In different paper, 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. This change of paradigm has not yet been completely grasped by many contemporary scientific disciplines and current researchers, so that not all the implications of this big change have been realized hitherto, even less their related, vital applications. The fact that we can build devices that implement the same basic operations as those the nervous system uses leads to the inevitable conclusion that we should be able to build entire systems based on the organizing principles used by the nervous system. Nevertheless, the human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine. The unavoidable conclusion is that we have something fundamental to learn from the brain and biology about new and much more effective form of computation. Thus, one of the key questions in understanding the quantum-classical transition is what happens to the superposition as you go up that atoms-to-apple scale. Exactly when and how does "both/and" become "either/or"? As a sound example, we present and discuss the observer space-time splitting case. In other words, we show spacetime mapping to classical system additive representation with loss of overall system fundamental information. It is exactly at this point that "both/and" becomes "either/or" representation by usual Science 1.0 approach. CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space) of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean (direct space) surface representation can open the way to holographic information geometry (HIG) to recover lost coherence information. CICT can help us to develop strategies to gather much more reliable experimental information from single experimentation and to keep overall system information coherence. In this way, coherent representation precision leads to information conservation and clarity. This paper is a relevant contribute towards an effective and convenient "Science 2.0" universal framework to develop computer science innovative application and beyond.

Brain Modeling, Spacetime Splitting and Computer Science

FIORINI, RODOLFO
2015-01-01

Abstract

In different paper, 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. This change of paradigm has not yet been completely grasped by many contemporary scientific disciplines and current researchers, so that not all the implications of this big change have been realized hitherto, even less their related, vital applications. The fact that we can build devices that implement the same basic operations as those the nervous system uses leads to the inevitable conclusion that we should be able to build entire systems based on the organizing principles used by the nervous system. Nevertheless, the human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine. The unavoidable conclusion is that we have something fundamental to learn from the brain and biology about new and much more effective form of computation. Thus, one of the key questions in understanding the quantum-classical transition is what happens to the superposition as you go up that atoms-to-apple scale. Exactly when and how does "both/and" become "either/or"? As a sound example, we present and discuss the observer space-time splitting case. In other words, we show spacetime mapping to classical system additive representation with loss of overall system fundamental information. It is exactly at this point that "both/and" becomes "either/or" representation by usual Science 1.0 approach. CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space) of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean (direct space) surface representation can open the way to holographic information geometry (HIG) to recover lost coherence information. CICT can help us to develop strategies to gather much more reliable experimental information from single experimentation and to keep overall system information coherence. In this way, coherent representation precision leads to information conservation and clarity. This paper is a relevant contribute towards an effective and convenient "Science 2.0" universal framework to develop computer science innovative application and beyond.
2015
Proc. of the 6th European Conference of Computer Science (ECCS ’15)
978-1-61804-344-3
quantum-classical transition, CICT, spacetime, decoherence, incomputability, combinatorial optimization, reversibility, quantum mechanics, entropy, statistical mechanics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/991280
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