Computational Aspects of Cognition and Consciousness in Intelligent Devices

Abstract

Abstract:We review computational intelligence methods of sensory perception and cognitive functions in animals, humans, and artificial devices. Top-down symbolic methods and bottom-up sub-symbolic approaches are described. In recent years, computational intelligence, cognitive science and neuroscience have achieved a level of maturity that allows integration of top-down and bottom-up approaches in modeling the brain. Continuous adaptation and learning is a key component of computationally intelligent devices, which is achieved using dynamic models of cognition and consciousness. Human cognition performs a granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of concepts and complex behavioral schemas. They are accessed during action selection and conscious decision making as part of the intentional cognitive cycle. Implementations in computational and robotic environments are demonstrated.

Reflections on a giant of brain science

“…One of the key features of these discoveries lies in the mathematical models utilizing a system of coupled ordinary differential equations with distributed parameters. The nested hierarchy of Freeman K models (K0, KI, KII, KII, and KIV) describes brain dynamics from microscopic, through mesoscopic, to macroscopic structures and characterizes the corresponding cognitive functions Freeman 2001, 2003;Kozma et al , 2007. 3.…”

“…His revolutionary approach to nonlinear dynamics and chaos played leading role in using chaos theory to explain brain function (Aihara et al 1990;Borisyuk and Borisyuk 1997). Since their inception, Freeman K-sets have been used successfully as dynamic memory architectures (Chang and Freeman 1996;Kozma and Freeman 2001;Xu and Principe 2004;Harter and Kozma 2005;Ilin and Kozma 2006), as well for describing the dynamic operation of sensory cortices and the formation of multisensory percepts, Gestalts (Kozma et al 2007). …”

“…KIV is formed by the interaction of three (or more) KIII sets . It models the hemisphere with multiple sensory areas and the genesis of simple forms of intentional behaviors with intermittent synchronizationdesynchronization Kozma et al 2007). The KV set is not shown in the diagram and its implementation is to be completed in the future.…”

“…The cinematic synchronization of activity in diverse locations suggests that the transition that initiates the AM pattern does so by creating a field of nonsynaptic communication in the neuropil shared by all synchronized Fig. 5 Illustration of the hierarchy of Freeman K models, including K0, KI, KII, KII, and KIV sets of increasing complexity (Kozma et al 2007) cortical areas by which all local networks are coordinated simultaneously rather than by serial synaptic transmission among selected parts. The advantage is extremely rapid updating of the local memories to include associative contributions from all modalities within the time window defined by the duration of a global AM pattern (Freeman 2015).…”

Previsão de séries temporais econômicas usando redes neurais caóticas

“…No capítulo 3, serão apresentados trabalhos da literatura relacionados à esta dissertação e que contribuíram para seu desenvolvimento, juntamente com uma descrição de sua contribuição. No capítulo 4, serão Kozma et al (2007).…”