# Phase Equations

Many times equations that you are interested in are not defined on all of Euclidean space but instead are restricted to lie in some other space such as a torus or a cylinder. XPP has methods that make plotting this type of equation easy. Recall that by averaging we could reduce a pair of coupled oscillators to a pair of equations for the phases and that this set of equations lies on a torus. We performed this reduction on the Morris-Lecar equations coupled with a synapse and found that the interaction function was essentially sinusoidal. In this short section, we look at a pair of symmetrically coupled phase equations that are nearly identical except for a small difference in frequency:

Here g is the scaled coupling strength or maximal synaptic conductance and Delta is a difference in frequency due, for example, to a different bias current to neuron 1. Fire up XPP for this model so we can look at some special plotting flags for models that lie on tori.

Notice on the menu item (phAse space). This lets us place the two variables in our maodel, X1, X2 on a circle. Click (phAse space) and then (Choose). Click (Enter) to accept the period of 2 Pi, since our model is 2 Pi-periodic. A new window appears. Use the mouse to click on both variables. Note a little X appears next to the names of the variables. (This tells XPP that each time one of these variables leaves the interval [0, 2 Pi] that it should be shifted by some multiple of 2 Pi to get it back in the interval. The plotting of such variables is "smart" enough to know not to draw extraneous lines for where the variables seem "discontinuous" because they really are not: 0 and 2 Pi are the same on a circle. ) Click (Ok) when both variables have X next to them. (Note: we could have just clicked (phAse space) (All) and that would make all plottable quantities lie on the circle, but I wanted you to see the little choice window.)

Now choose a two-dimensional view with X1, X2 as the variables and [0, 6.3] and [0, 6.3] as the window dimensions. (Click (V) (2) and fill in accordingly.)

Go into the nUmerics menu and change the total time to 200 and the time-step to Dt=.2 . Go back to the main menu and use the mouse to click in some initial data in the plane. (Use (I) (M) and position the mouse.) Trajectories all seem to end up on the diagonal line X1 = X2. This says that the oscillators always tend to synchronize since the phase-difference between them is zero. This is just what the theory told us: for this particular phase interaction function, synchrony is stable.

NOTE: If we had not defined X1, X2 to lie on the torus, we would have gotten an Out of Bounds warning after integrating the equations. Typically, phase variables monotonically increase in a nearly linear fashion with time as they cycle around their periodic phase-space. Thus the utility of using the (phAse space) option in XPP is clear.

Go back to the nUmerics menu and change Dt to -.2. This means integrate the equations backwards in time. Escape back to the main menu and click on initial conditions with the mouse. You will see that all solutions tend to a shifted diagonal line that intersects the coordinate axes at Pi. This is the other phase-locked solution and it represents a phase-shift of Pi. Since all solutions tend to it backwards in time this means it is unstable. Again as the theory told us, anti-phase solutions are unstable.

Return to the nUmerics menu and change Dt back to .2 and return to the main menu. Change the frequency difference, delta from 0 to .3 and intergate the equations with different initial conditions. Note that it still goes to a diagonal line, but this intersects the X1 axis at slightly less than 1. Thus, the phase difference between the two oscillators is about 0.9 with X1 leading X2. This is because X2 has a faster intrinsic rhythm since delta>0. Make delta negative and you will see that X2 leads X1. In either case, there is a stable phase-locked solution for this model.

## Poincare Maps I

Now, change delta to .5. And integrate the equations. You should see the trajectory curve all over but not converge to a phase-locked solution. In fact, the trajectory eventually densely covers the torus. The motion is "quasi-periodic." The frequency difference between the two oscillators is too great for the coupling to lock them.

We can attempt to understand this behavior a little better by reducing the equations via a map: the Poincare section. Imagine a vertical line through X1=3 for example. Looking at the phase plane, one sees that the trajectory hits this line at different values of X2. Given a value of X2 on the line, the dynamics yields a new value of X2 the next time it hits that line. The line is called a Poincare Section and the map X2(n-1) --> X2(n) is called the Poincare Map. The Poincare map always reduces the dimension of the system by one and changes the dynamics from continuous to discrete.

XPP provides a tool for studying Poincare sections. Click on (nUmerics) to get to the numerics menu and then on (Poincare) to set up the Poincare map. Choose (Section) since we are interested in when the trajectory crosses a particular value of X1 and not on maxima or minima. XPP is presently limited to looking at sections that are along the coordinate axes of the dynamic variables. (Eventually, I will make it more general.)

Change the total amount of integration time from 200 to 2000. Click (Esc) to go back to the main menu and then integrate starting from the end of the last integration by clicking (I) (L). You should see a bunch of lines drawn from the left to the section. Since the Poincare map is discrete, we should plot points instead of lines. Click (G) (E) and type in 0 to edit the principle curve. A window pops up. Change the Linetype entry from 1 to 0 to indicate points are to be drawn. Click on (Ok). Click on (R) to restore the graph. You should see a sequence of points along the X1=3 axis. Click on (Xi vs T) and type X2 followed by (Enter) to see the points plotted as a function of time.

### Invariant density

NOTE This is optional: you can skip to the next section or follow along.

They are not really uniformly distributed, but seem to be denser around X2=1.5 Let's verify that now by computing a Histogram for the data.

