
Reference 156
7 How to control simulations 157
7.1 Simulation control with the graphical user interface 157
7.2 The standard run system 159
Table of contents ix
7.2.1 An outline of the standard ran system 160
7.2.1.1 fadvanceO 160
7.2.1.2 advance () 161
7.2.1.3 stepO 161
7.2.1.4 steprunO and continuerun () 162
7.2.1.5 run() 163
7.3 Details of f advance () 164
7.3.1 The fixed step methods: backward Euler and
Crank-Nicholson 165
7.3.2 Adaptive integrators 171
7.3.2.1 Local time step integration with
discrete events 173
7.3.2.2 Global time step integration with
discrete events 179
7.4 Incorporating Graphs and new objects into the
plotting system 179
References 181
8 How to initialize simulations 183
8.1 State variables and STATE variables 183
8.2 Basic initialization in NEURON: f initialize () 185
8.3 Default initialization in the standard ran system:
stdinit() and init() 187
8.3.1 INITIAL blocks in NMODL 188
8.3.1.1 Default vs. explicit initialization
of STATES 190
8.3.1.2 Ion concentrations and equilibrium
potentials 190
8.4 Examples of custom initializations 195
8.4.1 Initializing to a particular resting potential 195
8.4.2 Initializing to steady state 197
8.4.3 Initializing to a desired state 198
8.4.4 Initializing by changing model parameters 199
8.4.4.1 Details of the mechanism 200
8.4.4.2 Initializing the mechanism 202
Reference 206
9 How to expand NEURON'S library of mechanisms 207
9.1 Overview of NMODL 207
9.2 Example 9.1: A passive "leak" current 208
9.2.1 The NEURON block 210
x Table of contents
9.2.2 Variable declaration blocks 211
9.2.2.1 The PARAMETER block 212
9.2.2.2 The ASSIGNED block 212
9.2.3 Equation definition blocks 213
9.2.3.1 The BREAKPOINT block 213
9.2.4 Usage 214
9.3 Example 9.2: A localized shunt 214
9.3.1 The NEURON block 215
9.3.2 Variable declaration blocks 215
9.3.3 Equation definition blocks 216
9.3.3.1 The BREAKPOINT block 216
9.3.4 Usage 217
9.4 Example 9.3: An intracellular stimulating
electrode 217
9.4.1 The NEURON block 218
9.4.2 Equation definition blocks 218
9.4.2.1 The BREAKPOINT block 218
9.4.2.2 The INITIAL block 219
9.4.3 Usage 219
9.5 Example 9.4: A voltage-gated current 220
9.5.1 The NEURON block 222
9.5.2 The UNITS block 222
9.5.3 Variable declaration blocks 222
9.5.3.1 The ASSIGNED block 222
9.5.3.2 The STATE block 223
9.5.4 Equation definition blocks 223
9.5.4.1 The BREAKPOINT block 223
9.5.4.2 The INITIAL block 224
9.5.4.3 The DERIVATIVE block 225
9.5.4.4 The FUNCTION block 226
9.5.5 Usage 227
9.6 Example 9.5: A calcium-activated,
voltage-gated current 228
9.6.1 The NEURON block 230
9.6.2 The UNITS block 231
9.6.3 Variable declaration blocks 231
9.6.3.1 The AS SIGNED block 231
9.6.3.2 The STATE block 232
9.6.4 Equation definition blocks 232
9.6.4.1 The BREAKPOINT block 232
Table of contents xi
9.6.4.2 The DERIVATIVE block 232
9.6.4.3 The FUNCTION and
PROCEDURE blocks 232
9.6.5 Usage 233
9.7 Example 9.6: Extracellular potassium
accumulation 233
9.7.1 The NEURON block 235
9.7.2 Variable declaration blocks 236
9.7.2.1 The PARAMETER block 236
9.7.2.2 The STATE block 236
9.7.3 Equation definition blocks 236
9.7.3.1 The BREAKPOINT block 236
9.7.3.2 The INITIAL block 236
9.7.3.3 The DERIVATIVE block 237
9.7.4 Usage 237
9.8 General comments about kinetic schemes 238
9.9 Example 9.7: Kinetic scheme for a
voltage-gated current 240
9.9.1 The NEURON block 242
9.9.2 Variable declaration blocks 242
9.9.2.1 The STATE block 242
9.9.3 Equation definition blocks 243
9.9.3.1 The BREAKPOINT block 243
