# 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.

__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]) clear_input() 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. filter(g) get_delay_data(identifier, delay_step, *indices) Get delay data according to the provided delay steps. load_state_dict(state_dict[, warn, compatible]) 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. register_delay(identifier, delay_step, ...) Register delay variable. register_implicit_nodes(*nodes[, node_cls]) register_implicit_vars(*variables[, var_cls]) register_master(master) reset(*args, **kwargs) Reset function which reset the whole variables in the model. reset_local_delays([nodes]) Reset local delay variables. reset_state([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) Unflatten the data to construct an object of this class. unique_name([name, type_]) Get the unique name for this object. update(tdi, 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

 derivative global_delay_data Global delay data, which stores the delay variables and corresponding delay targets. 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. pass_shared