030820 The problem of brain design
David Perkins, Professor of Education at the Harvard Graduate School of Education, observes (reported by Beth Potier in Harvard Gazette) It’s far easier for a group of people to pool physical effort than it is for them to effectively combine their mental energy. He illustrates this point with what he calls the “lawn mower paradox”: 10 people with lawn mowers can handily mow a lawn much faster than one, yet it’s far more difficult for the same 10 people to design a lawn mower.
”Many physical tasks divide up into chunks very nicely,” he says, but not so with intellectual duties. “It’s pretty hard to say, ‘Let’s make a decision together: you take part A of the decision, I take part B of the decision.'”
So what the brain has is a large number of interconnected pattern recognition systems. The individual systems fall into a smaller number of categories, e.g., cortical systems, cerebellar systems, etc. System categories differ among themselves at the very least in terms of plasticity and responsiveness to various neurotransmitters and neural activity modulators.
These systems each work along the lines proposed by Natschlaeger, Markram and Maass. This is not to say that I totally buy into their liquid state machine model, but that I do believe that the systems act as an analog fading memory (with digital overtones) and that their structure serves to project their inputs non linearly into a high dimensional space. Different systems have different memory decay time constants, ranging from short (early visual processing, for example), to medium (audio processing for speech recognition), to long (maintaining context while reading).
I hypothesize that (at least some of) these systems become tuned (and in that sense optimized) over time to their inputs (thus improving the separation of components projected into high dimensional space) by a process approximating Hebbian learning. This could account for the acquisition of the ability to distinguish among phonemes when learning a language. In effect, Hebbian learning creates “grooves” into which particular stimuli are likely to fall, thus enhancing the separation of minimally differing phonemes.