Dynamical Systems in Neuroscience:

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292 Simple Modelsgeneral algorithm for doing this is not known. However, much success has been achievedin some important cases. The canonical model for a system near an equilibrium is thetopological normal form at the equilibrium (Kuznetsov 1995). Such canonical model islocal, but it can be extended to describe global dynamics. For example, the quadraticintegrate-and-fire model with a fixed v reset < 0 is a global canonical model for all Class1 excitable systems, i.e., systems near saddle-node on invariant circle bifurcation. Thesame model with variable v reset is a global canonical model for all systems near saddlenodehomoclinic orbit bifurcation considered in Sect. 6.3.6. The phase model ˙ϑ = 1derived in Chap. 10 is a global canonical model for the family of nonlinear oscillatorshaving exponentially stable limit cycle attractors. Other examples of canonical modelsfor spiking and bursting can be found in subsequent chapters of this book.The vector-field of excitable conductance-based models in the subthreshold regionand in the region corresponding to the up-stroke of the spike can be converted into thesimple form (8.3, 8.4), possibly with u being a vector. Therefore, the simple model(8.3, 8.4) is a local canonical model for the spike-generation mechanism and the spikeup-stroke of the Hodgkin-Huxley-type neuronal models. It is not a global canonicalmodel because it ignores the spike downstroke. Yet, it describes remarkably well thespiking and bursting dynamics of many biological neurons, as we demonstrate next.8.2 CortexIn this section we consider the six most fundamental classes of firing patterns observedin the mammalian neocortex and depicted in Fig. 8.11 (Connors and Gutnick 1990,Gray and McCormick 1996, Gibson et al. 1999). Though most biologists agree withthe classification in the figure, many would point out that it is greatly oversimplified(Markram et al. 2004), that the distinction between the classes is not sharp, that thereare subclasses within each class (Nowak et al. 2003, Toledo-Rodriguez et al. 2004),and that neurons can change their firing classes depending on the state of the brain(Steriade 2004).• (RS) Regular spiking neurons fire tonic spikes with adapting (decreasing) frequencyin response to injected pulses of dc-current. Most of them have Class 1excitability in the sense that the interspike frequency vanishes when the amplitudeof the injected current decreases. Morphologically, these neurons are spinystellate cells in layer 4 and pyramidal cells in layers 2,3,5, and 6.• (IB) Intrinsically bursting neurons generate a burst of spikes at the beginning ofa strong depolarizing pulse of current, and then switch to tonic spiking mode.Those are excitatory pyramidal neurons found in all cortical layers, but mostabundant in layer 5.• (CH) Chattering neurons fire high-frequency bursts of spikes with relatively shortinterburst periods, hence another name — FRB or fast rhythmic bursting. Outputof such a cell fed to the loudspeaker “sounds a lot like a helicopter — cha,
Simple Models 293regular spiking (RS)excitatorychattering (CH)300 pA600 pA100 pA300 pA35 pA200 pAintrinsically bursting (IB)50 mV600 pA100 ms500 pA400 pAinhibitoryfast spiking (FS) low threshold spiking (LTS)500 pA 400 pA300 pAlate spiking (LS)200 pA175 pA100 pA150 pAdelayFigure 8.11: Six most fundamental classes of firing patterns of neocortical neuronsin response to pulses of depolarizing dc-current. RS and IB are in vitro recordingsof pyramidal neurons of layer 5 of primary visual cortex of a rat, CH was recordedin vivo in cat’s visual cortex (area 17, data provided by D. McCormick). FS wasrecorded in vitro in rat’s primary visual cortex, LTS was recorded in vitro in layer 4or 6 of rat’s barrel cortex (data provided by B. Connors). LS was recorded in layer1 of rat’s visual cortex (data provided by S. Hestrin). All recordings are plotted onthe same voltage and time scale, and the data are available on the author’s webpage(www.izhikevich.com).
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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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102 Two-Dimensional Systems0.70.6K
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104 Two-Dimensional Systemsy1.510.5
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106 Two-Dimensional SystemsFor exam
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108 Two-Dimensional SystemsIn gener
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110 Two-Dimensional Systemsv 2 v 2v
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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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234 Excitability0.20.150.10.05I 1I
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236 Excitabilitysaddle-node bifurca
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238 Excitability(a) resting spiking
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240 Excitabilityproperties integrat
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Page 258 and 259: 248 Excitability(g)9 ms(f)(e)(d)(c)
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Page 280 and 281: 270 Excitability50 mV300 msFigure 7
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Page 334 and 335: 324 Simple ModelsBibliographical No
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342 Bursting(a) cortical chattering
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344 Burstingmembranepotential (mV)-
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346 Burstingvoltage-gatedCa2+-gated
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348 Burstingslow dynamicsneuronvolt
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350 Burstingslow inactivation of in
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352 Bursting9.2.1 Fast-slow burster
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354 Burstingn-nullclinen slow =-0.0
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356 Bursting0maxmembrane potential,
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358 Bursting0membrane potential, V
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360 BurstingI=0I=4.5425 ms 25 mVI=5
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362 Burstingmembrane potential, V (
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364 Bursting9.3 ClassificationIn Fi
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366 Burstingbifurcation of spiking
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368 BurstingFigure 9.26: Putative
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370 Bursting108spikingslow variable
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372 Bursting(a)membrane potential,
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374 Burstingdepending on the type o
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376 BurstingsubcriticalAndronov-Hop
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378 Bursting(a)membrane potential,
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spiking380 Burstingfoldbifurcations
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382 BurstingspikingsupercriticalAnd
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384 Burstingaction potentials cut2
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386 Bursting9.4.3 BistabilitySuppos
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388 BurstingFigure 9.49: The instan
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esting390 Burstingspikesynchronizat
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392 BurstingReview of Important Con
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394 BurstingspikingrestingFigure 9.
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396 Bursting0-10membrane potential,
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398 Bursting0membrane potential, V
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400 BurstingFigure 9.61: A cycle-cy
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402 Bursting28. [Ph.D.] Develop an
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404 Synchronization (see www.izhike
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408 Solutions to Exercises, Chap. 3
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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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