Consciousness and Complexity

Neurosciences Institute, 10640 John J. Hopkins Drive, San Diego, CA 92121, USA.
Science (Impact Factor: 33.61). 01/1999; 282(5395):1846-51. DOI: 10.1126/science.282.5395.1846
Source: PubMed


Conventional approaches to understanding consciousness are generally concerned with the contribution of specific brain areas or groups of neurons. By contrast, it is considered here what kinds of neural processes can account for key properties of conscious experience. Applying measures of neural integration and complexity, together with an analysis of extensive neurological data, leads to a testable proposal-the dynamic core hypothesis-about the properties of the neural substrate of consciousness.


Available from: Giulio Tononi, Sep 09, 2014
    • "functional or effective connectivity) between brain processes. These advances may provide key predictive information regarding brain function and dysfunction [8] [9] [10] [11]. In particular, measuring interactions at the level of cortical sources, rather than sensors can offer increased interpretability while reducing confounding factors of volume conduction [12] [13] [14]. "
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    ABSTRACT: Goal: We present and evaluate a wearable highdensity dry electrode EEG system and an open-source software framework for online neuroimaging and state classification. Methods: The system integrates a 64-channel dry EEG formfactor with wireless data streaming for online analysis. A realtime software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in 9 subjects using the dry EEG system. Results: Simulations yielded high accuracy (AUC=0.97?0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity (sdDTF) was significantly above chance with similar performance (AUC) for cLORETA (0.74?0.09) and LCMV (0.72?0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74?0.16) but significantly better for LCMV (0.82?0.12). Conclusion: We demonstrated the feasibility for realtime cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. Significance: This paper is the first validated application of these methods to 64-channel dry EEG. The work addresses a need for robust realtime measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and braincomputer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.
    IEEE Transactions on Biomedical Engineering 09/2015; pp(99):1. DOI:10.1109/TBME.2015.2481482 · 2.35 Impact Factor
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    • "Addressing this question in typical development is necessary to understand brain stability in relation to cognitive flexibility in neurodevelopmental disorders such as autism spectrum disorder (ASD). Furthermore, a greater balance between opposing stable and unstable inclinations in functional brain data implies greater brain complexity, a concept that, while variously defined (Coffey 1998; Janjarasjitt et al. 2008; Manor and Lipsitz 2012; Meyer-Lindenberg 1996; Sporns 2011; Tononi and Edelman 1998), has already shown potential as a biomarker of ASD (Bosl et al. 2011; Catarino et al. 2011; Eldridge et al. 2014; Ghanbari et al. 2013). "
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    ABSTRACT: This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = −0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.
    Brain Imaging and Behavior 12/2014; 9(1). DOI:10.1007/s11682-014-9339-3 · 4.60 Impact Factor
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    • "Importantly, stability, strength, or distinctiveness can be achieved by different means. They can result, for instance, from the simultaneous top-down and bottom-up activation involved in the so-called reentrant processing (Lamme, 2004), from processes of " adaptive resonance " (Grossberg, 1999), from processes of " integration and differentiation " (Tononi & Edelman, 1998), or from contact with the neural workspace, brought about by " dynamic mobilization " (Dehaene & Naccache, 2001). It is important to realize that the ultimate effect of any of these putative mechanisms is to make the target representations stable, strong, and distinctive, in precisely the way attractor basins instantiate in dynamical connectionist networks (Mathis & Mozer, 1996). "
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    ABSTRACT: Consciousness remains a mystery—“a phenomenon that people do not know how to think about—yet” (Dennett, 1991, p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the “computational correlates of consciousness” (Mathis & Mozer, 1996) because it makes it possible to focus on the mechanisms through which information processing takes place.
    Cognitive Science A Multidisciplinary Journal 08/2014; 38(6). DOI:10.1111/cogs.12149 · 2.59 Impact Factor
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