I would like to know the memory storage capacity of the BCM learning rule when it is implemented on a Hopfield network. I understand that it will be a function of n where n is the number of neurons.
I am looking for references (pedagogic and beginner friendly!) to these two topics, hierarchical temporal memory algorithms applied to deep planning problems (multi-layer) neural networks trained ...
Suppose we have a classification problem and we wish to solve the problem by Neural Network. What factors must one consider choosing an NN structure? e.g Feed Forward, Recurrent and other available ...
Are there any advantages of having more than 2 hidden layers in a Neural Network? I've seen some places that recommend it, others prove that there is no advantage. Which one is right?