Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience: Dynamical Systems in Neuroscience:

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276 ExcitabilityBibliographical NotesThere is no universally accepted definition of excitability. Our definition is consistentwith the one involving ε-pseudo-orbits (Izhikevich 2000). R. FitzHugh (1955, 1960,1976) pioneered geometrical analyses of phase portraits of neuronal models with theview to understand their neuro-computational properties. It is amazing that such importantneuro-computational properties as all-or-none action potentials, firing thresholds,and integration of EPSPs are still introduced and illustrated using the Hodgkin-Huxley model, which according to FitzHugh, cannot have these properties. Throughoutthis chapter we follow Izhikevich (2000) to compare and contrast neuro-computationalproperties of integrators and resonators.The frozen noise experiment in Fig. 7.24 was pioneered by Bryant and Segundo in1976, but due to an interesting quirk of history, it is better known at present as theMainen-Sejnowski (1995) experiment (despite the fact that the latter paper refers tothe former). Post-inhibitory facilitation was pointed out by Luk and Aihara (2000),Izhikevich (2001). John Rinzel suggested to call it “post-inhibitory exaltation” (ina similar vain, the phenomenon in Fig. 7.49b may be called “post-excitatory hesitation”).Richardson et al. (2003) pointed out that frequency preference and resonanceoccurs without subthreshold oscillations when the system is near the transition froman integrator to a resonator.The Hodgkin’s classification of neuronal excitability can be applied to classify anyrhythmic system, e.g., contractions of uterus during labor. Typically, the contractionsstart with low frequency that gradually increases — Class 1 excitability. The author’swife had to be induced pharmacologically to evoke labor contractions, which is a typicalmedical intervention when the baby is overdue. The contraction monitor showed asinusoidal signal with constant period, around 2 minutes, but slowly growing amplitude— Class 2 excitability via supercritical Andronov-Hopf bifurcation! Since the motherhad an advance degree in applied mathematics, the author waited for a 1-minute periodof quiescence between the contractions and managed to explain to the delivering motherthe basic relationship between bifurcations and excitability. Five years later, induceddelivery of the author’s second daughter resulted in the same supercritical Andronov-Hopf bifurcation. The author reminded this concept to the mother and explained it tothe obstetrician minutes after the delivery.Exercises1. When can the FitzHugh-Nagumo model (4.11, 4.12) exhibit inhibition-inducedspiking, such as the one in Fig. 7.32?2. (French ducks) Numerically investigate the quasi-threshold in the FitzHugh-Nagumo model (4.11, 4.12). How is it related to the French duck (canard; seeEckhaus 1983) limit cycles discussed in Sect. 6.3.4?

Excitability 277Figure 7.55: Ex. 6: Zero frequency firing near subcritical Andronov-Hopf bifurcationin the I Na,p +I K -model with parameters as in Fig. 6.16 and a high-threshold slow K +current (g K,slow = 25, τ K,slow = 10 ms, n ∞,slow (V ) has V 1/2 = −10 mV and k = 5 mV.)3. (Noise-induced oscillations) Consider the systemż = (−ε + iω)z + εI(t) , z ∈ C (7.3)which has a stable focus equilibrium z = 0 and is subject to a weak noisy inputεI(t). Show that the system exhibits sustained noisy oscillations with an averageamplitude |I ∗ (ω)|, whereI ∗ 1(ω) = limT →∞ T∫ T0e −iωt I(t) dtis the Fourier coefficient of I(t) corresponding to the frequency ω.4. (Frequency preference) Show that a system exhibiting damped oscillation withfrequency ω is sensitive to an input having frequency ω in its power spectrum.(Hint: use Ex. 3.)5. (Rush and Rinzel 1996) Use the phase portrait of the reduced Hodgkin-Huxleymodel in Fig. 5.21 to explain some small but noticeable latencies in Fig. 7.26.6. The neuronal model in Fig. 7.55 has a high-threshold slow persistent K + current.Its resting state undergoes a subcritical Andronov-Hopf bifurcation, yet it canfire low-frequency spikes, and hence exhibits Class 1 excitability. Explain. (Hint:Show numerically that the model is near a certain codimention-2 bifurcationinvolving a homoclinic orbit).

Excitability 277Figure 7.55: Ex. 6: Zero frequency fir<strong>in</strong>g near subcritical Andronov-Hopf bifurcation<strong>in</strong> the I Na,p +I K -model with parameters as <strong>in</strong> Fig. 6.16 and a high-threshold slow K +current (g K,slow = 25, τ K,slow = 10 ms, n ∞,slow (V ) has V 1/2 = −10 mV and k = 5 mV.)3. (Noise-<strong>in</strong>duced oscillations) Consider the systemż = (−ε + iω)z + εI(t) , z ∈ C (7.3)which has a stable focus equilibrium z = 0 and is subject to a weak noisy <strong>in</strong>putεI(t). Show that the system exhibits susta<strong>in</strong>ed noisy oscillations with an averageamplitude |I ∗ (ω)|, whereI ∗ 1(ω) = limT →∞ T∫ T0e −iωt I(t) dtis the Fourier coefficient of I(t) correspond<strong>in</strong>g to the frequency ω.4. (Frequency preference) Show that a system exhibit<strong>in</strong>g damped oscillation withfrequency ω is sensitive to an <strong>in</strong>put hav<strong>in</strong>g frequency ω <strong>in</strong> its power spectrum.(H<strong>in</strong>t: use Ex. 3.)5. (Rush and R<strong>in</strong>zel 1996) Use the phase portrait of the reduced Hodgk<strong>in</strong>-Huxleymodel <strong>in</strong> Fig. 5.21 to expla<strong>in</strong> some small but noticeable latencies <strong>in</strong> Fig. 7.26.6. The neuronal model <strong>in</strong> Fig. 7.55 has a high-threshold slow persistent K + current.Its rest<strong>in</strong>g state undergoes a subcritical Andronov-Hopf bifurcation, yet it canfire low-frequency spikes, and hence exhibits Class 1 excitability. Expla<strong>in</strong>. (H<strong>in</strong>t:Show numerically that the model is near a certa<strong>in</strong> codimention-2 bifurcation<strong>in</strong>volv<strong>in</strong>g a homocl<strong>in</strong>ic orbit).

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