brainpy.synapses.GapJunction

brainpy.synapses.GapJunction#

class brainpy.synapses.GapJunction(pre, post, conn, comp_method='dense', g_max=1.0, name=None)[source]#
__init__(pre, post, conn, comp_method='dense', g_max=1.0, name=None)[source]#

Methods

__init__(pre, post, conn[, comp_method, ...])

add_aft_update(key, fun)

Add the after update into this node

add_bef_update(key, fun)

Add the before update into this node

add_inp_fun(key, fun[, label, category])

Add an input function.

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

clear_input(*args, **kwargs)

Empty function of clearing inputs.

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

get_aft_update(key)

Get the after update of this node by the given key.

get_bef_update(key)

Get the before update of this node by the given key.

get_delay_data(identifier, delay_pos, *indices)

Get delay data according to the provided delay steps.

get_delay_var(name)

get_inp_fun(key)

Get the input function.

get_local_delay(var_name, delay_name)

Get the delay at the given identifier (name).

has_aft_update(key)

Whether this node has the after update of the given key.

has_bef_update(key)

Whether this node has the before update of the given key.

jit_step_run(i, *args, **kwargs)

The jitted step function for running.

load_state(state_dict, **kwargs)

Load states from a dictionary.

load_state_dict(state_dict[, warn, compatible])

Copy parameters and buffers from state_dict into this module and its descendants.

nodes([method, level, include_self])

Collect all children nodes.

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

register_local_delay(var_name, delay_name[, ...])

Register local relay at the given delay time.

reset(*args, **kwargs)

Reset function which reset the whole variables in the model (including its children models).

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

save_state(**kwargs)

Save states as a dictionary.

setattr(key, value)

rtype:

None

state_dict(**kwargs)

Returns a dictionary containing a whole state of the module.

step_run(i, *args, **kwargs)

The step run function.

sum_current_inputs(*args[, init, label])

Summarize all current inputs by the defined input functions .current_inputs.

sum_delta_inputs(*args[, init, label])

Summarize all delta inputs by the defined input functions .delta_inputs.

sum_inputs(*args, **kwargs)

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

tracing_variable(name, init, shape[, ...])

Initialize the variable which can be traced during computations and transformations.

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)

Unflatten the data to construct an object of this class.

unique_name([name, type_])

Get the unique name for this object.

update()

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

after_updates

before_updates

cur_inputs

current_inputs

delta_inputs

implicit_nodes

implicit_vars

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

supported_modes

Supported computing modes.