GifRef

GifRef#

class brainpy.state.GifRef(in_size, R=Quantity(20., "ohm"), tau=Quantity(20., "ms"), tau_ref=Quantity(1.7, "ms"), V_rest=Quantity(-70., "mV"), V_reset=Quantity(-70., "mV"), V_th_inf=Quantity(-50., "mV"), V_th_reset=Quantity(-60., "mV"), V_th_initializer=Constant(value=-50. mV), a=Quantity(0., "kHz"), b=Quantity(0.01, "kHz"), k1=Quantity(0.2, "kHz"), k2=Quantity(0.02, "kHz"), R1=0.0, R2=1.0, A1=Quantity(0., "mA"), A2=Quantity(0., "mA"), V_initializer=Constant(value=-70. mV), I1_initializer=Constant(value=0. mA), I2_initializer=Constant(value=0. mA), spk_fun=ReluGrad(alpha=0.3, width=1.0), spk_reset='soft', ref_var=False, name=None)#

Generalized Integrate-and-Fire neuron model with refractory mechanism.

This model extends Gif by adding an absolute refractory period during which the neuron cannot fire. This creates more realistic firing patterns and prevents unrealistic high-frequency firing.

Parameters:

  • in_size (Size) – Size of the input to the neuron.

  • R (ArrayLike, default 20. * u.ohm) – Membrane resistance.

  • tau (ArrayLike, default 20. * u.ms) – Membrane time constant.

  • tau_ref (ArrayLike, default 1.7 * u.ms) – Absolute refractory period duration.

  • V_rest (ArrayLike, default -70. * u.mV) – Resting potential.

  • V_reset (ArrayLike, default -70. * u.mV) – Reset potential after spike.

  • V_th_inf (ArrayLike, default -50. * u.mV) – Target value of threshold potential updating.

  • V_th_reset (ArrayLike, default -60. * u.mV) – Free parameter, should be larger than V_reset.

  • V_th_initializer (Callable) – Initializer for the threshold potential.

  • a (ArrayLike, default 0. / u.ms) – Coefficient describes dependence of V_th on membrane potential.

  • b (ArrayLike, default 0.01 / u.ms) – Coefficient describes V_th update.

  • k1 (ArrayLike, default 0.2 / u.ms) – Constant of I1.

  • k2 (ArrayLike, default 0.02 / u.ms) – Constant of I2.

  • R1 (ArrayLike, default 0.) – Free parameter.

  • R2 (ArrayLike, default 1.) – Free parameter.

  • A1 (ArrayLike, default 0. * u.mA) – Free parameter.

  • A2 (ArrayLike, default 0. * u.mA) – Free parameter.

  • V_initializer (Callable) – Initializer for the membrane potential state.

  • I1_initializer (Callable) – Initializer for internal current I1.

  • I2_initializer (Callable) – Initializer for internal current I2.

  • spk_fun (Callable, default surrogate.ReluGrad()) – Surrogate gradient function.

  • spk_reset (str, default 'soft') – Reset mechanism after spike generation.

  • ref_var (bool, default False) – Whether to expose a boolean refractory state variable.

  • name (str, optional) – Name of the neuron layer.

V#

Membrane potential.

Type:

HiddenState

I1#

Internal current 1.

Type:

HiddenState

I2#

Internal current 2.

Type:

HiddenState

V_th#

Spiking threshold potential.

Type:

HiddenState

last_spike_time#

Last spike time recorder.

Type:

ShortTermState

refractory#

Neuron refractory state (if ref_var=True).

Type:

HiddenState

See also

Gif

Gif without refractory period.

AdExIFRef

Adaptive exponential IF with refractory period.

Notes

  • Combines Gif model’s rich dynamics with absolute refractory period.

  • During refractory period, all state variables are held at reset values.

  • Set ref_var=True to track refractory state as a boolean variable.

  • More biologically realistic than Gif without refractory mechanism.

  • Can still exhibit diverse firing patterns: regular, bursting, adaptation.

  • Refractory period prevents unrealistically high firing rates.

References

Examples

>>> import brainpy
>>> import brainstate
>>> import saiunit as u
>>> # Create a GifRef neuron layer with refractory period
>>> gif_ref = brainpy.state.GifRef(10, tau=20*u.ms, tau_ref=2.0*u.ms,
...     k1=0.2/u.ms, k2=0.02/u.ms, ref_var=True)
>>> # Initialize the state
>>> gif_ref.init_state(batch_size=1)
get_spike(V=None, V_th=None)[source]#

Generate spikes based on neuron state variables.

This abstract method must be implemented by subclasses to define the spike generation mechanism. The method should use the surrogate gradient function self.spk_fun to enable gradient-based learning.

Parameters:

  • *args – Positional arguments (typically state variables like membrane potential)

  • **kwargs – Keyword arguments

Returns:

Binary spike tensor where 1 indicates a spike and 0 indicates no spike.

Return type:

ArrayLike

Raises:

NotImplementedError – If the subclass does not implement this method.

init_state(batch_size=None, **kwargs)[source]#

State initialization function.

reset_state(batch_size=None, **kwargs)[source]#

State resetting function.