Lecture 16 - Debate - Edward Rolls VS Kim Plunkett

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Biologically Plausible Neworks - Edmond Rolls

• Local Learning Rule
• Rarely a seperate "teacher" for each neuron
• Pattern association is likely
• No multilayer backpropagation net
• No information processing with limited numbers of hidden neurons to improve generalization
• Uses large number of neurons and expansion recoding to simplify subsequent stages.
• Though there are back-projections in the cerebellum, they don't seem to propagate error
• The brain is a terrible discrete-step processor (seems to be continous dynamic of the "integrate and fire" neurons).
• Neuronal precision is limited only 4-5 distinct firing rates.
• The brain does not solve logic equally (fixing XOR may not be neccessary)
• Connectionist models are helpful in providing insight, but an aim should be toward ever more biological plausibility.

Connectionist Nets - Kim Plunkett

• Need to let many flowers blossom, even symbolic accounts. So the question is: Is the intermediate level provided by backprop really helpful to that endeavor?
• Well, whatever you do, you simplify. Simplification is a necessary part of any model building. So don't think that there is any good reason to throw out backprop just because the evidence isn't in. Particularly, because we seem to get the same results anyway. Backprop is part of a class of algorithms, of which Hebbian is a part. Thus, it shares many similar features. Why not use it?
• To look at the kind of problems connectionists like to study (like language, cognition, etc), we really have no idea about the real networks involved. Indeed, the level of specificity we use is actually an abstract representation of the space, using known computational skills of the brain. Thus, as we can bring to bear actual networks involved in language, by all means use them.
• But, until then, such abstract accounts may actually inform upon empirical research.
• Finally, the issue of teacher signals is often misconstrued. Connectionist networks are usually drivin by positive evidence with an internally generated error signal.