Dynamical Systems in Neuroscience:

More documents

Recommendations

Info

396 Bursting0-10membrane potential, V (mV)-20-30-40-50-60-700 10 20 30 40 50 60 70time (ms)Figure 9.56: Bursting in a twodimensionalI Na,p +I K -model; see Ex. 3.0.21V-nullclinew0.1w-nullcline0.50V(t)0-0.5 0 0.5 1VI(t)-0.50 1000 2000 3000 4000timeFigure 9.57: Rebound bursting in the FitzHugh-Nagumo oscillator; see Ex. 4.
Bursting 3970.5x 1Figure 9.58: Hopf/Hopf burstingtwithout co-existence of attractors;see Ex. 6 (modified from Hoppensteadtand Izhikevich 1997).4. (Rebound bursting) Explain the mechanism of rebound bursting in the twodimensionalFitzHugh-Nagumo oscillator (4.11,4.12) shown in Fig. 9.57.5. Can “circle/*” and “fold/*” bursters have fast subthreshold oscillations of membranepotential? Explain.6. (Hopf/Hopf bursting) The systemẋ = (y + i)x − x|x| 2 , x = x 1 + ix 2 ∈ C ,has a unique attractor for any value of the parameter y ∈ R. Ifẏ = µ(2aS( y a − a) − |x|) , µ = 0.05 , a = √ µ/20 , S(u) =11 + e −uthen the “2+1” system above can burst, as we show in Fig. 9.58. Explore thesystem numerically and explain the origin of bursting.7. (Hopf/Hopf canonical model) Consider the “2+1” fast-slow burster (9.1) andsuppose that x 0 is the supercritical Andronov-Hopf bifurcation point of the fastsubsystem when u = u 0 . Also suppose that u 0 is a stable equilibrium of theslow subsystem when x = x 0 is fixed. Show that there is a continuous change ofvariables that transforms (9.1) into the canonical modelz ′ = (u + iω)z − z|z| 2 ,u ′ = µ(±1 ± u − a|z| 2 ) ,where z ∈ C is the new fast variable, u ∈ R is a slow variable, and ω, a and µ areparameters.8. (Bursting in the I Na,t +I Na,slow -model) Take advantage of the phenomenon ofinhibition-induced spiking described in Sect. 7.2.8 to show that a slow persistentinward current, say I Na,slow , can make a spiking model burst.9. Modify the example above to obtain repetitive bursting in a model consisting offast I Na,t current, leak current, and a slow passive dendritic compartment.
Page 1:
Eugene M. IzhikevichThe Neuroscienc
Page 6 and 7:
6.3.6 Saddle-node homoclinic orbit
Page 8 and 9:
9.4.2 Integrators vs. Resonators .
Page 10 and 11:
xPrefaceversely, cells having quite
Page 12 and 13:
2 Introductionapical dendritessomar
Page 14 and 15:
4 Introduction(a)spikes(b)spikes cu
Page 16 and 17:
6 Introduction1.1.3 Why are neurons
Page 18 and 19:
8 Introductionconcepts of dynamical
Page 20 and 21:
10 Introductionspikingmoderesting m
Page 22 and 23:
12 Introduction(a)spikinglimitcycle
Page 24 and 25:
14 Introductionco-existence of rest
Page 26 and 27:
16 Introduction1.2.4 Neuro-computat
Page 28 and 29:
18 IntroductionspikeFigure 1.16: Ph
Page 30 and 31:
20 Introductionwith coupled neurons
Page 32 and 33:
22 IntroductionFigure 1.17: John Ri
Page 34 and 35:
24 Introduction
Page 36 and 37:
26 Electrophysiology of NeuronsInsi
Page 38 and 39:
28 Electrophysiology of Neuronsouts
Page 40 and 41:
30 Electrophysiology of NeuronsRest
Page 42 and 43:
32 Electrophysiology of NeuronsFigu
Page 44 and 45:
34 Electrophysiology of Neuronsm (V
Page 46 and 47:
36 Electrophysiology of Neurons10.8
Page 48 and 49:
38 Electrophysiology of Neurons2.3
Page 50 and 51:
Page 52 and 53:
42 Electrophysiology of NeuronsDepo
Page 54 and 55:
44 Electrophysiology of NeuronsV(x,
Page 56 and 57:
46 Electrophysiology of Neurons1m (
Page 58 and 59:
48 Electrophysiology of NeuronsPara
Page 60 and 61:
Page 62 and 63:
52 Electrophysiology of Neuronsvolt
Page 64 and 65:
