Neurodynamics Of Cognition And Consciousness (U... !!TOP!!
Dynamic aspects of higher cognitive functions are addressed. Dynamical neural networks with encoding in limit cycle and non-convergent attractors have gained increasing popularity in the past decade. Experimental evidence in humans and other mammalians indicates that complex neurodynamics is crucial for the emergence of higher-level intelligence and consciousness. We give an overview of research activities in the field, including dynamic models of consciousness, experiments to identify neurodynamic correlates of cognitive functions, interpretation of experimental findings, development of dynamical neural memories, and applications of dynamical approaches to intelligent system.
Neurodynamics of Cognition and Consciousness (U...
The complex neurodynamics at a mesoscopic level of the brain seems significant for the macroscopic phenomena of cognition and consciousness (Århem & Liljenström, 2007). It has been related to perception, attention and associative memory, but also to volition and activity in the sensory and motor areas of the brain. Even though many details are still unknown, it is obvious that there is an interplay between the neurodynamics of the sensory and motor systems, essential for the interaction with our environment in a perception-action cycle (Cotterill, 1998; Freeman, 2000).
The chapter discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions and neural dynamics. Although evidence for this drive was discussed by biologists for some time, its fundamental nature was unclear without mathematical modeling. We discuss mathematical difficulties encountered in the past attempts at modeling the mind and relate them to logic. The main mechanisms of the mind include instincts, concepts, emotions, and behavior. Neural modeling fields and dynamic logic mathematically describe these mechanisms and relate their neural dynamics to the knowledge instinct. Dynamic logic overcomes past mathematical difficulties encountered in modeling intelligence. Mathematical mechanisms of concepts, emotions, instincts, consciousness and unconscious are described and related to perception and cognition. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, and lead to complex dynamics of evolution of consciousness and cultures. Mathematical modeling of this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.
The entropic brain hypothesis by Robin L. Carhart-Harris (2014) refers to a theory which is informed by neuroimaging research that uses the hallucinogen induced neurological state to make inferences about other states of consciousness.The expression "entropy" is applied here in the context of states of consciousness and their associated neurodynamics, while high entropy is synonymous with high disorder.It is proposed that a general distinction can be made between two fundamentally different modes of cognition: Primary and secondary consciousness.
Primary consciousness is associated with unconstrained cognition and less ordered (higher-entropy) neurodynamics that preceded the development of modern, normal waking consciousness in adults. Examples include the psychedelic state, the rapid eye movement sleep (REM) state or the onset phase of psychosis.Secondary consciousness is associated with constrained cognition and more ordered neurodynamics. Examples include normal waking consciousness, the anesthetized or the depressed state.
What is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what "you" decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world. 041b061a72