Capturing the dynamic of perception in fuzzy space time

Living systems are dynamic. They efficiently adapt to constantly changing conditions. The human nervous system perceives complex sensory input and produces an appropriate response. One goal of machine automation is to produce a similar adaptive response. Methods: We sought to represent the dynamic of the adaptive response in fuzzy space time. We hypothesize that perception and response are captured in fuzzy space time duration, that percepts and responses are physiologically encoded by fuzzy sets, and that the neuroanatomy of the adaptive response accommodates a fuzzy physiology. To test this hypothesis we use the geometry of fuzzy "sets as points" in the unit square. Complex graded sensory input is transformed into fuzzy representations as elements on sensory neurons. This frequency coded information is reduced to a far smaller number of fuzzy multiplex signal spikes in the cortical minicolumn. Through a process of breaking the symmetry of fuzzy set entropy, we allow fuzzy set elements to blend to the degree of their dissimilarity so that a disambiguated fuzzy signal emerges within a doubling area of pyramidal cell dendritic architecture. Results: While maintaining the structure of the unit hypercube fuzzy set geometry, repeated doubly extended magnitudes of cortical minicolumn pyramidal cell dentritic area accommodates a decreasing amount of ambiguity of each fuzzy frequency signal while maintaining all degrees of its historical ambiguity. The maximum possible number of disambiguations in extended magnitudes is 10, the limit of the fuzzy symmetry breaking in continuous cellular automata. Conclusion: Doubling dendritic area by a maximum factor of 10 is a fundamental principle that governs the distribution and connections of neurons within the central nervous system.