# brainpy.dyn.channels.ICaL#

class brainpy.dyn.channels.ICaL(size, T=36.0, T_base_p=3.55, T_base_q=3.0, g_max=1.0, V_sh=0.0, method='exp_auto', name=None)[source]#

The L-type calcium channel model.

The L-type calcium channel model is adopted from (Inoue, et, al., 2008) 1. Its dynamics is given by:

$\begin{split}I_{CaL} &= g_{max} p^2 q(V-E_{Ca}) \\ {dp \over dt} &= {\phi_p \cdot (p_{\infty}-p)\over \tau_p} \\ &p_{\infty} = {1 \over 1+\exp [-(V+10-V_{sh}) / 4.]} \\ &\tau_{p} = 0.4+{0.7 \over \exp [(V+5-V_{sh}) / 15]+\exp [-(V+5-V_{sh}) / 15]} \\ {dq \over dt} &= {\phi_q \cdot (q_{\infty}-q) \over \tau_q} \\ &q_{\infty} = {1 \over 1+\exp [(V+25-V_{sh}) / 2]} \\ &\tau_q = 300 + {100 \over \exp [(V+40-V_{sh}) / 9.5]+\exp [-(V+40-V_{sh}) / 9.5]}\end{split}$

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) – 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

Inoue, Tsuyoshi, and Ben W. Strowbridge. “Transient activity induces a long-lasting increase in the excitability of olfactory bulb interneurons.” Journal of neurophysiology 99, no. 1 (2008): 187-199.

__init__(size, T=36.0, T_base_p=3.55, T_base_q=3.0, g_max=1.0, V_sh=0.0, 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. 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. vars([method, level, include_self]) Collect all variables in this node and the children nodes.

Attributes

 global_delay_targets global_delay_vars name steps