Abstract
As a contribution to increasing the range of ideas on architecture and process for incorporation in ANNs a new theory is outlined of the emergence of parcellation of the cerebral cortex on an evolutionary time scale. Slow learning and accelerated evolution, involving a form of inheritance of acquired characteristics, are assigned fundamental roles in the creation of functionally tilted, local cortical area architectures. Within each generation a cycle of neuron → astrocyte → neuron interaction produces a web of associated astrocytes defining local neural areas consistently engaging in integrated subsymbolic processing. Effects of intra-generational experience enter the germ line via processes involving astrocytes, epithelial cells, lymphocytes and RNA retroviruses. Potential application of the theory is explored in evolutionary programming aimed at constructing a generalisable, recurrent network induction algorithm.
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Wallace, J.G., Bluff, K. (1999). Slow learning and fast evolution: An approach to cytoarchitectonic parcellation. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098158
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DOI: https://doi.org/10.1007/BFb0098158
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