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Dynamical Systems in Neuroscience:

Dynamical Systems in Neuroscience:

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128 Two-Dimensional <strong>Systems</strong>abcdFigure 4.38: Determ<strong>in</strong>e the approximate direction of the vector field <strong>in</strong> each regionbetween the nullcl<strong>in</strong>es. Cont<strong>in</strong>uous (dashed) curve is the x-nullcl<strong>in</strong>e (y-nullcl<strong>in</strong>e), andthe direction of vector field <strong>in</strong> one region is <strong>in</strong>dicated by the arrow.trajectories, as well as other representative trajectories. Estimate the signs ofeigenvalues at each equilibrium.4. Prove the classification diagram <strong>in</strong> Fig. 4.15.5. (van der Pol oscillator) Determ<strong>in</strong>e nullcl<strong>in</strong>es and draw phase portrait of the vander Pol oscillator (given <strong>in</strong> the Liénard (1928) form)where b > 0 is a parameter.ẋ = x − x 3 /3 − y ,ẏ = bx ,6. (Bonhoeffer–van der Pol oscillator) Determ<strong>in</strong>e the nullcl<strong>in</strong>es and sketch representativephase portraits of the Bonhoeffer–van der Pol oscillatorẋ = x − x 3 /3 − y ,ẏ = b(x − a) − cy ,<strong>in</strong> the case of c = 0. Treat a and b > 0 as parameters.7. (H<strong>in</strong>dmarsh-Rose spik<strong>in</strong>g neuron) The follow<strong>in</strong>g system is a generalization of theFitzHugh-Nagumo model (H<strong>in</strong>dmarsh and Rose 1982)ẋ = f(x) − y + I ,ẏ = g(x) − y ,

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