Dynamical Systems in Neuroscience:

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366 Burstingbifurcation of spiking statesaddle-node oninvariant circlesaddlehomoclinic orbitsupercriticalAndronov-Hopffold limit cycle"Fold/Circle" Bursting"Fold/Homoclinic" Bursting "Fold/Hopf" Bursting "Fold/Fold Cycle" Burstingsaddle-node (fold)"Circle/Circle" Bursting "Circle/Homoclinic" Bursting "Circle/Hopf" Bursting "Circle/Fold Cycle" Burstingbifurcation of resting statesaddle-node oninvariant circlesupercriticalAndronov-Hopf"Hopf/Circle" Bursting "Hopf/Homoclinic" Bursting "Hopf/Hopf" Bursting "Hopf/Fold Cycle" Bursting"SubHopf/Circle" Bursting"SubHopf/Homoclinic"Bursting"SubHopf/Hopf" Bursting"SubHopf/Fold Cycle"BurstingsubcriticalAndronov-HopfFigure 9.24: Examples of “2+1” point-cycle fast-slow co-dimension-1 bursters ofhysteresis-loop type (modified from Izhikevich 2000). Dashed chains of arrows showtransitions that might involve other bifurcations not relevant to the bursting type.
spikingBursting 367foldbifurcationxurestsaddlehomoclinicorbitbifurcationfold bifurcationsaddle homoclinicorbit bifurcationFigure 9.25: “Fold/homoclinic” bursting: The resting state disappears via saddle-node(fold) bifurcation and the spiking limit cycle disappears via saddle homoclinic orbitbifurcation.tential corresponds to the left stable equilibrium, which is the intersection of the leftknee of the fast N-shaped nullcline with the slow nullcline. During resting, the N-shapenullcline slowly moves up, until its knee touches the slow nullcline at a saddle-nodepoint. Right after this moment, the resting state disappears via saddle-node (fold) bifurcation,hence the part “fold” in the name of the burster. After the fold bifurcation,the membrane potential jumps up to the stable limit cycle corresponding to repetitivespiking. During the spiking state, the N-shaped nullcline slowly moves down, and themiddle (saddle) equilibrium moves away from the resting state toward the limit cycle.After a while, the limit cycle becomes a homoclinic trajectory to the saddle, and thenthe cycle disappears via saddle homoclinic orbit bifurcation, hence the part “homoclinic”in the name of the burster. After this bifurcation, the membrane potentialjumps down to the resting state and closes the hysteresis loop.
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Eugene M. IzhikevichThe Neuroscienc
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6.3.6 Saddle-node homoclinic orbit
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9.4.2 Integrators vs. Resonators .
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xPrefaceversely, cells having quite
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2 Introductionapical dendritessomar
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4 Introduction(a)spikes(b)spikes cu
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6 Introduction1.1.3 Why are neurons
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8 Introductionconcepts of dynamical
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10 Introductionspikingmoderesting m
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12 Introduction(a)spikinglimitcycle
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14 Introductionco-existence of rest
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16 Introduction1.2.4 Neuro-computat
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18 IntroductionspikeFigure 1.16: Ph
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20 Introductionwith coupled neurons
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22 IntroductionFigure 1.17: John Ri
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24 Introduction
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26 Electrophysiology of NeuronsInsi
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28 Electrophysiology of Neuronsouts
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30 Electrophysiology of NeuronsRest
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32 Electrophysiology of NeuronsFigu
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34 Electrophysiology of Neuronsm (V
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36 Electrophysiology of Neurons10.8
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38 Electrophysiology of Neurons2.3
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42 Electrophysiology of NeuronsDepo
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44 Electrophysiology of NeuronsV(x,
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46 Electrophysiology of Neurons1m (
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48 Electrophysiology of NeuronsPara
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52 Electrophysiology of Neuronsvolt
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54 Electrophysiology of Neurons
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56 One-Dimensional SystemsActivatio
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58 One-Dimensional Systemsinwardcur
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60 One-Dimensional Systems40bistabi
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62 One-Dimensional Systemsat every
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64 One-Dimensional Systems?F(V)>0+
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66 One-Dimensional SystemsUnstableE
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70 One-Dimensional Systems+ - - + +
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72 One-Dimensional Systemsλ(V-Veq)
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74 One-Dimensional Systemsbifurcati
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76 One-Dimensional Systems100806040
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78 One-Dimensional Systemsbifurcati
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80 One-Dimensional Systems20 mV100
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82 One-Dimensional Systemsmembrane
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84 One-Dimensional Systemssteady-st
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86 One-Dimensional Systemsspike rig
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88 One-Dimensional SystemsVF (V)1VV
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90 One-Dimensional Systems10.80.60.
