brainpy.dyn.channels.Ca.ICaT_HP1992#

class brainpy.dyn.channels.Ca.ICaT_HP1992(size, keep_size=False, T=36.0, T_base_p=5.0, T_base_q=3.0, g_max=1.75, V_sh=- 3.0, phi_p=None, phi_q=None, method='exp_auto', name=None, mode=NormalMode)[source]#

The low-threshold T-type calcium current model for thalamic reticular nucleus proposed by (Huguenard & Prince, 1992) 1.

The dynamics of the low-threshold T-type calcium current model in thalamic reticular nucleus neurons 1 is given by:

\[\begin{split}I_{CaT} &= g_{max} p^2 q(V-E_{Ca}) \\ {dp \over dt} &= {\phi_p \cdot (p_{\infty}-p)\over \tau_p} \\ &p_{\infty} = {1 \over 1+\exp [-(V+52-V_{sh}) / 7.4]} \\ &\tau_{p} = 3+{1 \over \exp [(V+27-V_{sh}) / 10]+\exp [-(V+102-V_{sh}) / 15]} \\ {dq \over dt} &= {\phi_q \cdot (q_{\infty}-q) \over \tau_q} \\ &q_{\infty} = {1 \over 1+\exp [(V+80-V_{sh}) / 5]} \\ & \tau_q = 85+ {1 \over \exp [(V+48-V_{sh}) / 4]+\exp [-(V+407-V_{sh}) / 50]}\end{split}\]

where \(\phi_p = 5^{\frac{T-24}{10}}\) 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, Callable, Initializer) – The maximum conductance.

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

  • phi_p (optional, float, Array, Callable, Initializer) – The temperature factor for channel \(p\).

  • phi_q (optional, float, Array, Callable, Initializer) – The temperature factor for channel \(q\).

References

1(1,2)

Huguenard JR, Prince DA (1992) A novel T-type current underlies prolonged Ca2+- dependent burst firing in GABAergic neurons of rat thalamic reticular nucleus. J Neurosci 12: 3804–3817.

See also

ICa_p2q_form

__init__(size, keep_size=False, T=36.0, T_base_p=5.0, T_base_q=3.0, g_max=1.75, V_sh=- 3.0, phi_p=None, phi_q=None, 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