brainpy.dyn.channels.Ca.ICaHT_HM1992#

class brainpy.dyn.channels.Ca.ICaHT_HM1992(size, keep_size=False, T=36.0, T_base_p=3.55, T_base_q=3.0, g_max=2.0, V_sh=25.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The high-threshold T-type calcium current model proposed by (Huguenard & McCormick, 1992) 1.

The high-threshold T-type calcium current model is adopted from 1. Its dynamics is given by

\[\begin{split}\begin{aligned} I_{\mathrm{Ca/HT}} &= g_{\mathrm{max}} p^2 q (V-E_{Ca}) \\ {dp \over dt} &= {\phi_{p} \cdot (p_{\infty} - p) \over \tau_{p}} \\ &\tau_{p} =\frac{1}{\exp \left(\frac{V+132-V_{sh}}{-16.7}\right)+\exp \left(\frac{V+16.8-V_{sh}}{18.2}\right)}+0.612 \\ & p_{\infty} = {1 \over 1+exp[-(V+59-V_{sh}) / 6.2]} \\ {dq \over dt} &= {\phi_{q} \cdot (q_{\infty} - h) \over \tau_{q}} \\ & \begin{array}{l} \tau_q = \exp \left(\frac{V+467-V_{sh}}{66.6}\right) \quad V< (-80 +V_{sh})\, mV \\ \tau_q = \exp \left(\frac{V+22-V_{sh}}{-10.5}\right)+28 \quad V \geq (-80 + V_{sh})\, mV \end{array} \\ &q_{\infty} = {1 \over 1+exp[(V+83 -V_{shift})/4]} \end{aligned}\end{split}\]

where \(phi_p = 3.55^{\frac{T-24}{10}}\) and \(phi_q = 3^{\frac{T-24}{10}}\) are temperature-dependent factors (\(T\) is the temperature in Celsius), \(E_{Ca}\) is the reversal potential of Calcium channel.

Parameters
  • T (float, Array) – The temperature.

  • T_base_p (float, Array) – The base temperature factor of \(p\) channel.

  • T_base_q (float, Array) – The base temperature factor of \(q\) channel.

  • g_max (float, Array, Initializer, Callable) – The maximum conductance.

  • V_sh (float, Array, Initializer, Callable) – The membrane potential shift.

References

1(1,2)

Huguenard JR, McCormick DA (1992) Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons. J Neurophysiol 68:1373–1383.

See also

ICa_p2q_form

__init__(size, keep_size=False, T=36.0, T_base_p=3.55, T_base_q=3.0, g_max=2.0, V_sh=25.0, method='exp_auto', name=None, mode=NormalMode)[source]#

Methods

__init__(size[, keep_size, T, T_base_p, ...])

clear_input()

current(V, C_Ca, E_Ca)

dp(p, t, V)

dq(q, t, V)

f_p_inf(V)

f_p_tau(V)

f_q_inf(V)

f_q_tau(V)

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset(V, C_Ca, E_Ca[, batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state(V, C_Ca, E_Ca[, batch_size])

Reset function which reset the states in the model.

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(tdi, V, C_Ca, E_Ca)

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

varshape