• Since the histogram will have numers larger the 2 Pi, we will turn off the XPP (phAse space) by clicking (A) (N).
• We will also want solid lines, so click (G) (E) and type 0 to edit the main curve. Then change the Line type to 1 and click (Ok).
• Enter the nUmerics menu and choose (stocHastic) and then (Histogram). (Click (U) (H) (H) )
• Choose the default of 10 for the number of bins, 0 for the low, 6.28 for the high, and X2 for the variable. Just type (Enter) for the condition as there are none. This will produce a histogram for the values of X2 between 0 and 6.28 with 10 bins. The histogram is always put in the second column and the bins in the first column.
• Click (Esc) to return to the main window, click (Xi vs T) and type in X1 to plot the second versus the first column. You should see a graph with a big peak between 1 and 2 and another one at around 5. This shows that the density is not uniform. You can view the actual histogram by looking at the first two columns of the browser.
• Click (U) to return to the numerics menu and click (stocHastic) (Data) to load the data back. (You could recompute the histogram at this point using a larger number of bins to get a better idea of the peak of the invariant density. Too large a bin number is useless, so be careful. Make sure you get the data back when you are done.) Click (Esc) to go back to the main menu.
• Change the line type back to points (Click (G) (E) 0 ...) and change the phase space back to a torus (Click (phAse space) (All) (Enter) as a shortcut.)

### Getting the standard map

Now I assume you have the data from the Poincare map in the browser. From our discussion above, it would appear the each succesive point in the X2 column depends on the previous point. (In fact this must be true, since we start with the initial conditions at a value of X2 and with X1=3 so by the uniqueness theorem for differential equations, we get a unique value of X2 the next time the trajectory crosses X1=3. ) Thus, to see the nature of this map, it would be useful to plot the (n+1)st point versus the nth point. This is easily done in XPP.

First set up the window so that it is a 2D plot with X2 on both axes: Click (View axes) (2d) and put X2 on both axes which should both be windowed between 0 and 6.3. You will see a diagonal line representing X2 vs X2 as expected. (It will not be a solid line, but, instead, a series of points.) Now, click (nUmerics) to enter the numerics menu and then click on (rUelle plot) to set negative shifts of the data. (This is named for David Ruelle who showed that dynamical sysyems could be reconstructed from time series by looking at the data plotted against a shifted version of itself. ) We will shift the X coordinate by 1 so that we will then plot X2(n) vs X2(n-1) where X2(n) is the y-coordinate and X2(n-1) is the x-coordinate. Thus, we can see if there is a reasonable looking map.

At the command-line prompt, set

• x-axis shift: 1
• y-axis shift: 0
• z-axis shift: 0
and then (Esc) to the main menu and click (Restore) to redraw the graph. You should see something like:

with or without the diagonal line. Clearly, there is some sort of smooth function plotted here.

#### Cobwebs

To understand what is happening with this map, we can use a technique called cobwebbing which is often used in one-dimensional discrete dynamical systems. Unfortunately, XPP does not support cobwebbing but it is something you can do on a piece of paper once you have the map. Here is an example for the present map:

Given a starting point, you compute the new value using the map (1), then project back down by reflecting on the line y=x to get the next point (2) and so on.

### A bit about discrete dynamical systems

This map is actually very close to the so-called standard map in the weakly perturbed regime:

This is an example of a discrete dynamical system. The ODE file is similar to that of an ordinary differential equation. XPP has a method for solving this type of model. Instead of integrating the equations, XPP iterates them. Since this is again a model on the circle, you should invoke the (phAse space) command followed by (All) and then accepting the default of 2 Pi. Next, change the linestyle to 0 (Click (Graphics) (Edit) 0 and change the linestyle entry to 0.) Finally, go into the numerics menu and change the total amount of time to, say, 1000, and change the method of integration to (Discrete). This tells XPP to treat this iteratively rather than as a continuous dynamical system. (Esc) to the main menu and integrate the equations. Plot using (Xi vs t). It looks similar to what you have already plotted for the Poincare map. The standard map is a good approximation for out Poincare map as long as the parameter g in the standard map is small. If this parameter is too large, then even with delta=0 the map becomes chaotic an interesting phenomenon that we will look at shortly.

### Homework 3.3

• Try looking at a pair of phase models like the one we just explored, but add one new term to the interaction function:
```h(phi) = a0 + a1*cos(phi) + b1*sin(phi)+a2*sin(2*phi)
```
First study the stable and unstable phase-locked behavior for delta=0 as you vary the amplitude of a2 letting it be both positive and negative. Then, let delta be such that phase-locking is lost. Study the Poincare map.
• Play around with the standard map with g increasingly larger, say up to -5. Integrate for at least 2000 timesteps. Use the Ruelle plots to look at Y(n) vs Y(n-k) for k=1,2. Deduce that the map is not invertible.

Note that if Y(n)=Y(n-1) then there is a fixed point for the map. Use the Ruelle plot to show there is only one fixed point.

Similarly, if Y(n)=Y(n-2) that means that there is a fixed point of the second iterate, a so called period two point. For delta=0,g=-5 use the Ruelle plot to determine how many period two points there are. ( Hint: First plot without a shift to get the diagonal line. Then plot with a shift without erasing the first line.)