This documentation is for a development version. Click here for the latest stable release (v0.1.0).
KerasSpiking provides tools for training and running spiking neural networks
directly within the Keras framework. The main feature is
keras_spiking.SpikingActivation, which can be used to transform
any activation function into a spiking equivalent. For example, we can translate a
non-spiking model, such as
inp = tf.keras.Input((5,)) dense = tf.keras.layers.Dense(10)(inp) act = tf.keras.layers.Activation("relu")(dense) model = tf.keras.Model(inp, act)
into the spiking equivalent:
# add time dimension to inputs inp = tf.keras.Input((None, 5)) dense = tf.keras.layers.Dense(10)(inp) # replace Activation with SpikingActivation act = keras_spiking.SpikingActivation("relu")(dense) model = tf.keras.Model(inp, act)
Models with SpikingActivation layers can be optimized and evaluated in the same way as any other Keras model. They will automatically take advantage of KerasSpiking’s “spiking aware training”: using the spiking activations on the forward pass and the non-spiking (differentiable) activation function on the backwards pass.
If you are interested in building and optimizing spiking neuron models, you may also be interested in NengoDL. See this page for a comparison of the different use cases supported by these two packages.
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