Rodney M. J. Cotterill

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It is suggested that the anatomical structures which mediate consciousness evolved as decisive embellishments to a (non-conscious) design strategy present even in the simplest unicellular organisms. Consciousness is thus not the pinnacle of a hierarchy whose base is the primitive reflex, because reflexes require a nervous system, which the single-celled(More)
Neural networks provide a basis for semiempirical studies of pattern matching between the primary and secondary structures of proteins. Networks of the perceptron class have been trained to classify the amino-acid residues into two categories for each of three types of secondary feature: alpha-helix or not, beta-sheet or not, and random coil or not. The(More)
Three-dimensional structures of protein backbones have been predicted using neural networks. A feed forward neural network was trained on a class of functionally, but not structurally, homologous proteins, using backpropagation learning. The network generated tertiary structure information in the form of binary distance constraints for the C(alpha) atoms in(More)
Some birds display behavior reminiscent of the sophisticated cognition and higher levels of consciousness usually associated with mammals, including the ability to fashion tools and to learn vocal sequences. It is thus important to ask what neuroanatomical attributes these taxonomic classes have in common and whether there are nevertheless significant(More)
Computer simulation of the dynamics of neuronal assemblies within minicolumns, and of the interactions between minicolumns in different cortical areas, has produced a quantitative explanation of the 35-60 Hz oscillations recently observed in adult cat striate cortices. The observed behavior suggests an association mechanism that exploits the NMDA receptor's(More)
The subject of consciousness is proving to be almost like a black hole to those who draw close to it. Once seduced inside the event horizon, they appear lost to normal scientific activity but follow a trajectory towards an explanation of the phenomenon which others, standing well away from the fateful edge, shout out to them is impossible to follow(More)
Three-dimensional (3D) structures of protein backbones have been predicted using neural networks. A feed forward neural network was trained on a class of functionally, but not structurally, homologous proteins, using backpropagation learning. The network generated tertiary structure information in the form of binary distance constraints for the Co atoms in(More)
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