Predictable Internal Brain Dynamics in EEG and Its Relation to Conscious States

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.The open access fee for this work was funded through the Texas A&M University Open Access to Knowledge (OAK) Fund

Free Will of an Ontologically Open Mind

The problem of free will has persistently resisted a solution throughout centuries. There is reason to believe that new elements need to be introduced into the analysis in order to make progress. In the present physicalist approach, these elements are emergence and information theory in relation to universal limits set by quantum physics. Furthermore the common, but vague, characterization of free will as "being able to act differently" is, in the spirit of Carnap, rephrased into an explicatum more suitable for formal analysis. It is argued that the mind is an ontologically open system; a causal high-level system, the future of which cannot be reduced to the states of its associated low-level neural systems, not even if it is rendered physically closed. A positive answer to the question of free will is subsequently outlined

The Role of Consciousness in Memory

Conscious events interact with memory systems in learning, rehearsal and retrieval (Ebbinghaus 1885/1964; Tulving 1985). Here we present hypotheses that arise from the IDA computional model (Franklin, Kelemen and McCauley 1998; Franklin 2001b) of global workspace theory (Baars 1988, 2002). Our primary tool for this exploration is a flexible cognitive cycle employed by the IDA computational model and hypothesized to be a basic element of human cognitive processing. Since cognitive cycles are hypothesized to occur five to ten times a second and include interaction between conscious contents and several of the memory systems, they provide the means for an exceptionally fine-grained analysis of various cognitive tasks. We apply this tool to the small effect size of subliminal learning compared to supraliminal learning, to process dissociation, to implicit learning, to recognition vs. recall, and to the availability heuristic in recall. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. In most cases, memory is hypothesized to interact with conscious events for its normal functioning. The methodology of the paper is unusual in that the hypotheses and explanations presented are derived from an empirically based, but broad and qualitative computational model of human cognition

Corticonic models of brain mechanisms underlying cognition and intelligence

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it:(a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime bymeans of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo–cortical loop, (e) distinguishes between redundant (structured)and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo–cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions. Physics of Life Reviews 4 (2007) 223–252 © 2007 Elsevier B.V. All rights reserved

From locomotion to dance and back : exploring rhythmic sensorimotor synchronization

Le rythme est un aspect important du mouvement et de la perception de l’environnement. Lorsque l’on danse, la pulsation musicale induit une activité neurale oscillatoire qui permet au système nerveux d’anticiper les évènements musicaux à venir. Le système moteur peut alors s’y synchroniser. Cette thèse développe de nouvelles techniques d’investigation des rythmes neuraux non strictement périodiques, tels que ceux qui régulent le tempo naturellement variable de la marche ou la perception rythmes musicaux. Elle étudie des réponses neurales reflétant la discordance entre ce que le système nerveux anticipe et ce qu’il perçoit, et qui sont nécessaire pour adapter la synchronisation de mouvements à un environnement variable. Elle montre aussi comment l’activité neurale évoquée par un rythme musical complexe est renforcée par les mouvements qui y sont synchronisés. Enfin, elle s’intéresse à ces rythmes neuraux chez des patients ayant des troubles de la marche ou de la conscience.Rhythms are central in human behaviours spanning from locomotion to music performance. In dance, self-sustaining and dynamically adapting neural oscillations entrain to the regular auditory inputs that is the musical beat. This entrainment leads to anticipation of forthcoming sensory events, which in turn allows synchronization of movements to the perceived environment. This dissertation develops novel technical approaches to investigate neural rhythms that are not strictly periodic, such as naturally tempo-varying locomotion movements and rhythms of music. It studies neural responses reflecting the discordance between what the nervous system anticipates and the actual timing of events, and that are critical for synchronizing movements to a changing environment. It also shows how the neural activity elicited by a musical rhythm is shaped by how we move. Finally, it investigates such neural rhythms in patient with gait or consciousness disorders

