Universal neural field computation

Turing machines and G\"odel 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 G\"odel 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 \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with arXiv:1204.546

Holistic competence level of Corporate governance bodies in the selection process in the Czech Republic

The main objective of the paper has been based on two-phased research to compare the level of holistic competence in selecting the members of governance bodies (Managing Board and Supervisory Board) between the two periods. Holistic competence was defined based on a holistic model of competence by Porvaznik (2008) and implementation of the model was subsequently described in the Corporate Governance based on a study by Taraba, Bartosikova and Bilikova (2014). Using methods of descriptive statistics and spider charts two data files were compared. Data were collected by questionnaire survey conducted among members of the Corporate Governance bodies operating in the Czech Republic in the period from April 2012 to April 2013 (Phase I) and in the period from September 2013 to February 2014 (Phase II.). There is also discussion as a part of paper conclusion which formulates the underlying causes of changes in the holistic assessment of competence in the selection of the members of corporate governance bodies and the recommendations made in this area

OrBEAGLE: Integrating Orthography into a Holographic Model of the Lexicon

Abstract. Many measures of human verbal behavior deal primarily with semantics (e.g., associative priming, semantic priming). Other measures are tied more closely to orthography (e.g., lexical decision time, visual word-form priming). Semantics and orthography are thus often studied and modeled separately. However, given that concepts must be built upon a foundation of percepts, it seems desirable that models of the human lexicon should mirror this structure. Using a holographic, distributed representation of visual word-forms in BEAGLE [12], a corpustrained model of semantics and word order, we show that free association data is better explained with the addition of orthographic information. However, we find that orthography plays a minor role in accounting for cue-target strengths in free association data. Thus, it seems that free association is primarily conceptual, relying more on semantic context and word order than word form information

Integration of Graphical-Based Rules with Adaptive Learning of Structured Information

We briefly review the basic concepts underpinning the adaptive processing of data structures as outlined in [3]. Then, turning to practical applications of this framework, we argue that stationarity of the computational model is not always desirable. For this reason we introduce very briefly our idea on how a priori knowledge on the domain can be expressed in a graphical form, allowing the formal specification of perhaps very complex (i.e., non-stationary) requirements for the structured domain to be treated by a neural network or Bayesian approach. The advantage of the proposed approach is the systematicity in the specification of both the topology and learning propagation of the adopted computational model (i.e., either neural or probabilistic, or even hybrid by combining both of them)