AdQuaIFRef

AdQuaIFRef#

class brainpy.state.AdQuaIFRef(in_size, R=Quantity(1., "ohm"), tau=Quantity(10., "ms"), tau_w=Quantity(10., "ms"), tau_ref=Quantity(1.7, "ms"), V_th=Quantity(-30., "mV"), V_reset=Quantity(-68., "mV"), V_rest=Quantity(-65., "mV"), V_c=Quantity(-50., "mV"), c=Quantity(0.07, "1 / mV"), a=Quantity(1., "S"), b=Quantity(0.1, "mA"), V_initializer=Constant(value=-65. mV), w_initializer=Constant(value=0. mA), spk_fun=ReluGrad(alpha=0.3, width=1.0), spk_reset='soft', ref_var=False, name=None)#

Adaptive Quadratic Integrate-and-Fire neuron model with refractory mechanism.

This model extends AdQuaIF by adding an absolute refractory period during which the neuron cannot fire regardless of input. The combination of adaptation and refractory period creates realistic firing patterns.

Parameters:

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

  • R (ArrayLike, default 1. * u.ohm) – Membrane resistance.

  • tau (ArrayLike, default 10. * u.ms) – Membrane time constant.

  • tau_w (ArrayLike, default 10. * u.ms) – Adaptation current time constant.

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

  • V_th (ArrayLike, default -30. * u.mV) – Firing threshold voltage.

  • V_reset (ArrayLike, default -68. * u.mV) – Reset voltage after spike.

  • V_rest (ArrayLike, default -65. * u.mV) – Resting membrane potential.

  • V_c (ArrayLike, default -50. * u.mV) – Critical voltage for spike initiation.

  • c (ArrayLike, default 0.07 / u.mV) – Coefficient describing membrane potential update.

  • a (ArrayLike, default 1. * u.siemens) – Coupling strength from voltage to adaptation current.

  • b (ArrayLike, default 0.1 * u.mA) – Increment of adaptation current after a spike.

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

  • w_initializer (Callable) – Initializer for the adaptation current.

  • 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

w#

Adaptation current.

Type:

HiddenState

last_spike_time#

Last spike time recorder.

Type:

ShortTermState

refractory#

Neuron refractory state (if ref_var=True).

Type:

HiddenState

See also

AdQuaIF

Adaptive quadratic IF without refractory period.

QuaIF

Quadratic IF without adaptation or refractory.

Notes

  • Combines spike-frequency adaptation with absolute refractory period.

  • During refractory period, neuron state is held at reset values.

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

  • Refractory period prevents unrealistically high firing rates.

  • More biologically realistic than AdQuaIF without refractory period.

References

Examples

>>> import brainpy
>>> import brainstate
>>> import saiunit as u
>>> # Create an AdQuaIFRef neuron layer with refractory period
>>> adquaif_ref = brainpy.state.AdQuaIFRef(10, tau=10*u.ms,
...     tau_w=100*u.ms, tau_ref=2.0*u.ms, ref_var=True)
>>> # Initialize the state
>>> adquaif_ref.init_state(batch_size=1)
get_spike(V=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.