ExpIFRef

ExpIFRef#

class brainpy.state.ExpIFRef(in_size, R=Quantity(1., "ohm"), tau=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_T=Quantity(-59.9, "mV"), delta_T=Quantity(3.48, "mV"), V_initializer=Constant(value=-65. mV), spk_fun=ReluGrad(alpha=0.3, width=1.0), spk_reset='soft', ref_var=False, name=None)#

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

This neuron adds an absolute refractory period to ExpIF. While the exponential spike-initiation term keeps the membrane potential dynamics smooth, the refractory mechanism prevents the neuron from firing within tau_ref after a spike.

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

  • V_th (ArrayLike, default -30. * u.mV) – Numerical 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_T (ArrayLike, default -59.9 * u.mV) – Threshold potential of the exponential term.

  • delta_T (ArrayLike, default 3.48 * u.mV) – Spike slope factor controlling spike initiation sharpness.

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

  • spk_fun (Callable, default surrogate.ReluGrad()) – Surrogate gradient function for the spike generation.

  • 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

last_spike_time#

Last spike time recorder.

Type:

ShortTermState

refractory#

Neuron refractory state.

Type:

HiddenState

See also

ExpIF

ExpIF without refractory period.

AdExIFRef

Adaptive ExpIF with refractory period.

Notes

  • The refractory mechanism prevents the neuron from firing within tau_ref after a spike by holding the membrane potential at the reset value.

  • The simulation environment time variable t must be available via brainstate.environ.get('t') for refractory tracking.

References

Examples

>>> import brainpy
>>> import brainstate
>>> import saiunit as u
>>> # Create an ExpIFRef neuron layer with 10 neurons
>>> expif = brainpy.state.ExpIFRef(10, tau=10*u.ms, tau_ref=1.7*u.ms)
>>> expif.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.