brainpy.dyn.channels.Ca.ICaL_IS2008#

class brainpy.dyn.channels.Ca.ICaL_IS2008(size, keep_size=False, 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, mode=NormalMode)[source]#

The L-type calcium channel model proposed by (Inoue & Strowbridge, 2008) 1.

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(1,2)

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.

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=1.0, V_sh=0.0, 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