state_and_parameter_discontinuities.rst

State and parameter discontinuities

Physical System

Transient voltage clamp to assess action potential stability.

Model

Force a discontinuous change in potential during an Action Potential.

Simulation

To work properly with variable time step methods, models that change states and/or parameters discontinuously during a simulation must notify NEURON when such events take place. This exercise illustrates the kinds of problems that occur when a model is changed without reinitializing the variable step integrator.

1.

Start with a current pulse stimulated HH patch. We recommend that you try creating this yourself with a brief current pulse at t = 0.1, either in Python or with the GUI tools. Our Python solution is hh_patch.py <code/hh_patch.py>.

2.

Discontinuously change the voltage by +20 mV via

def change():
    print(f'change at {h.t}')
    soma.v += 20

def setup_discontinuities():
    h.cvode.event(2, change)

fih = h.FInitializeHandler(setup_discontinuities)

Note the difference between the fixed and variable step methods.

3.

Replace the change() function with the following and try again:

def change():
    print(f'change at {h.t}')
    soma.v += 20
    h.cvode.re_init()

4.

What happens if you discontinuously change a parameter such as gnabar_hh during the interval 2-3 ms without notifying the variable time step method?

def change(action):
    print(f'change at {h.t}: {action}')
    if action == 'raise':
        soma(0.5).hh.gnabar *= 2
    else:
        soma(0.5).hh.gnabar /= 2
    # h.cvode.re_init()   # should be here for cvode, but see below

def setup_discontinuities():
    h.cvode.event(2, (change, 'raise'))
    h.cvode.event(3, (change, 'lower'))

fih = h.FInitializeHandler(setup_discontinuities)

It will be helpful to use the Crank-Nicholson fixed step method and compare the variable step method with and without the cvode.re_init(). Zoom in around the discontinuity at 2 ms.