brainpy.neurons.ALIFBellec2020

class brainpy.neurons.ALIFBellec2020(size, keep_size=False, V_rest=-70.0, V_th=-60.0, R=1.0, beta=1.6, tau=20.0, tau_a=2000.0, tau_ref=None, noise=None, V_initializer=OneInit(value=-70.0), a_initializer=OneInit(value=-50.0), spike_fun=<function relu_grad>, input_var=True, method='exp_auto', name=None, mode=None, eprop=False)

Leaky Integrate-and-Fire model with SFA [1].

This model is similar to the GLIF2 model in the Technical White Paper on generalized LIF (GLIF) models from AllenInstitute [2].

Formally, this model is given by:

\[\begin{split}\tau \dot{V} = -(V - V_{\mathrm{rest}}) + R*I \\ \tau_a \dot{a} = -a\end{split}\]

Once a spike is induced by \(V(t) > V_{\mathrm{th}} + \beta a\), then

\[\begin{split}V \gets V - V_{\mathrm{th}} \\ a \gets a + 1\end{split}\]

References

[1]

Bellec, Guillaume, et al. “A solution to the learning dilemma for recurrent networks of spiking neurons.” Nature communications 11.1 (2020): 1-15.

[2]

Allen Institute: Cell Types Database. © 2018 Allen Institute for Brain Science. Allen Cell Types Database, cell feature search. Available from: celltypes.brain-map.org/data (2018).

__init__(size, keep_size=False, V_rest=-70.0, V_th=-60.0, R=1.0, beta=1.6, tau=20.0, tau_a=2000.0, tau_ref=None, noise=None, V_initializer=OneInit(value=-70.0), a_initializer=OneInit(value=-50.0), spike_fun=<function relu_grad>, input_var=True, method='exp_auto', name=None, mode=None, eprop=False)

Methods

__init__(size[, keep_size, V_rest, V_th, R, ...])
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.
dV(V, t, I_ext)
da(a, t)
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
derivative
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.