engine.neuron.spiking.LIF
Leaky Integrate-and-Fire neuron model (LIF).
- class engine.neuron.spiking.LIF.LIFNeuron(volt_thresh=-55.0, volt_rest=-75.0, leak_gl=5.0, tau_mem=5.0, tau_ref=1.0, **kwargs)
Base class for the leaky integrate-and-fire neuron model (LIF).
Extends
SpikingNeuronby adding necessary parameters and implementing the LIFforward()method efficiently for 1D tensors of arbitrary size. User may implement subclasses to differentiate variants along additional dimensions.LIF units own the following attributes in addition to those inherited from
SpikingNeuron:- Parameters:
volt_thresh (float or Tensor) – Spike threshold (defaults to -55.0).
volt_rest (float or Tensor) – Resting potential (defaults to -75.0).
leak_gl (float or Tensor) – Leak conductance (defaults to 5.0).
tau_mem (float or Tensor) – Membrane time constant (e.g., 5.0).
tau_ref (float or Tensor) – Refractory period (e.g., 1.0).
cycle_length (int, optional) – Oscillatory cycle period, required to time optional resetting of the refractory period.
release_phase (int, optional) – Oscillation phase at which to release all neurons from refractory mode, if required.
References
- LIF Tutorial
forward()implements the algorithm described in this tutorial.
- _config_props_: tuple[str] = ('volt_thresh', 'volt_rest', 'leak_gl', 'tau_mem', 'tau_ref')
A list of configurable property names of the class that is set/read by the configuration API.
Each sub/super-class can define this and the properties are accumulated across all the sub/super-classes.