jaxsnn.event.modules
Modules
Classes
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Functions
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jaxsnn.event.modules.LIF(size: int, n_steps: int, params: jaxsnn.event.modules.lif.parameters.LIFParameters) → jaxsnn.event.types.Population Creates a LIF layer for event-based simulation and backpropagation. Returns a generator function that builds the layer based on input connections, delays, and chosen backpropagation strategy. Supports both forward-only simulation and custom backward passes (eventprop).
- Parameters
size – Number of neurons in the layer.
n_steps – Number of event steps in the simulation.
params – Parameters for the LIF neuron dynamics.
- Returns
A Population object containing the generator and parameters.
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jaxsnn.event.modules.Linear(mean: float = 0.5, std: float = 2.0, min_delay: float = 0.0, pre_weights: Optional[jax.Array, None] = None) → jaxsnn.event.types.Projection Creates a Linear projection layer
- Either:
initialize weights from a Gaussian (mean, std), or
provide a concrete weight array.
- Parameters
mean – Mean of the Gaussian distribution for weight initialization.
std – Standard deviation of the Gaussian distribution.
min_delay – Minimum delay associated with this projection.
pre_weights – Optional weight array. If provided, mean and std are ignored.
- Returns
A Projection object containing the generator and parameters.
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jaxsnn.event.modules.Source(size: int) → jaxsnn.event.types.SourcePopulation Creates a Source population layer representing external input.
- Parameters
size – Number of neurons/channels in the source layer.
- Returns
A SourcePopulation object containing the generator and parameters.