Teadvuse neuronaalsete korrelaatide uurimismetoodika edasiarendusi

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Küsimust selle kohta, mis on teadvus, peetakse kaasaegse teaduse viimaseks tõeliselt suureks probleemiks, sest senini pole mitte keegi suutnud selgitada, miks ja kuidas aju töö on seotud teadvuselamustega. Tänapäeval on üldiselt aktsepteeritud, et teadvuse mõistmiseks on esimese sammuna tarvis täpsemalt määratleda ja tundma õppida teadvuse neuronaalseid korrelaate – neid ajuprotsesse, mis on minimaalselt tarvilikud mingi kindla teadvuselamuse jaoks. Antud väitekirjas on sellele eesmärgile kaasa aidatud nägemistajuga kaasnevate teadvuselamuste neuronaalsete korrelaatide ning nende markerite uurimise kaudu. Kuna on tõenäoline, et varasemate uurimuste tulemused ei peegelda mitte ainult teadvuse neurokorrelaate, vaid ka neile süstemaatiliselt eelnevate või järgnevate protsesside korrelaate, püüti käesolevas töös neid kaasnevaid protsesse täpsemalt uurida ning osades katsetes metodoloogilistel eesmärkidel hoopis vältida. Selleks kasutati varasemast hoolikamalt planeeritud katseparadigmasid, tulemuste töötlemise uusi võtteid ning mitmekesisemat loomulikku pildimaterjali. Lisaks võeti arvesse neurovõrgustike pidevalt muutuvat dünaamilist seisundit ja selle mõju teadvustamise protsessidele. Kokkuvõtvalt võib öelda, et väitekirjas kajastatud uurimustööde tulemuste järgi hakkavad visuaalsete teadvuselamuste vahetud neuromarkerid ilmnema umbes 200 ms jooksul pärast visuaalse stimulatsiooni jõudmist võrkkestale. Samas võib teadvuselamuste kujunemine aga olla hoopis järkjärguline protsess, sest teadvusega korreleerub usaldusväärselt ka umbes 100 ms hilisem neuromarker. Lisaks viitavad tulemused sellele, et visuaalse süsteemi keeruline dünaamika tuleb kõige paremini esile realistliku pildimaterjaliga, mitte lihtsate ja kunstlike geomeetriliste kujunditega. Inimajus on talletunud ohtralt eelteadmisi reaalse visuaalse maailma ning selle reeglipärasuste kohta. See informatsioon mõjutab juba väga varajases faasis nägemistaju ja teadvustamise protsesse, neid täiendades ning parandades.Consciousness is the biggest unsolved problem of modern science because no one has successfully explained how the concerted firing of brain cells is able to produce our subjective experience of the world. It has been argued that in order to understand this phenomenon better we must first identify the neural correlates of consciousness – those neural events which are jointly sufficient for producing a specific conscious experience. The present thesis was set to contribute to this research effort by investigating the neural correlates and markers of conscious visual perception. It is likely that previous studies have failed to identify the true correlates of consciousness because their results also contain processes that systematically precede or follow conscious experience, but do not directly reflect consciousness itself. Thus, the current aim was to study these additional processes in more detail or to even avoid their contribution is some studies. To that end more carefully designed experimental paradigms and more realistic stimulus material was employed. Furthermore, the ongoing state of more global neural networks and its influence on conscious perception was taken into account. Together the results demonstrate that neural markers of conscious visual perception begin to arise about 200 ms after the visual image reaches our eyes. It is however possible that conscious perception is a gradual phenomenon proceeding step-by-step, because another marker also reliably correlates with conscious visual perception around 100 ms later. Furthermore, some results indicate that the complex dynamics of our visual system are best observable with realistic images and not to the same extent with simple and artificial figures. The brain retains a lot of prior knowledge about the natural visual world and its regularities. This information influences the processes of conscious perception early on by complementing and correcting them

A mean field approach to model levels of consciousness from EEG recordings

We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications