Article Preview
TopIntroduction
By using computational information conservation theory (CICT) framework we already discussed the major intrinsic limitations of “Science 1.0” brain arbitrary multiscale (AMS) modeling and strategies to get better simulation results by “Science 2.0” approach, in a different paper published elsewhere (Fiorini, 2015a). Here we like to stress how CICT can help in quantum cognitive computation modeling. In fact, CICT new awareness of the hyperbolic framework of coded heterogeneous hyperbolic structures in reciprocal space (RS), underlying the familiar Euclidean (direct space, DS) surface representation system, emerged from the study of the geometrical structure of the discrete manifold of ordered hyperbolic substructures, coded by formal power series, under the criterion of evolutive structural invariance at arbitrary precision (Fiorini, 2016a). CICT sees rational geometric series as simple recursion sequences in a wider recursive operative framework where all algebraic recursion sequences of any countable higher order include all the lower order ones and they can be optimally mapped to rational number system Q operational representations and generating functions exactly. CICT sees natural Integers N as specific numeric resonances emerging out of the OECS (optimized exponential cyclic sequence) (Fiorini, 2015b) manifold Q of rational values. In turn, OECS can be thought as emerging out from the peculiar numeric resonances of irrational numeric sequences generated by DS (Direct Space) – RS (Reciprocal Space) coherent cross-interaction with their duals (Fiorini, 2015c). DS-RS cross-interaction with their duals is assumed to be our representation fundamental property to model our spacetime quantum field theory (QFT) fluctuations effectively (Fiorini, 2015c). QFT has emerged from major paradigm shift with respect to Classical Physics which still provides the framework of the vision of nature of most scientists. All the implications of this big change have not been realized hitherto, even less their related, vital applications (Bischof, 1998). The discreteness approach, developed under the Quantum Theory (QT) “discreteness hypothesis” (DH) assumption, has been considered in peculiar application areas only. It has been further slowly developed by a few specialists and less understood by a wider audience to arrive to the fresh QFT approach.
Data, information, knowledge, and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom-up (BU). Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing.
The LRMB (Layered Reference Model of the Brain) (Wang, 2012; Wang et al, 2006) provides an integrated framework for modeling the brain and the mind. LRMB also enables future extension and refinement of the CPs (Cognitive Processes) within the same hierarchical framework. LRMB can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, those of the subconscious and conscious CPs, as well as the dichotomy between two modes of thought: “System 1”, fast, instinctive and emotional; “System 2”, slower, more deliberative, and more logical (Kahneman, 2011). CICT can provide LRMB with a natural computational framework to capture even quantum system behavior modeling effectively.