brainpy.neurons.AdQuaIF

brainpy.neurons.AdQuaIF#

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

Adaptive quadratic integrate-and-fire neuron model.

Model Descriptions

The adaptive quadratic integrate-and-fire neuron model [1] is given by:

\[\begin{split}\begin{aligned} \tau_m \frac{d V}{d t}&=c(V-V_{rest})(V-V_c) - w + I(t), \\ \tau_w \frac{d w}{d t}&=a(V-V_{rest}) - w, \end{aligned}\end{split}\]

once the membrane potential reaches the spike threshold,

\[\begin{split}V \rightarrow V_{reset}, \\ w \rightarrow w+b.\end{split}\]

Model Examples

>>> import brainpy as bp
>>> group = bp.neurons.AdQuaIF(1, )
>>> runner = bp.DSRunner(group, monitors=['V', 'w'], inputs=('input', 30.))
>>> runner.run(300)
>>> fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
>>> fig.add_subplot(gs[0, 0])
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V')
>>> fig.add_subplot(gs[1, 0])
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, ylabel='w', show=True)

Model Parameters

Parameter

Init Value

Unit

Explanation

V_rest

-65

mV

Resting potential.

V_reset

-68

mV

Reset potential after spike.

V_th

-30

mV

Threshold potential of spike and reset.

V_c

-50

mV

Critical voltage for spike initiation. Must be larger than \(V_{rest}\).

a

1

The sensitivity of the recovery variable \(u\) to the sub-threshold fluctuations of the membrane potential \(v\)

b

.1

The increment of \(w\) produced by a spike.

c

.07

Coefficient describes membrane potential update. Larger than 0.

tau

10

ms

Membrane time constant.

tau_w

10

ms

Time constant of the adaptation current.

Model Variables

Variables name

Initial Value

Explanation

V

0

Membrane potential.

w

0

Adaptation current.

input

0

External and synaptic input current.

spike

False

Flag to mark whether the neuron is spiking.

t_last_spike

-1e7

Last spike time stamp.

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.

dV(V, t, w, I)

dw(w, t, V)

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)

rtype:

None

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 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.

spk_dtype

supported_modes

Supported computing modes.

varshape

The shape of variables in the neuron group.