# brainpy.neurons.LIF_SFA_Bellec2020#

class brainpy.neurons.LIF_SFA_Bellec2020(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, V_initializer=OneInit(value=-70.0), a_initializer=OneInit(value=-50.0), spike_fun=<function relu_grad>, method='exp_auto', name=None, mode=None)[source]#

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

__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, V_initializer=OneInit(value=-70.0), a_initializer=OneInit(value=-50.0), spike_fun=<function relu_grad>, method='exp_auto', name=None, mode=None)[source]#

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(*args, **kwargs) 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.