9.9.3.2 The INITIAL block 243
9.9.3.3 The KINETIC block 243
9.9.3.4 The FUNCTIONJTABLEs 244
9.9.4 Usage 245
9.10 Example 9.8: Calcium diffusion with buffering 245
9.10.1 Modeling diffusion with kinetic schemes 246
9.10.2 The NEURON block 250
9.10.3 The UNITS block 250
9.10.4 Variable declaration blocks 250
9.10.4.1 The ASSIGNED block 250
9.10.4.2 The STATE block 250
9.10.4.3 LOCAL variables
declared outside of equation
definition blocks 251
9.10.5 Equation definition blocks 251
9.10.5.1 The INITIAL block 251
9.10.5.2 PROCEDURE factors () 252
I
xii Table of contents j
9.10.5.3 The KINETIC block 252 :
9.10.6 Usage 254
9.11 Example 9.9: A calcium pump 255
9.11.1 The NEURON block 255
9.11.2 The UNITS block 256
9.11.3 Variable declaration blocks 256
9.11.3.1 The PARAMETER block 256
9.11.3.2 The ASSIGNED block 257
9.11.3.3 The CONSTANT block 257
9.11.3.4 The STATE block 257
9.11.4 Equation definition blocks 257
9.11.4.1 The BREAKPOINT block 257
9.11.4.2 The INITIAL block 258
9.11.4.3 The KINETIC block 259
9.11.5 Usage 260
9.12 Models with discontinuities 260
9.12.1 Discontinuities in PARAMETERS and
AS SIGNED variables 260
9.12.2 Discontinuities in STATES 261
9.12.3 Event handlers 263
9.13 Time-dependent PARAMETER changes 263
References 264
10 Synaptic transmission and artificial spiking cells 265
10.1 Modeling communication between cells 266
10.1.1 Example 10.1: Graded synaptic transmission 266
10.1.1.1 The NEURON block 268
10.1.1.2 The BREAKPOINT block 269
10.1.1.3 Usage 269
10.1.2 Example 10.2: A gap junction 271
10.1.2.1 Usage 272
10.1.3 Modeling spike-triggered synaptic
transmission: an event-based strategy 272
10.1.3.1 Conceptual model 273
10.1.3.2 The Net Con class 274
10.1.4 Example 10.3: Synapse with
exponential decay 277
10.1.4.1 The BREAKPOINT block 278
10.1.4.2 The DERIVATIVE block 278
10.1.4.3 The NET_RECEIVE block 278
10.1.4.4 Usage 278
Table of contents xiii
10.1.5 Example 10.4: Alpha function synapse 280
10.1.6 Example 10.5: Use-dependent synaptic
plasticity 281
10.1.6.1 TheNET_RECEIVEblock 283
10.1.7 Example 10.6: Saturating synapses 284
10.1.7.1 The PARAMETER block 287
10.1.7.2 The STATE block 287
10.1.7.3 The INITIAL block 287
10.1.7.4 The BREAKPOINT and
DERIVATIVE blocks 288
10.1.7.5 TheNET_RECEIVEblock 288
10.2 Artificial spiking cells 289
10.2.1 Example 10.7: IntFirel, a basic
integrate and fire model 290
10.2.1.1 The NEURON block 291
10.2.1.2 TheNET_RECEIVEblock 292
10.2.1.3 Enhancements to the basic
mechanism 292
10.2.2 Example 10.8: IntFire2, firing rate
proportional to input 297
10.2.2.1 Implementation in NMODL 298
10.2.3 Example 10.9: IntFire4, different
synaptic time constants 301
10.2.4 Other comments regarding artificial
spiking cells 304
References 305
11 Modeling networks 306
11.1 Building a simple network with the GUI 307
11.2 Conceptual model 308
11.3 Adding a new artificial spiking cell to
NEURON 309
11.4 Creating a prototype net with the GUI 311
11.4.1 Define the types of cells 311
11.4.2 Create each cell in the network 312
11.4.3 Connect the cells 315
11.4.3.1 Setting up network architecture 315
11.4.3.2 Specifying delays and weights 316
11.4.4 Set up instrumentation 318
11.4.5 Set up controls for running simulations 319
11.4.6 Run a simulation 322
xiv Table of contents
11.4.7 Caveats and other comments 322
11.4.7.1 Changing the properties of an
existing network 322
11.4.7.2 A word about cell names 323
11.5 Combining the GUI and programming 324
11.5.1 Creating a hoc file from the NetWork Builder 324
