An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Minimal BSDT abstract selectional machines and their selectional and computational performance
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
| Hi-index | 0.00 |
At the ground of most brain computations may be minimal abstract selectional machines (ASMs) implementing optimal algorithms of recent binary signal detection theory (BSDT). Using the BSDT ASMs, such fundamental cognitive notions as subjectivity and the meaning of a message have already been defined mathematically. BSDT neural network assembly memory model provides strict and biologically plausible definition of optimal assembly memory units (AMUs, implementations of ASMs) which may be considered as `atoms' of consciousness (AOCs). The idea of an AOC is here developed into an `atom' of consciousness model (AOCM) -- a mathematical theory of consciousness. Neuronal computational structures leading to the emergence of subjective experience or a `quale' (a formal solution of the `hard problem' of consciousness) are presented as complex dynamical hierarchical associations of AMUs/AOCs of infinite prehistory. Within the AOCM framework some cognitive phenomena are explained and it has been demonstrated that unified and modular biological models of consciousness are not antithetical.