54 Electrophysiology of Neurons
Page 66 and 67:
56 One-Dimensional SystemsActivatio
Page 68 and 69:
58 One-Dimensional Systemsinwardcur
Page 70 and 71:
60 One-Dimensional Systems40bistabi
Page 72 and 73:
62 One-Dimensional Systemsat every
Page 74 and 75:
64 One-Dimensional Systems?F(V)>0+
Page 76 and 77:
66 One-Dimensional SystemsUnstableE
Page 78 and 79:
¡ ¡ ¡¡ ¡ ¡¡ ¡ ¡¡ ¡ ¡¡
Page 80 and 81:
70 One-Dimensional Systems+ - - + +
Page 82 and 83:
72 One-Dimensional Systemsλ(V-Veq)
Page 84 and 85:
74 One-Dimensional Systemsbifurcati
Page 86 and 87:
76 One-Dimensional Systems100806040
Page 88 and 89:
78 One-Dimensional Systemsbifurcati
Page 90 and 91:
80 One-Dimensional Systems20 mV100
Page 92 and 93:
82 One-Dimensional Systemsmembrane
Page 94 and 95:
84 One-Dimensional Systemssteady-st
Page 96 and 97:
86 One-Dimensional Systemsspike rig
Page 98 and 99:
88 One-Dimensional SystemsVF (V)1VV
Page 100 and 101:
90 One-Dimensional Systems10.80.60.
Page 102 and 103:
92 One-Dimensional Systems
Page 104 and 105:
94 Two-Dimensional SystemsI Na,pneu
Page 106 and 107:
96 Two-Dimensional Systemsdefines a
Page 108 and 109:
98 Two-Dimensional Systems(f(x(t),y
Page 110 and 111:
100 Two-Dimensional Systems0.70acti
Page 112 and 113:
102 Two-Dimensional Systems0.70.6K
Page 114 and 115:
104 Two-Dimensional Systemsy1.510.5
Page 116 and 117:
106 Two-Dimensional SystemsFor exam
Page 118 and 119:
108 Two-Dimensional SystemsIn gener
Page 120 and 121:
110 Two-Dimensional Systemsv 2 v 2v
Page 122 and 123:
112 Two-Dimensional SystemsV-nullcl
Page 124 and 125:
114 Two-Dimensional SystemsV-nullcl
Page 126 and 127:
116 Two-Dimensional Systemsheterocl
Page 128 and 129:
118 Two-Dimensional Systems0.60.5n-
Page 130 and 131:
120 Two-Dimensional Systemseigenval
Page 132 and 133:
122 Two-Dimensional SystemsK + acti
Page 134 and 135:
124 Two-Dimensional Systemsexcitati
Page 136 and 137:
126 Two-Dimensional SystemsReview o
Page 138 and 139:
128 Two-Dimensional SystemsabcdFigu
Page 140 and 141:
130 Two-Dimensional SystemsFigure 4
Page 142 and 143:
132 Two-Dimensional Systems
Page 144 and 145:
134 Conductance-Based Models• If
Page 146 and 147:
136 Conductance-Based Modelsinward(
Page 148 and 149:
138 Conductance-Based Models1I=01I=
Page 150 and 151:
140 Conductance-Based Modelsleak cu
Page 152 and 153:
142 Conductance-Based Modelshyperpo
Page 154 and 155:
144 Conductance-Based Models0I=0ina
Page 156 and 157:
146 Conductance-Based Models0I=9.75
Page 158 and 159:
148 Conductance-Based Models1I=651I
Page 160 and 161:
150 Conductance-Based Modelshave co
Page 162 and 163:
152 Conductance-Based Models1 sec10
Page 164 and 165:
154 Conductance-Based Modelsvoltage
Page 166 and 167:
156 Conductance-Based Models1h=0.89
Page 168 and 169:
158 Conductance-Based Models5.2.2 E
Page 170 and 171:
160 Conductance-Based Models1recove
Page 172 and 173:
162 Conductance-Based Modelscurrent
Page 174 and 175:
164 Conductance-Based Modelsmodels
Page 176 and 177:
166 Conductance-Based Models
Page 178 and 179:
168 Bifurcationssaddle-node bifurca
Page 180 and 181:
170 Bifurcationssaddle-node bifurca
Page 182 and 183:
172 Bifurcations0.5v 2V-nullclineK
Page 184 and 185:
174 BifurcationsBoth types of the b
Page 186 and 187:
176 BifurcationsV-nullcline0.5K + g
Page 188 and 189:
178 Bifurcationsrstable limit cycle
Page 190 and 191:
180 BifurcationsnVstable limit cycl
Page 192 and 193:
¡ ¡¡ ¡¡ ¡ ¡¡ ¡ ¡182 Bifur
Page 194 and 195:
¡ ¡ ¡¡ ¡ ¡¡ ¡ ¡¡ ¡ ¡184
Page 196 and 197:
186 Bifurcationsmembrane potential,
Page 198 and 199:
188 BifurcationsBifurcation of a li