11.5.1.1 NetGUI default section 326
11.5.1.2 Network cell templates 326
11.5.1.3 Network specification interface 327
11.5.1.4 Network instantiation 328
11.5.2 Exploiting the reusable code 328
References 341
12 hoc, NEURON'S interpreter 343
12.1 The interpreter 344
12.2 Adding new mechanisms to the interpreter 345
12.3 The stand-alone interpreter 346
12.3.1 Starting and exiting the interpreter 346
12.3.2 Error handling 348
12.4 Syntax 350
12.4.1 Names 350
12.4.2 Keywords 350
12.4.3 Variables 353
12.4.4 Expressions 354
12.4.5 Statements 355
12.4.6 Comments 355
12.4.7 Flow control 356
12.4.8 Functions and procedures 357
12.4.8.1 Arguments 358
12.4.8.2 Call by reference vs. call by value 359
12.4.8.3 Local variables 360
12.4.8.4 Recursive functions 360
12.4.9 Input and output 361
12.4.10 Editing 362
Reference 362
13 Object-oriented programming 363
13.1 Object vs. class 363
13.2 The object model in hoc 364
13.3 Objects and object references 364
13.3.1 Declaring an object reference 364
Table of contents xv
13.3.2 Creating and destroying an object 365
13.3.3 Using an object reference 366
13.3.3.1 Passing obj ref s (and objects)
to functions 366
13.3.4 Denning an object class 367
13.3.4.1 Direct commands 368
13.3.4.2 Initializing variables in an object 368
13.3.4.3 Keyword names 369
13.3.5 Object references vs. object names 370
13.3.5.1 An example of the didactic use
of object names 371
13.4 Using objects to solve programming problems 372
13.4.1 Dealing with collections or sets 372
13.4.1.1 Array of objects 372
13.4.1.2 List of objects 373
13.4.2 Encapsulating code 375
13.5 Polymorphism and inheritance 376
Reference 377
14 How to modify NEURON itself 378
14.1 A word about graphics terminology 378
14.2 Graphical interface programming 378
14.2.1 General issues 380
14.2.1.1 A pattern for defining a GUI
tool template 381
14.2.1.2 Enclosing the GUI tool in a
single window 383
14.2.1.3 Saving the window to a session 385
14.2.2 Tool-specific development 389
14.2.2.1 Plotting 389
14.2.2.2 Handling events 392
14.2.2.3 Finishing up 395
Appendix Al Mathematical analysis of IntFire4 399
A1.1 Proof that the estimate is never later than the
true firing time 401
Al.1.1 Part 1: If m^=sO, then m(t) remains 1 402
Al.1.2 Part 2: If m' 0, (1 - m)lm'
underestimates the firing time 404
xvi Table of contents
Appendix A2 NEURON's built-in editor 406
A2.1 Starting and stopping 407
A2.1.1 Switching from hoc to emacs 407
A2.1.2 Returning from emacs to hoc 407
A2.1.3 Killing the current command 407
A2.2 Moving the cursor 407
A2.3 Modes 408
A2.4 Deleting and inserting 408
A2.5 Blocks of text: marking, cutting, and pasting 408
A2.6 Searching and replacing 409
A2.7 Text formatting and other tricks 409
A2.8 Buffers and file I/O 409
A2.9 Windows 410
A2.10 Macros and repeating commands 411
References 411
Epilogue 412
Index 413 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Carnevale, Nicholas T. Hines, Michael |
| author_facet | Carnevale, Nicholas T. Hines, Michael |
| author_role | aut aut |
| author_sort | Carnevale, Nicholas T. |
| author_variant | n t c nt ntc m h mh |
| building | Verbundindex |
| bvnumber | BV035189069 |
| callnumber-first | Q - Science |
| callnumber-label | QP363 |
| callnumber-raw | QP363 |
| callnumber-search | QP363 |
| callnumber-sort | QP 3363 |
| callnumber-subject | QP - Physiology |
| classification_rvk | ST 301 WC 7700 WW 2200 |
| classification_tum | MED 602f DAT 780f DAT 717f |
