I created a nengo network and another non-spiking network with the same architecture. Is it normal to have approximately the same amount of trainable parameters but an enormous higher amount of non-trainable parameters for the spiking neural network?
My non-spiking network has 3,085,155 trainable and 486 non-trainable parameters. My spiking neural network has 3,354,981 trainable and 174,016,834 non-trainable parameters.
I am new to the world of spiking neural networks and would like to understand why the amount of non-trainable parameters differ so much.
Thank you so much in advance!
That’s how I calculate the number of parameter for the spiking neural network:
nengo_params = sum(
np.prod(s.shape) for s in sim.keras_model.weights)
nengo_trainable_params = sum(
np.prod(w.shape) for w in sim.keras_model.trainable_weights)