Page 200 and 201:
190 Bifurcationsstableunstablelimit
Page 202 and 203:
192 Bifurcationsamplitude (max-min)
Page 204 and 205:
194 Bifurcationshomoclinic orbithom
Page 206 and 207:
196 Bifurcations0.80.60.4stable lim
Page 208 and 209:
198 Bifurcationsfrequency (Hz)40030
Page 210 and 211:
200 BifurcationsSaddle-Focus Homocl
Page 212 and 213:
202 Bifurcationsc 1c 2xFigure 6.34:
Page 214 and 215:
204 BifurcationseigenvaluesHopffold
Page 216 and 217:
206 Bifurcationsfast nullclineslow
Page 218 and 219:
208 Bifurcationswith fast and slow
Page 220 and 221:
210 BifurcationsSupercritical Andro
Page 222 and 223:
212 BifurcationsK + conductance tim
Page 224 and 225:
214 BifurcationsIn contrast, if the
Page 226 and 227:
216 Bifurcationsinvariant circlesad
Page 228 and 229:
218 Bifurcationsbifurcationssaddle-
Page 230 and 231:
220 BifurcationsExercises1. (Transc
Page 232 and 233:
222 Bifurcationsv 2v 11a-11-1Figure
Page 234 and 235:
224 Bifurcations19. [M.S.] A leaky
Page 236 and 237:
226 Excitabilityspike?spikerestrest
Page 238 and 239:
228 ExcitabilityAlternatively, the
Page 240 and 241:
230 Excitability50 ms 20 mV 1 ms100
Page 242 and 243:
232 ExcitabilityClass 3 excitable n
Page 244 and 245:
234 Excitability0.20.150.10.05I 1I
Page 246 and 247:
236 Excitabilitysaddle-node bifurca
Page 248 and 249:
238 Excitability(a) resting spiking
Page 250 and 251:
240 Excitabilityproperties integrat
Page 252 and 253:
242 ExcitabilityThe existence of fa
Page 254 and 255:
244 Excitability1coincidencedetecti
Page 256 and 257:
246 Excitabilityspikeexcitatory pul
Page 258 and 259:
248 Excitability(g)9 ms(f)(e)(d)(c)
Page 260 and 261:
250 Excitabilitysquid axonmodel0 mV
Page 262 and 263:
252 Excitability(a) integrator(b) r
Page 264 and 265:
254 Excitability(a) integrator(b) r
Page 266 and 267:
256 Excitabilitymembrane potential,
Page 268 and 269:
258 Excitability0.1saddle-node bifu
Page 270 and 271:
260 Excitability100 ms10 mVoscillat
Page 272 and 273:
262 ExcitabilityBogdanov-Takens bif
Page 274 and 275:
264 Excitabilitya pulse of current.
Page 276 and 277:
266 Excitabilitymembrane potential
Page 278 and 279:
268 Excitability(a)150100I K(M)(b)3
Page 280 and 281:
270 Excitability50 mV300 msFigure 7
Page 282 and 283:
272 Excitability20 mVADP100 msAHP-6
Page 284 and 285:
274 ExcitabilityK + activation gate
Page 286 and 287:
276 ExcitabilityBibliographical Not
Page 288 and 289:
278 Excitability7. Show that the re
Page 290 and 291:
280 Simple Modelsspikemembrane pote
Page 292 and 293:
282 Simple Models1thresholdyz reset
Page 294 and 295:
284 Simple Models1v reset =|b| 1/2b
Page 296 and 297:
286 Simple Modelsbe an integrator o
Page 298 and 299:
288 Simple Modelsintegrate-and-fire
Page 300 and 301:
290 Simple Models(A) tonic spiking(
Page 302 and 303:
292 Simple Modelsgeneral algorithm
Page 304 and 305:
294 Simple Modelscha, cha — real
Page 306 and 307:
296 Simple Modelslayer 5 neuronsimp
Page 308 and 309:
298 Simple Modelsb=-240200saddle-no
Page 310 and 311:
300 Simple Models(a)simple modellay
Page 312 and 313:
302 Simple Models(a)burstingspiking
Page 314 and 315:
304 Simple Models(a)dendriticspike2
Page 316 and 317:
306 Simple Models(a)74 5 6 3210(b)C
Page 318 and 319:
308 Simple Modelschattering neuron
Page 320 and 321:
310 Simple ModelsLTS neuron (in vit
Page 322 and 323:
312 Simple ModelsFS neuron (in vitr
Page 324 and 325:
314 Simple Models(1) fast oscillati
Page 326 and 327:
316 Simple Modelsthrough an appropr
Page 328 and 329:
318 Simple Modelsthey are able to g
