Since there have been some replies to the query about artificial perception, I suppose it's OK to mention my own work in progress. I have been doing research in the formal semantics of neural networks. I am working on a mathematical model in which concepts (formulas) are formed in memory as colimits. The diagrams involve neural structures representing other concepts, going all the way back to simple percepts. A concept is stored in memory as a neuron or neuron pool together with its attendant synaptic connections. Logically closed portions of memory are theories. Functors and natural transformations enter in in the usual fashion of categorical model theory. There is still a lot of work to do on this, and I am still learning the mathematics. I do have a proposed neural implementation of it, and am working on a paper. Previous work along these lines has involved geometric logic, so that I could understand some of the basics of learning, which for me involves working with an observational logic. I have a paper on this, too. Finding reviewers for this kind of material in the neural network community has been difficult. If any of this sounds interesting enough to discuss, I certainly wouldn't mind getting some feedback from category theorists. Sincerely, Mike Healy -- =========================================================================== e Michael J. Healy A FA ----------> GA (425)865-3123 | | FAX(425)865-2964 | | Ff | | Gf c/o The Boeing Company | | PO Box 3707 MS 7L-66 \|/ \|/ Seattle, WA 98124-2207 ' ' USA FB ----------> GB e "I'm a natural man." michael.j.healy@boeing.com B -or- mjhealy@u.washington.edu ============================================================================