Biomedical subjects
Publications and source records attributed to K G Kirby.
Papers arising out of the 1996 Conference on the Foundations of Information Science are introduced. A key motif is the continual violation and restoration of consistency, percolating and precipitating along vertical chains of informational objects.
Several conundrums are provoked by attempts to provide algorithmic descriptions of natural phenomena. A characteristic feature of natural computation is a breakdown in the formal simulation relation. This is called hermeneutic torsion, and is formally the failure to commute of a diagram describing homomorphisms between dynamical systems. This torsion is a source of computational power. For example, it is deeply involved with phenomena such as exaptation, wherein an existing structure is recruited for a novel function. Exaptation occurs continually at the macromolecular level and is fundamentally nonalgorithmic; our system-theoretic models of computation deal with structural descriptions for which a functional semantics must be assigned in advance, and a natural system continually 'diagonalizes out' of this semantics. This perspective clarifies the nature of computing power and encourages consideration of a new kind of transcomputational complexity.
Three components of a brain model operating on neuromolecular computing principles are described. The first component comprises neurons whose input-output behavior is controlled by significant internal dynamics. Models of discrete enzymatic neurons, reaction-diffusion neurons operating on the basis of the cyclic nucleotide cascade, and neurons controlled by cytoskeletal dynamics are described. The second component of the model is an evolutionary learning algorithm which is used to mold the behavior of enzyme-driven neurons or small networks of these neurons for specific function, usually pattern recognition or target seeking tasks. The evolutionary learning algorithm may be interpreted either as representing the mechanism of variation and natural selection acting on a phylogenetic time scale, or as a conceivable ontogenetic adaptation mechanism. The third component of the model is a memory manipulation scheme, called the reference neuron scheme. In principle it is capable of orchestrating a repertoire of enzyme-driven neurons for coherent function. The existing implementations, however, utilize simple neurons without internal dynamics. Spatial navigation and simple game playing (using tic-tac-toe) provide the task environments that have been used to study the properties of the reference neuron model. A memory-based evolutionary learning algorithm has been developed that can assign credit to the individual neurons in a network. It has been run on standard benchmark tasks, and appears to be quite effective both for conventional neural nets and for networks of discrete enzymatic neurons. The models have the character of artificial worlds in that they map the hierarchy of processes in the brain (at the molecular, neuronal, and network levels), provide a task environment, and use this relatively self-contained setup to develop and evaluate learning and adaptation algorithms.
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