| ctrlnum | (OCoLC)64091421 (DE-599)BVBBV035189069 |
| dewey-full | 573.8536 573.801/13 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 573 - Specific physiological systems in animals |
| dewey-raw | 573.8536 573.801/13 |
| dewey-search | 573.8536 573.801/13 |
| dewey-sort | 3573.8536 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Informatik Medizin |
| discipline_str_mv | Biologie Informatik Medizin |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV035189069 |
| illustrated | Illustrated |
| index_date | 2024-07-02T23:01:04Z |
| indexdate | 2024-07-09T21:27:03Z |
| institution | BVB |
| isbn | 0521843219 9780521115636 9780521843218 |
| language | English |
| lccn | 2006277066 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016995712 |
| oclc_num | 64091421 |
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| owner | DE-20 DE-83 DE-188 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-11 DE-703 |
| owner_facet | DE-20 DE-83 DE-188 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-11 DE-703 |
| physical | XIX, 457 S. graph. Darst. 24 cm |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Carnevale, Nicholas T. Verfasser aut The NEURON book Ted Carnevale, Michael Hines 1. publ. Cambridge Cambridge University Press 2006 XIX, 457 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke NEURON (Computer file) Neurons Computer simulation Neural networks (Neurobiology) Computer simulation Nervenzelle (DE-588)4041649-5 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Nervenzelle (DE-588)4041649-5 s Computersimulation (DE-588)4148259-1 s DE-604 Hines, Michael Verfasser aut http://www.loc.gov/catdir/enhancements/fy0661/2006277066-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0661/2006277066-t.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0733/2006277066-b.html Contributor biographical information HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016995712&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Carnevale, Nicholas T. Hines, Michael The NEURON book NEURON (Computer file) Neurons Computer simulation Neural networks (Neurobiology) Computer simulation Nervenzelle (DE-588)4041649-5 gnd Computersimulation (DE-588)4148259-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4041649-5 (DE-588)4148259-1 (DE-588)4226127-2 |
| title | The NEURON book |
| title_auth | The NEURON book |
| title_exact_search | The NEURON book |
| title_exact_search_txtP | The NEURON book |
| title_full | The NEURON book Ted Carnevale, Michael Hines |
| title_fullStr | The NEURON book Ted Carnevale, Michael Hines |
| title_full_unstemmed | The NEURON book Ted Carnevale, Michael Hines |
| title_short | The NEURON book |
| title_sort | the neuron book |
| topic | NEURON (Computer file) Neurons Computer simulation Neural networks (Neurobiology) Computer simulation Nervenzelle (DE-588)4041649-5 gnd Computersimulation (DE-588)4148259-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | NEURON (Computer file) Neurons Computer simulation Neural networks (Neurobiology) Computer simulation Nervenzelle Computersimulation Neuronales Netz |
| url | http://www.loc.gov/catdir/enhancements/fy0661/2006277066-d.html http://www.loc.gov/catdir/enhancements/fy0661/2006277066-t.html http://www.loc.gov/catdir/enhancements/fy0733/2006277066-b.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016995712&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT carnevalenicholast theneuronbook AT hinesmichael theneuronbook |