Mackey-Glass equation

The Mackey-Glass equation is the nonlinear time delay differential equation

$\frac{dx}{dt} = \beta \frac{ x_{\tau} }{1+{x_{\tau}}^n}-\gamma x, \quad \gamma,\beta,n > 0,$

where $$\beta, \gamma, \tau, n$$ are real numbers, and $$x_{\tau}$$ represents the value of the variable $$x$$ at time $$(t−\tau)$$. Depending on the values of the parameters, this equation displays a range of periodic and chaotic dynamics.

[28]:

import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt

[29]:

bm.set_dt(0.05)

[30]:

class MackeyGlassEq(bp.dyn.NeuGroup):
def __init__(self, num, beta=2., gamma=1., tau=2., n=9.65):
super(MackeyGlassEq, self).__init__(num)

# parameters
self.beta = beta
self.gamma = gamma
self.tau = tau
self.n = n
self.delay_len = int(tau/bm.get_dt())

# variables
self.x = bm.Variable(bm.zeros(num))
self.x_delay = bm.LengthDelay(
self.x,
delay_len=self.delay_len,
initial_delay_data=lambda sh, dtype: 1.2+0.2*(bm.random.random(sh)-0.5)
)
self.x_oldest = bm.Variable(self.x_delay(self.delay_len))

# functions
self.integral = bp.odeint(lambda x, t, x_tau: self.beta * x_tau / (1 + x_tau ** self.n) - self.gamma * x,
method='exp_auto')

def update(self, tdi):
self.x.value = self.integral(self.x.value, tdi.t, self.x_oldest.value, tdi.dt)
self.x_delay.update(self.x.value)
self.x_oldest.value = self.x_delay(self.delay_len)

[31]:

eq = MackeyGlassEq(1, beta=0.2, gamma=0.1, tau=17, n=10)
# eq = MackeyGlassEq(1, )

[32]:

runner = bp.dyn.DSRunner(eq, monitors=['x', 'x_oldest'])
runner.run(1000)

[33]:

plt.plot(runner.mon.x[1000:, 0], runner.mon.x_oldest[1000:, 0])
plt.show()