brainpy.dyn.channels.ICaT_RE#

class brainpy.dyn.channels.ICaT_RE(size, T=36.0, T_base_p=5.0, T_base_q=3.0, g_max=1.75, V_sh=- 3.0, method='exp_auto', name=None)[source]#

The low-threshold T-type calcium current model in thalamic reticular nucleus.

The dynamics of the low-threshold T-type calcium current model 1 2 in thalamic reticular nucleus neurons 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) – The temperature.

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

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

  • g_max (float) – The maximum conductance.

  • V_sh (float) – The membrane potential shift.

References

1

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.

2

Bal, Thierry, and DAVID A. McCORMICK. “Mechanisms of oscillatory activity in guinea‐pig nucleus reticularis thalami in vitro: a mammalian pacemaker.” The Journal of Physiology 468.1 (1993): 669-691.

__init__(size, T=36.0, T_base_p=5.0, T_base_q=3.0, g_max=1.75, V_sh=- 3.0, method='exp_auto', name=None)[source]#

Methods

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

current(V, C_Ca, E_Ca)

dp(p, t, V)

dq(q, 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