brainpy.neurons.LeakyIntegrator

class brainpy.neurons.LeakyIntegrator(size, keep_size=False, V_rest=0.0, R=1.0, tau=10.0, V_initializer=ZeroInit, noise=None, input_var=True, name=None, mode=None, method='exp_auto')

Leaky Integrator Model.

Model Descriptions

This class implements a leaky integrator model, in which its dynamics is given by:

\[\tau \frac{dV}{dt} = - (V(t) - V_{rest}) + RI(t)\]

where \(V\) is the membrane potential, \(V_{rest}\) is the resting membrane potential, \(\tau\) is the time constant, and \(R\) is the resistance.

Parameters:

• size (sequence of int, int) – The size of the neuron group.

• V_rest (float, ArrayType, Initializer, callable) – Resting membrane potential.

• R (float, ArrayType, Initializer, callable) – Membrane resistance.

• tau (float, ArrayType, Initializer, callable) – Membrane time constant.

• V_initializer (ArrayType, Initializer, callable) – The initializer of membrane potential.

• noise (ArrayType, Initializer, callable) – The noise added onto the membrane potential

• method (str) – The numerical integration method.

• name (str) – The group name.

__init__(size, keep_size=False, V_rest=0.0, R=1.0, tau=10.0, V_initializer=ZeroInit, noise=None, input_var=True, name=None, mode=None, method='exp_auto')

Methods

__init__(size[, keep_size, V_rest, R, tau, ...])
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()	Empty function of clearing inputs.
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
derivative(V, t, I_ext)
get_aft_update(key)	Get the after update of this node by the given key.
get_batch_shape([batch_size])
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.
init_param(param[, shape, sharding])	Initialize parameters.
init_variable(var_data, batch_or_mode[, ...])	Initialize variables.
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])
return_info()	rtype: Union[Variable, ReturnInfo]
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([x])	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.
varshape	The shape of variables in the neuron group.