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92 One-Dimensional Systems
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94 Two-Dimensional SystemsI Na,pneu
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96 Two-Dimensional Systemsdefines a
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98 Two-Dimensional Systems(f(x(t),y
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100 Two-Dimensional Systems0.70acti
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106 Two-Dimensional SystemsFor exam
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112 Two-Dimensional SystemsV-nullcl
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114 Two-Dimensional SystemsV-nullcl
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116 Two-Dimensional Systemsheterocl
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118 Two-Dimensional Systems0.60.5n-
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120 Two-Dimensional Systemseigenval
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122 Two-Dimensional SystemsK + acti
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124 Two-Dimensional Systemsexcitati
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126 Two-Dimensional SystemsReview o
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128 Two-Dimensional SystemsabcdFigu
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130 Two-Dimensional SystemsFigure 4
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132 Two-Dimensional Systems
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134 Conductance-Based Models• If
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136 Conductance-Based Modelsinward(
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138 Conductance-Based Models1I=01I=
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140 Conductance-Based Modelsleak cu
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142 Conductance-Based Modelshyperpo
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144 Conductance-Based Models0I=0ina
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146 Conductance-Based Models0I=9.75
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148 Conductance-Based Models1I=651I
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150 Conductance-Based Modelshave co
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152 Conductance-Based Models1 sec10
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154 Conductance-Based Modelsvoltage
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156 Conductance-Based Models1h=0.89
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158 Conductance-Based Models5.2.2 E
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160 Conductance-Based Models1recove
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162 Conductance-Based Modelscurrent
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164 Conductance-Based Modelsmodels
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166 Conductance-Based Models
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168 Bifurcationssaddle-node bifurca
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170 Bifurcationssaddle-node bifurca
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172 Bifurcations0.5v 2V-nullclineK
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174 BifurcationsBoth types of the b
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176 BifurcationsV-nullcline0.5K + g
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178 Bifurcationsrstable limit cycle
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180 BifurcationsnVstable limit cycl
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¡ ¡¡ ¡¡ ¡ ¡¡ ¡ ¡182 Bifur
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186 Bifurcationsmembrane potential,
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188 BifurcationsBifurcation of a li
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190 Bifurcationsstableunstablelimit
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192 Bifurcationsamplitude (max-min)
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194 Bifurcationshomoclinic orbithom
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196 Bifurcations0.80.60.4stable lim
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198 Bifurcationsfrequency (Hz)40030
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200 BifurcationsSaddle-Focus Homocl
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202 Bifurcationsc 1c 2xFigure 6.34:
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204 BifurcationseigenvaluesHopffold
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206 Bifurcationsfast nullclineslow
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208 Bifurcationswith fast and slow
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210 BifurcationsSupercritical Andro
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212 BifurcationsK + conductance tim
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214 BifurcationsIn contrast, if the
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216 Bifurcationsinvariant circlesad
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218 Bifurcationsbifurcationssaddle-
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220 BifurcationsExercises1. (Transc
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222 Bifurcationsv 2v 11a-11-1Figure
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224 Bifurcations19. [M.S.] A leaky
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226 Excitabilityspike?spikerestrest
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228 ExcitabilityAlternatively, the
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230 Excitability50 ms 20 mV 1 ms100
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232 ExcitabilityClass 3 excitable n
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236 Excitabilitysaddle-node bifurca
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240 Excitabilityproperties integrat
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242 ExcitabilityThe existence of fa
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246 Excitabilityspikeexcitatory pul
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248 Excitability(g)9 ms(f)(e)(d)(c)
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250 Excitabilitysquid axonmodel0 mV
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252 Excitability(a) integrator(b) r
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256 Excitabilitymembrane potential,
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258 Excitability0.1saddle-node bifu
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260 Excitability100 ms10 mVoscillat
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262 ExcitabilityBogdanov-Takens bif
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264 Excitabilitya pulse of current.
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266 Excitabilitymembrane potential
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268 Excitability(a)150100I K(M)(b)3
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270 Excitability50 mV300 msFigure 7
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272 Excitability20 mVADP100 msAHP-6
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274 ExcitabilityK + activation gate
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276 ExcitabilityBibliographical Not
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278 Excitability7. Show that the re
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280 Simple Modelsspikemembrane pote
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282 Simple Models1thresholdyz reset
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284 Simple Models1v reset =|b| 1/2b
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286 Simple Modelsbe an integrator o
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288 Simple Modelsintegrate-and-fire
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290 Simple Models(A) tonic spiking(
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292 Simple Modelsgeneral algorithm
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294 Simple Modelscha, cha — real
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296 Simple Modelslayer 5 neuronsimp
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298 Simple Modelsb=-240200saddle-no
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300 Simple Models(a)simple modellay
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302 Simple Models(a)burstingspiking
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304 Simple Models(a)dendriticspike2
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308 Simple Modelschattering neuron
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314 Simple Models(1) fast oscillati
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Page 352 and 353: 342 Bursting(a) cortical chattering
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Page 356 and 357: 346 Burstingvoltage-gatedCa2+-gated
Page 358 and 359: 348 Burstingslow dynamicsneuronvolt
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Page 374 and 375: 364 Bursting9.3 ClassificationIn Fi
Page 378 and 379: 368 BurstingFigure 9.26: Putative
Page 380 and 381: 370 Bursting108spikingslow variable
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Page 384 and 385: 374 Burstingdepending on the type o
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Page 396 and 397: 386 Bursting9.4.3 BistabilitySuppos
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416 Solutions to Exercises, Chap. 4
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442 Referencesterneurons mediated b
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444 ReferencesDickson C.T., Magistr
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446 ReferencesGuckenheimer J. (1975
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448 Referencestional Journal of Bif
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450 ReferencesMarkram H, Toledo-Rod
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452 ReferencesRosenblum M.G. and Pi
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454 ReferencesTuckwell H.C. (1988)
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456 References9
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458 Synchronization (see www.izhike
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