Ordinary differential equations: Bifurcations
Saddle-node bifurcation
We consider a simple one-dimensional dynamical system in which a bifurcation parameter is present, namely
b = -1 We begin with a stability analysis of
b = 0 If
b = 1 We end with
Saddle-node bifurcation In the previous three examples we have seen that the number of equilibria and their nature for the differential equation
- If then there are two equilibria of which is attracting and is repelling;
- If then a single semi-stable equilibrium exist;
- If then there is no equilibrium.
Three types of phase lines can be distinguished:
Qualitative changes in the number of equilibria and/or their nature are called bifurcations. The parameter values at which bifurcations occur are called bifurcation values or bifurcation points.
In this example, the number of the equilibria alters when the parameter value changes from negative to positive values. The following animation in a looping illustrates this.
This kind of bifurcation, in which two equilibria get close together when the parameter approaches the bifurcation value and vanish at his point, is called a saddle-node bifurcation. The bifurcation diagram looks as follows:
In the interactive version below you can vary the parameter by dragging the corresponding green triangle along the horizontal axis. In this way you can explore how the slope field and the behaviour of solution curves depend on the bifurcation parameter . By displaying the derivative as a function of you can read off the sign of an equilibrium value and thus determine the location of an equilibrium with respect to the origin.
Normal form of a saddle-node bifurcation In essence, there only two types of saddle-node bifurcations, associated with the so-called normal forms
Generation of action potentials in a quadratic integrate-and-fire neuron As an application of saddle-node bifurcation we study the following mathematical model for a single neuron. In the so-called quadratic integrate-and-fire neuron, the dynamics of the membrane potential of a neuron that is stimulated by a current is described in nondimensional form by
When , the right-hand side of the differential equation has two roots, . The negative root corresponds with an attractor (the resting membrane potential of the neuron) and the positive root with a repeller (the firing threshold ). Depending on the reset value a single spike or periodic spiking will be generated once the membrane potential passes for whatever reason the threshold value .
For , a potential starting above the threshold value first increases to reach the maximum value . Then it is reset in the basin of attraction of the stable equilibrium, which means that the voltage moves to the resting membrane potential . So, one action potential is generated, after which the neuron goes back to the resting state. Note that the situation where is included in this case.
For , the potential is reset in the basin of repelling to an infinite potential. The neuron exhibits periodic spiking because the reset value is also to the right of the unstable equilibrium. The firing frequency is bounded from below because the membrane potential is reset to the right of repeller and consequently the potential drop has a maximum.
For , there are no equilibria and the neuron exhibits periodic spiking. In theory, the firing frequency can be arbitrary small because the sudden drop of the potential value has no maximum.
The figure below, taken from the book Dynamical Systems in Neuroscience of Eugene M. Izhikevich (MIT Press, 2010, Figure 3.35, p. 81), shows the phase lines and potential-time profiles for the quadratic integrate-and-fire neuron model, as well as the behaviour of the neuron described above.