Dynamical Systems in Neuroscience:
Dynamical Systems in Neuroscience: Dynamical Systems in Neuroscience:
426 Solutions to Exercises, Chap. 611. The projection onto the v 1 -axis is described by the equationẋ = λ 1 x , x(0) = a .The trajectory leaves the square when x(t) = ae λ1t = 1; that is, whent = − 1 ln a = − 1 ln τ(I − I b ) .λ 1 λ 112. Equation (6.13) has two bifurcation parameters, b and v, and the saddle-node homoclinicbifurcation occurs when b = b sn and v = V sn . The saddle-node bifurcation curve is the straightline b = b sn (any v). This bifurcation is on an invariant circle when v < V sn and off otherwise.When b > b sn , there are no equilibria and the normal form exhibits periodic spiking. Whenb < b sn , the normal form has two equilibria,node{ }} {V sn − √ c|b − b sn |/aandsaddle{ }} {V sn + √ c|b − b sn |/a .The saddle homoclinic orbit bifurcation occurs when the voltage is reset to the saddle, i.e.,whenv = V sn + √ c|b − b sn |/a .13. The Jacobian matrix at an equilibrium is( 2v −1L =ab −a).Saddle-node condition det L = −2va + ab = 0, results in v = b/2. Since v is an equilibrium, itsatisfies v 2 − bv + I = 0, hence b 2 = 4I. Andronov-Hopf condition tr L = 2v − a = 0 results inv = a/2, hence a 2 /4 − ab/2 + I = 0. The bifurcation occurs when det L > 0, resulting in a < b.Combining the saddle-node and Andronov-Hopf conditions results in the Bogdanov-Takensconditions.14. Change of variables (6.5), v = x and u = √ µy, transforms the relaxation oscillator into theformẋ = f(x) − √ (µyfẏ = √ with the Jacobian L =′ (b) − √ )µ√µ(x − b)µ 0at the equilibrium v = x = b, u = √ µy = f(b). The Andronov-Hopf bifurcation occurs whentr L = f ′ (b) = 0 and det L = µ > 0. Using (6.7), we find that it is supercritical when f ′′′ (b) < 0and subcritical when f ′′′ (b) > 0.15. The Jacobian matrix at the equilibrium, which satisfies F (v) − bv = 0, is( )F′−1L =.µb −µThe Andronov-Hopf bifurcation occurs when tr L = F ′ − µ = 0 (hence F ′ = µ) and det L =ω 2 = µb − µ 2 > 0 (hence b > µ). The change of variables (6.5), v = x and u = µx + ωy,transforms the system into the formẋ = −ωy + f(x)ẏ = ωx + g(x)where f(x) = F (x) − µx and g(x) = µ[bx − F (x)]/ω. The result follows from (6.7).
Solutions to Exercises, Chap. 7 42716. The change of variables (6.5) converts the system into the formThe result follows from (6.7).ẋ = F (x) + linear termsẏ = µ(G(x) − F (x))/ω + linear terms17. The change of variables (6.5), v = x and u = µx + ωy, converts the system intoThe result follows from (6.7).ẋ = F (x) − x(µx + ωy) + linear termsẏ = µ[G(x) − F (x) + x(µx + ωy)]/ω18. The system undergoes Andronov-Hopf bifurcation when F v = −µG u and F u G v < −µG 2 u. Weperform all the steps from (6.4) to (6.7) disregarding linear terms (they do not influence a)and the terms of the order o(µ). Let ω = √ −µF u G v + O(µ), then u = (µG u x − ωy)/F u =−ωy/F u + O(µ), andandf(x, y) = F (x, −ωy/F u + O(µ)) = F (x, 0) − F u (x, 0)ωy/F u + O(µ)The result follows from (6.7).g(x, y) = (µ/ω)[G u F (x, 0) − F u G(x, 0)] + O(µ) .Solutions to Chapter 71. Take c < 0 so that the slow w-nullcline has a negative slope.2. The quasi-threshold contains the union of canard solutions.3. The change of variables z = e iωt u transforms the system into the formwhich can be averaged, yielding˙u = ε{−u + e −iωt I(t)} ,˙u = ε{−u + I ∗ (ω)} .Apparently, the stable equilibrium u = I ∗ (ω) corresponds to the sustained oscillation z =e iωt I ∗ (ω).4. The existence of damped oscillations with frequency ω implies that the system has a focusequilibrium with eigenvalues −ε ± iω, where ε > 0. The local dynamics near the focus can berepresented in the form (7.3). The rest of the proof is the same as the one for Ex. 3.5. Even though the slow and the fast nullclines in Fig. 5.21 intersect only in one point, theycontinue to be close and parallel to each other in the voltage range 10 mV to 30 mV. Such aproximity creates a tunneling effect (Rush and Rinzel 1996) that prolongs the time spent nearthose nullclines.6. (Shilnikov-Hopf bifurcation) The model is near a co-dimension-2 bifurcation having a homoclinicorbit to an equilibrium undergoing subcritical Andronov-Hopf bifurcation, as we illustratein Fig. 10.28. Many weird phenomena could happen near bifurcations of co-dimension 2or higher.
