brainpy.dyn.channels.ICaN#

class brainpy.dyn.channels.ICaN(size, E=10.0, g_max=1.0, phi=1.0, method='exp_auto', name=None)[source]#

The calcium-activated non-selective cation channel model.

The dynamics of the calcium-activated non-selective cation channel model is given by:

\[\begin{split}\begin{aligned} I_{CAN} &=g_{\mathrm{max}} M\left([Ca^{2+}]_{i}\right) p \left(V-E\right)\\ &M\left([Ca^{2+}]_{i}\right) ={[Ca^{2+}]_{i} \over 0.2+[Ca^{2+}]_{i}} \\ &{dp \over dt} = {\phi \cdot (p_{\infty}-p)\over \tau_p} \\ &p_{\infty} = {1.0 \over 1 + \exp(-(V + 43) / 5.2)} \\ &\tau_{p} = {2.7 \over \exp(-(V + 55) / 15) + \exp((V + 55) / 15)} + 1.6 \end{aligned}\end{split}\]

where \(\phi\) is the temperature factor.

Parameters

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

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

  • phi (float) – The temperature factor.

References

1

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

2

Inoue T, Strowbridge BW (2008) Transient activity induces a long-lasting increase in the excitability of olfactory bulb interneurons. J Neurophysiol 99: 187–199.

__init__(size, E=10.0, g_max=1.0, phi=1.0, method='exp_auto', name=None)[source]#

Methods

__init__(size[, E, g_max, phi, method, name])

current(V, C_Ca, E_Ca)

derivative(p, t, V)

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