jaxsnn.event.modules.synapse

Functions

jaxsnn.event.modules.synapse.Synapse(mean: float = 0.5, std: float = 2.0, min_delay: float = 0.0, weight_scale: float = 1.0, transform: Callable = <function linear_saturating>) → Tuple[Callable, Dict]

Creates a synapse initialization function and associated parameters.

Parameters

• mean – Mean value for initializing synaptic weights.

• std – Standard deviation for initializing synaptic weights.

• min_delay – Minimum allowable synaptic delay.

• weight_scale – Scaling factor for synaptic weights.

• transform – Transformation function applied to synaptic weights.

Returns

A tuple containing: - gen: A generator function that provides an init function and a module

generator.

• parameters: A dictionary containing the synapse configuration.

jaxsnn.event.modules.synapse.linear_saturating(weight: jax.Array, scale: float, min_weight: float = - 63.0, max_weight: float = 63.0, as_int: bool = True) → jax.Array

Scale all weights according to:

w <- clip(scale * w, min_weight, max_weight)

Parameters

• weight – The weight array to be transformed.

• scale – A constant the weight array is scaled with.

• min_weight – The minimum value, smaller values are clipped to after scaling.

• max_weight – The maximum value, bigger values are clipped to after scaling.

• as_int – Round to nearest int and return as int type.

Returns

The transformed weight tensor.