Thank you for your reply, Seanny.
Yesterday I saw an article that shows how to train a network which can be used by SNN(https://nengo.github.io/nengo_dl/examples/spiking_mnist.html).
Is this the way you trained the network?
I know little about this. When I saw the source code about Spaun, it loads many file, which terminate in '.npz', I think this may be the structure of the network.
self.vision_network_filename = os.path.join(self.filepath, 'params.npz')
self.vision_network_data = np.load(self.vision_network_filename)
self.weights = self.vision_network_data['weights']
self.biases = self.vision_network_data['biases']
weights_class = self.vision_network_data['Wc']
biases_class = self.vision_network_data['bc']
I think the 'weights' and 'bias' in 'params.npz' is the parameters of the network, the 'Wc' is used to calculate the semantic pointer(I think), dose it has a relationship with the 'weights' and 'bias'?
Besides, I don't know the meaning of 'class_means.npz', could you please tell me how to get the params, thank you!