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- Page 418 and 419: 408 Solutions to Exercises, Chap. 3
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- 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
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Solutions to Exercises, Chap. 7 42716. The change of variables (6.5) converts the system <strong>in</strong>to the formThe result follows from (6.7).ẋ = F (x) + l<strong>in</strong>ear termsẏ = µ(G(x) − F (x))/ω + l<strong>in</strong>ear terms17. The change of variables (6.5), v = x and u = µx + ωy, converts the system <strong>in</strong>toThe result follows from (6.7).ẋ = F (x) − x(µx + ωy) + l<strong>in</strong>ear termsẏ = µ[G(x) − F (x) + x(µx + ωy)]/ω18. The system undergoes Andronov-Hopf bifurcation when F v = −µG u and F u G v < −µG 2 u. Weperform all the steps from (6.4) to (6.7) disregard<strong>in</strong>g l<strong>in</strong>ear terms (they do not <strong>in</strong>fluence a)and the terms of the order o(µ). Let ω = √ −µF u G v + O(µ), then u = (µG u x − ωy)/F u =−ωy/F u + O(µ), andandf(x, y) = F (x, −ωy/F u + O(µ)) = F (x, 0) − F u (x, 0)ωy/F u + O(µ)The result follows from (6.7).g(x, y) = (µ/ω)[G u F (x, 0) − F u G(x, 0)] + O(µ) .Solutions to Chapter 71. Take c < 0 so that the slow w-nullcl<strong>in</strong>e has a negative slope.2. The quasi-threshold conta<strong>in</strong>s the union of canard solutions.3. The change of variables z = e iωt u transforms the system <strong>in</strong>to the formwhich can be averaged, yield<strong>in</strong>g˙u = ε{−u + e −iωt I(t)} ,˙u = ε{−u + I ∗ (ω)} .Apparently, the stable equilibrium u = I ∗ (ω) corresponds to the susta<strong>in</strong>ed oscillation z =e iωt I ∗ (ω).4. The existence of damped oscillations with frequency ω implies that the system has a focusequilibrium with eigenvalues −ε ± iω, where ε > 0. The local dynamics near the focus can berepresented <strong>in</strong> the form (7.3). The rest of the proof is the same as the one for Ex. 3.5. Even though the slow and the fast nullcl<strong>in</strong>es <strong>in</strong> Fig. 5.21 <strong>in</strong>tersect only <strong>in</strong> one po<strong>in</strong>t, theycont<strong>in</strong>ue to be close and parallel to each other <strong>in</strong> the voltage range 10 mV to 30 mV. Such aproximity creates a tunnel<strong>in</strong>g effect (Rush and R<strong>in</strong>zel 1996) that prolongs the time spent nearthose nullcl<strong>in</strong>es.6. (Shilnikov-Hopf bifurcation) The model is near a co-dimension-2 bifurcation hav<strong>in</strong>g a homocl<strong>in</strong>icorbit to an equilibrium undergo<strong>in</strong>g subcritical Andronov-Hopf bifurcation, as we illustrate<strong>in</strong> Fig. 10.28. Many weird phenomena could happen near bifurcations of co-dimension 2or higher.