brainpy.dyn.channels.IAHP

class brainpy.dyn.channels.IAHP(size, E=- 80.0, g_max=1.0, method='exp_auto', name=None)

The calcium-dependent potassium current model.

The dynamics of the calcium-dependent potassium current model is given by:

\[\begin{split}\begin{aligned} I_{AHP} &= g_{\mathrm{max}} p (V - E) \\ {dp \over dt} &= {p_{\infty}(V) - p \over \tau_p(V)} \\ p_{\infty} &=\frac{48[Ca^{2+}]_i}{\left(48[Ca^{2+}]_i +0.09\right)} \\ \tau_p &=\frac{1}{\left(48[Ca^{2+}]_i +0.09\right)} \end{aligned}\end{split}\]

where \(E\) is the reversal potential, \(g_{max}\) is the maximum conductance.

Parameters

• g_max (float) – The maximal conductance density (\(mS/cm^2\)).

• E (float) – The reversal potential (mV).

References

1

Contreras, D., R. Curró Dossi, and M. Steriade. “Electrophysiological properties of cat reticular thalamic neurones in vivo.” The Journal of Physiology 470.1 (1993): 273-294.

2

Mulle, Ch, Anamaria Madariaga, and M. Deschênes. “Morphology and electrophysiological properties of reticularis thalami neurons in cat: in vivo study of a thalamic pacemaker.” Journal of Neuroscience 6.8 (1986): 2134-2145.

3

Avanzini, G., et al. “Intrinsic properties of nucleus reticularis thalami neurones of the rat studied in vitro.” The Journal of Physiology 416.1 (1989): 111-122.

4

Destexhe, Alain, et al. “A model of spindle rhythmicity in the isolated thalamic reticular nucleus.” Journal of neurophysiology 72.2 (1994): 803-818.

5

Vijayan S, Kopell NJ (2012) Thalamic model of awake alpha oscillations and implications for stimulus processing. Proc Natl Acad Sci USA 109: 18553–18558.

__init__(size, E=- 80.0, g_max=1.0, method='exp_auto', name=None)

Methods

__init__(size[, E, g_max, method, name])
current(V, C_Ca, E_Ca)
derivative(p, t, V, C_Ca, E_Ca)
get_delay_data(name, delay_step, *indices)	Get delay data according to the provided delay steps.
ints([method])	Collect all integrators in this node and the children nodes.
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
register_delay(name, delay_step, delay_target)	Register delay variable.
register_implicit_nodes(nodes)
register_implicit_vars(variables)
reset(V, C_Ca, E_Ca)	Reset function which reset the whole variables in the model.
reset_delay(name, delay_target)	Reset the delay variable.
save_states(filename[, variables])	Save the model states.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
unique_name([name, type_])	Get the unique name for this object.
update(t, dt, V, C_Ca, E_Ca)	The function to specify the updating rule.
update_delay(name, delay_data)	Update the delay according to the delay data.
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

global_delay_vars
name
steps