Page 330 and 331:
320 Simple Modelsv r = −80 mV, an
Page 332 and 333:
322 Simple Modelsshow in Fig. 7.36.
Page 334 and 335:
324 Simple ModelsBibliographical No
Page 336 and 337:
326 Simple ModelsExercises1. (Integ
Page 338 and 339:
328 Simple Models17. [M.S.] Analyze
Page 340 and 341:
330 Simple Modelsa35 mV350 msc-NAC
Page 342 and 343:
332 Simple Modelsrat RTN neuronsimp
Page 344 and 345:
334 Simple ModelsFigure 8.34: Class
Page 346 and 347:
336 Simple Modelsspiny neuronlatenc
Page 348 and 349:
338 Simple Models(a)stellate cellof
Page 350 and 351:
340 Simple Modelsrat's mitral cell
Page 352 and 353:
342 Bursting(a) cortical chattering
Page 354 and 355:
344 Burstingmembranepotential (mV)-
Page 356 and 357: 346 Burstingvoltage-gatedCa2+-gated
Page 358 and 359: 348 Burstingslow dynamicsneuronvolt
Page 360 and 361: 350 Burstingslow inactivation of in
Page 362 and 363: 352 Bursting9.2.1 Fast-slow burster
Page 364 and 365: 354 Burstingn-nullclinen slow =-0.0
Page 366 and 367: 356 Bursting0maxmembrane potential,
Page 368 and 369: 358 Bursting0membrane potential, V
Page 370 and 371: 360 BurstingI=0I=4.5425 ms 25 mVI=5
Page 372 and 373: 362 Burstingmembrane potential, V (
Page 374 and 375: 364 Bursting9.3 ClassificationIn Fi
Page 376 and 377: 366 Burstingbifurcation of spiking
Page 378 and 379: 368 BurstingFigure 9.26: Putative
Page 380 and 381: 370 Bursting108spikingslow variable
Page 382 and 383: 372 Bursting(a)membrane potential,
Page 384 and 385: 374 Burstingdepending on the type o
Page 386 and 387: 376 BurstingsubcriticalAndronov-Hop
Page 388 and 389: 378 Bursting(a)membrane potential,
Page 390 and 391: spiking380 Burstingfoldbifurcations
Page 392 and 393: 382 BurstingspikingsupercriticalAnd
Page 394 and 395: 384 Burstingaction potentials cut2
Page 396 and 397: 386 Bursting9.4.3 BistabilitySuppos
Page 398 and 399: 388 BurstingFigure 9.49: The instan
Page 400 and 401: esting390 Burstingspikesynchronizat
Page 402 and 403: 392 BurstingReview of Important Con
Page 404 and 405: 394 BurstingspikingrestingFigure 9.
Page 408 and 409: 398 Bursting0membrane potential, V
Page 410 and 411: 400 BurstingFigure 9.61: A cycle-cy
Page 412 and 413: 402 Bursting28. [Ph.D.] Develop an
Page 414 and 415: 404 Synchronization (see www.izhike
Page 416 and 417: 406 Synchronization (see www.izhike
Page 418 and 419: 408 Solutions to Exercises, Chap. 3
Page 452 and 453: 442 Referencesterneurons mediated b
Page 454 and 455: 444 ReferencesDickson C.T., Magistr
Page 456 and 457:
446 ReferencesGuckenheimer J. (1975
Page 458 and 459:
448 Referencestional Journal of Bif
Page 460 and 461:
450 ReferencesMarkram H, Toledo-Rod
Page 462 and 463:
452 ReferencesRosenblum M.G. and Pi
Page 464 and 465:
454 ReferencesTuckwell H.C. (1988)
Page 466 and 467:
456 References9
Page 468 and 469:
458 Synchronization (see www.izhike
Page 470 and 471:
Page 472 and 473:
Page 474 and 475:
Page 476 and 477:
Page 478 and 479:
Page 480 and 481:
Page 482 and 483:
Page 484 and 485:
Page 486 and 487:
Page 488 and 489:
Page 490 and 491:
Page 492 and 493:
Page 494 and 495:
Page 496 and 497:
Page 498 and 499:
Page 500 and 501:
Page 502 and 503:
Page 504 and 505:
Page 506 and 507:
Page 508 and 509:
Page 510 and 511:
Page 512 and 513:
Page 514 and 515:
Page 516 and 517:
Page 518 and 519:
Page 520 and 521:
Page 522 and 523:
512 Solutions to Exercises, Chap. 1
Page 524 and 525:
Page 526 and 527:
Page 528:
show all

Dynamical Systems in Neuroscience:

Create successful ePaper yourself

Delete template?

Save as template?