Most of our disagreement seems to be about definitions and choices
of words, rather than facts.
(1) My memo is not intend to cover every system labeled as "neural network"
--- that is why I use a whole section to define what I mean by "NN
model" discussed in the paper. I'm fully aware of the fact that given
...
My strategy is to first discuss the most typical models of the "neural
network" family (or the "standard NN architectures", as Ben put it),
My problem is this: At my research institute a large portion of the people work
on neural networks. Things like, recurrent LSTM networks for continuous
speech recognition and evolved echo state networks for real time adaptive
control problems. I also follow research on computational liquids, biologically
plausible neural networks, neural microcircuit research, and the ideas of
people like Jeff Hawkins. In my mind, this is all work on "neural networks", and
the researchers themselves call it that, and publish in big NN conferences like
ICANN, IJCNN and journals like "Neural Networks". However none of this work
is like the "NN model" you have defined. Thus to my mind, your "NN model"
does not represent modern neural network research.
(3) Neuroscience results cannot be directly used to support
"artificial neural networks"
I disagree as a number of the trends and new ideas in artificial neural networks
that I follow are coming from neuroscience research.
If I had to sum up our differences: I'd say that what you call "standard neural
networks" and your "NN model", and most of the problems you describe, would
have been reasonable in 2000... but not now, 5 to 6 years later.
Shane
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