brainpy.neurons.LIF

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brainpy.neurons.LIF#

class brainpy.neurons.LIF(*args, input_var=True, spike_fun=None, **kwargs)[source]#

Leaky integrate-and-fire neuron model.

Model Descriptions

The formal equations of a LIF model [1] is given by:

\[\begin{split}\tau \frac{dV}{dt} = - (V(t) - V_{rest}) + RI(t) \\ \text{after} \quad V(t) \gt V_{th}, V(t) = V_{reset} \quad \text{last} \quad \tau_{ref} \quad \text{ms}\end{split}\]

where \(V\) is the membrane potential, \(V_{rest}\) is the resting membrane potential, \(V_{reset}\) is the reset membrane potential, \(V_{th}\) is the spike threshold, \(\tau\) is the time constant, \(\tau_{ref}\) is the refractory time period, and \(I\) is the time-variant synaptic inputs.

Model Examples

Parameters:

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

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

  • V_reset (float, ArrayType, Initializer, callable) – Reset potential after spike.

  • V_th (float, ArrayType, Initializer, callable) – Threshold potential of spike.

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

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

  • tau_ref (float, ArrayType, Initializer, callable) – Refractory period length.(ms)

  • 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.

References

__init__(*args, input_var=True, spike_fun=None, **kwargs)[source]#

Methods

__init__(*args[, input_var, spike_fun])

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)

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.

inv_scaling(x[, scale])

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.

offset_scaling(x[, bias, scale])

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()

save_state(**kwargs)

Save states as a dictionary.

setattr(key, value)

state_dict(**kwargs)

Returns a dictionary containing a whole state of the module.

std_scaling(x[, scale])

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 a variable that 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.

spk_dtype

supported_modes

Supported computing modes.

varshape

The shape of variables in the neuron group.
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