Brad Wyble wrote: > > Heck, even the underlying PC hardware is more complex in a number of > > ways than the brain, it seems...
> > The brain is very RISCy... using a relatively simple processing >> pattern and then repeating it millions of times. > Alan, I strongly suggest you increase your familiarity with > neuroscience before making such claims in the future. I'm not sure > what simplified model of the neuron you are using, but be assured that > there are many layers of complexity of function within even a simple > neuron, let alone in networks. I havn't looked at the neuron in quite a while. =P But I don't consider myself [completely] insane in this context either. > Dendrites are not simple summators, they have a variety of nonlinear > processes including recursive, catalytic chemical reactions and complex > econd-messenger systems. That's just the tip of the iceberg once you > get into pharmacological subsystems, the complexity becomes a bit > staggering. Yeah, the dendrite _trees_ are quite complex. My interest, however, lies in the *forest*. ;) So the question is: what program is necessary to generate a system with the same computational charactoristics as the brain? (completely ignoring the implementation details, most of which are irrelevant or artifacts of the general implementation strategy). My current understanding draws heavily on the Cerebral Code by William H. Calvin (assuming I don't have to go all the way over to the shelf to check the name). Calvin proposes what ammounts to a sophisticated, optomized Celular Automata. I'll go ahead and sketch it out here: Start with Conway's game of life... Notice that it is rather slow because of its topology, if it were more strongly connected signals could travel faster and more efficiently... To solve this we add a second layer of topology in the form of shortcuts between the varrious regions and hence we have the subcortical pathways... Now that our system is roughly brain-shaped we consider the cells individually. Conway proposed a computationally universal model which possessed only one bit of state. This system would require large numbers of cells to express concepts such as degree of magnitude and other similarly important facets. It also has no inherant distinction between situational awareness and long-term skill and memory systems making it vulnerable to computer viruses and generally too dynamic to support stable long-term behavior patterns. We solve the first problem by increasing the ammount of state the thing can carry... From a single bit we now have a vector of some unknown length (probably 10-12 8-bit words or less) that expresses the current pattern under study. This actually reduces the total complexity of the system drasticly. This system is still too dynamic, we want to ground it in a more stable system. We create two classes of state, a persistant structural state and a dynamic state that expresses the present activation of the persistant state. In almost all higher animals, a sleep period is required to clear the chaotic dynamic state of the matrix and re-initialize it from the persistant state. The reset process occours during delta wave sleep and the re-init process occours during beta wave sleep. Also during this time, the almost totally unbiased computational matrix which is the cortex is programmed through a program running on a small subset of the cortex loaded from what is essentially a ROM being the Amigdalya and hypothalamus as well as certain structures in the reticular formation. The neocortex, as far as I know, is fairly uniform in general algorithm. We only need to "wire" it up slightly differently for each region. I don't know wheather this applies to the older cortical regions such as the hypocampus as well. I do know that the latter structures use a different and moderately less complex algorithm... > If it were fanastically simple, more so than a Linux box, do you think > that thousands of scientists working over more than one hundred years > would still understand it so poorly, yet it takes a group of 5 people 2 > years to crank out a new Linux OS? That's not a proof at all. The evident fact that nobody has yet tried the right approach has no relationship to the nature of that correct approach. > > In the cortex, I would propose the number is 28 for the left >> hemisphere, and maybe another 10 or so in the right hemisphere which >> don't directly overlap with the ones on the left. > You realize that the blobs drawn on images of the brain in college > level textbooks are simply areas of cell responsivity, and not diagrams > of the systems themselves? [/me feels a sudden intense wave of frustration.] MY LACK OF KNOWLEGE OF ANY SUCH SYSTEM IS A DIRECT RESULT OF THE DEFICIENCIES OF SAID COLEGE TEXTBOOKS. =(((((((( I'm 100% self taught at this point. =\ > The cortex is highly differentiated containing probably dozens if not > hundreds of systems, not to mention the enormous variety of specialized > systems at the subcortical level. But how many of these require special code? How many of those are simply programs that were acquired through learning? (and hence *should* not be coded by the AI designer.) > The complex soup of the reticular formation is sufficient to turn a > sane anatomist into a sobbing wreck with its dozens of specific nerve > clusters. The reticular formation is currently my favorite part of the brain. =P -- I WANT A DEC ALPHA!!! =) 21364: THE UNDISPUTED GOD OF ALL CPUS. http://users.rcn.com/alangrimes/ [if rcn.com doesn't work, try erols.com ] ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
