Universal neural field computation

Turing machines and Gödel numbers are important pillars of the theory of computation. Thus, any computational architecture needs to show how it could relate to Turing machines and how stable implementations of Turing computation are possible. In this chapter, we implement universal Turing computation in a neural field environment. To this end, we employ the canonical symbologram representation of a Turing machine obtained from a Gödel encoding of its symbolic repertoire and generalized shifts. The resulting nonlinear dynamical automaton (NDA) is a piecewise affine-linear map acting on the unit square that is partitioned into rectangular domains. Instead of looking at point dynamics in phase space, we then consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. We call the resulting representation dynamic field automaton. Peter beim Graben Department of German Studies and Linguistics, Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, Germany · Roland Potthast Department of Mathematics and Statistics, University of Reading, UK and Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany 1 ar X iv :1 31 2. 35 50 v1 [ cs .F L ] 1 2 D ec 2 01 3 2 Peter beim Graben and Roland Potthast