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brainpy.synplast.STP

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brainpy.synplast.STP#

class brainpy.synplast.STP(U=0.15, tau_f=1500.0, tau_d=200.0, method='exp_auto', name=None)[source]#

Synaptic output with short-term plasticity.

This model filters the synaptic currents according to two variables: \(u\) and \(x\).

\[I_{syn}^+(t) = I_{syn}^-(t) * x * u\]

where \(I_{syn}^-(t)\) and \(I_{syn}^+(t)\) are the synaptic currents before and after STP filtering, \(x\) denotes the fraction of resources that remain available after neurotransmitter depletion, and \(u\) represents the fraction of available resources ready for use (release probability).

The dynamics of \(u\) and \(x\) are governed by

\[\begin{split} \begin{aligned} \frac{du}{dt} & = & -\frac{u}{\tau_f}+U(1-u^-)\delta(t-t_{sp}), \\ \frac{dx}{dt} & = & \frac{1-x}{\tau_d}-u^+x^-\delta(t-t_{sp}), \\ \tag{1}\end{aligned}\end{split}\]

where \(t_{sp}\) denotes the spike time and \(U\) is the increment of \(u\) produced by a spike. \(u^-, x^-\) are the corresponding variables just before the arrival of the spike, and \(u^+\) refers to the moment just after the spike.

Parameters:

  • tau_f (float) – The time constant of short-term facilitation.

  • tau_d (float) – The time constant of short-term depression.

  • U (float) – The fraction of resources used per action potential.

  • method (str) – The numerical integral method.

See also

STD

__init__(U=0.15, tau_f=1500.0, tau_d=200.0, method='exp_auto', name=None)[source]#

Methods

__init__([U, tau_f, tau_d, method, name])

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.

clear_input(*args, **kwargs)

Clear the input at the current time step.

clone()

The function useful to clone a new object when it has been used.

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

desc(*args, **kwargs)

rtype:

ParamDescriber

filter(g)

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.

register_master(master)

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])

return_info()

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(pre_spike)

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

derivative

implicit_nodes

implicit_vars

isregistered

State of the component, representing whether it has been registered.

mode

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

name

Name of the model.

not_desc_params

supported_modes

Supported computing modes.

master

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