Woah! Now whats the question?

After the summary description of the model, he says:

" In evaluating any model one must consider how well it accounts for existing research and whether or not it makes predictions -does it lead to novel lines of research. "

The concept of boundary completion by a dynamic directed diffusion algorithm is a completely novel way of doing it, significantly different from Grossberg's hard-wired receptive field approach. Thats a novel line of research.

And this model accounts for some curvy completion phenomena which are difficult to account for with Grossberg's model.

" Neural models can account for a number of findings, but it is quite hard to tell if these accounts require specific parameters. Is it possible to account for many phenomena with a single set of parameters? When faced with complex models, such as neural-based models and the feedback-based model presented here, it would be useful to understand what predictions are independent of the particulars of the parameters - are there differences between this model and Neural models that are parameter independent? "

What?

" Additionally, I am not sure that I can see novel predictions made by this model. "

Did you follow the curve completion thing? And the problem of spatial averaging if you try to do completion with large receptive fields?

" Note, parsimony is not a valid metric for comparing the two types of models since the nervous system need not follow any rule of parsimony. "

I think I would differ with that statement. Although the brain may not be the most parsimonious mechanism, biological systems tend to accumulate often redundant complexity, but a MODEL of that system, by Occam's razor, should attempt to be as parsimonious as possible. Especially a perceptual model. But I think I'm missing the larger point here.