brainpy.channels.ICaN_IS2008
brainpy.channels.ICaN_IS2008#
- class brainpy.channels.ICaN_IS2008(size, keep_size=False, E=10.0, g_max=1.0, phi=1.0, method='exp_auto', name=None, mode=None)[source]#
The calcium-activated non-selective cation channel model proposed by (Inoue & Strowbridge, 2008) 2.
The dynamics of the calcium-activated non-selective cation channel model 1 2 is given by:
\[\begin{split}\begin{aligned} I_{CAN} &=g_{\mathrm{max}} M\left([Ca^{2+}]_{i}\right) p \left(V-E\right)\\ &M\left([Ca^{2+}]_{i}\right) ={[Ca^{2+}]_{i} \over 0.2+[Ca^{2+}]_{i}} \\ &{dp \over dt} = {\phi \cdot (p_{\infty}-p)\over \tau_p} \\ &p_{\infty} = {1.0 \over 1 + \exp(-(V + 43) / 5.2)} \\ &\tau_{p} = {2.7 \over \exp(-(V + 55) / 15) + \exp((V + 55) / 15)} + 1.6 \end{aligned}\end{split}\]where \(\phi\) is the temperature factor.
- Parameters
References
- 1
Destexhe, Alain, et al. “A model of spindle rhythmicity in the isolated thalamic reticular nucleus.” Journal of neurophysiology 72.2 (1994): 803-818.
- 2(1,2)
Inoue T, Strowbridge BW (2008) Transient activity induces a long-lasting increase in the excitability of olfactory bulb interneurons. J Neurophysiol 99: 187–199.
- __init__(size, keep_size=False, E=10.0, g_max=1.0, phi=1.0, method='exp_auto', name=None, mode=None)[source]#
Methods
__init__
(size[, keep_size, E, g_max, phi, ...])clear_input
()cpu
()Move all variable into the CPU device.
cuda
()Move all variables into the GPU device.
current
(V, C_Ca, E_Ca)derivative
(p, t, V)get_delay_data
(identifier, delay_step, *indices)Get delay data according to the provided delay steps.
load_state_dict
(state_dict[, warn])Copy parameters and buffers from
state_dict
into this module and its descendants.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[, node_cls])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.
state_dict
()Returns a dictionary containing a whole state of the module.
to
(device)Moves all variables into the given device.
tpu
()Move all variables into the TPU device.
train_vars
([method, level, include_self])The shortcut for retrieving all trainable variables.
tree_flatten
()Flattens the object as a PyTree.
tree_unflatten
(aux, dynamic_values)New in version 2.3.1.
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
Global delay data, which stores the delay variables and corresponding delay targets.
mode
Mode of the model, which is useful to control the multiple behaviors of the model.
name
Name of the model.
varshape