Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems

Panpsychism and dualism in the science of consciousness

A resurgence of panpsychism and dualism is a matter of ongoing debate in modern neuroscience. Although metaphysically hostile, panpsychism and dualism both persist in the science of consciousness because the former is proposed as a straightforward answer to the problem of integrating consciousness into the fabric of physical reality, whereas the latter proposes a simple solution to the problem of free will by endowing consciousness with causal power as a prerequisite for moral responsibility. I take the Integrated Information Theory (IIT) as a paradigmatic exemplar of a theory of consciousness (ToC) that makes its commitments to panpsychism and dualism within a unified framework. These features are not, however, unique for IIT. Many ToCs are implicitly prone to some degree of panpsychism whenever they strive to propose a universal definition of consciousness, associated with one or another known phenomenon. Yet, those ToCs that can be characterized as strongly emergent are at risk of being dualist. A remedy against both covert dualism and uncomfortable corollaries of panpsychism can be found in the evolutionary theory of life, called here "bioprotopsychism" and generalized in terms of autopoiesis and the free energy principle. Bioprotopsychism provides a biologically inspired basis for a minimalist approach to consciousness by associating the stream of weakly emergent conscious states with an amount of information (best guesses) of the brain, engaged in unconscious predictive processing.

Decrease in phase slip rates and phase cone structures during seizure evolution and epileptogenic activities derived from microgrid ECoG data

Sudden phase changes are related to cortical phase transitions, which likely change in frequency and spatial distribution as epileptogenic activity evolves. A 100 s long section of micro-ECoG data obtained before and during a seizure was selected and analyzed. In addition, nine other short-duration epileptic events were also examined. The data was collected at 420 Hz, imported into MATLAB, downsampled to 200 Hz, and filtered in the 1-50 Hz band. The Hilbert transform was applied to compute the analytic phase, which was then unwrapped, and detrended to look for sudden phase changes. The phase slip rate (counts/s) and its acceleration (counts/s2) were computed with a stepping window of 1-s duration and with a step size of 5 ms. The analysis was performed for theta (3-7 Hz), alpha (7-12 Hz), and beta (12-30 Hz) bands. The phase slip rate on all electrodes in the theta band decreased while it increased for the alpha and beta bands during the seizure period. Similar patterns were observed for isolated epileptogenic events. Spatiotemporal contour plots of the phase slip rates were also constructed using a montage layout of 8 × 8 electrode positions. These plots exhibited dynamic and oscillatory formation of phase cone-like structures which were higher in the theta band and lower in the alpha and beta bands during the seizure period and epileptogenic events. These results indicate that the formation of phase cones might be an excellent biomarker to study the evolution of a seizure and also the cortical dynamics of isolated epileptogenic events.

Abrupt phase changes coupled with waning in amplitude of neural oscillation lead to phase-locking in the auditory evoked responses

Neural oscillations on the human auditory cortex measured with the magnetoencephalography were band-pass filtered between 3 and 16 Hz and then divided into instantaneous phases and amplitudes by the Hilbert transformation. Spontaneously, the amplitudes fluctuated, i.e. waxed and waned; The phases rotated at around 6 Hz most of the time, but abruptly accelerated or decelerated when the amplitudes waned close to zero. After auditory stimuli, the amplitudes and the phases were coupled in the same way as spontaneously. Amounts and directions of the accelerations or decelerations were thereby specific so that the phases subsequently took mostly the same value, i.e. were locked, at around the time of N100 peaks in the auditory evoked responses. In short, the auditory evoked responses emerged from spontaneous oscillations by abrupt phase changes coupled with waning in amplitudes and phase-locking thereafter.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson's First Skewness Coefficient Extracted from EEG Data

It has been proposed that meditative states show different brain dynamics than other more engaged states. It is known that when people sit with closed eyes instead of open eyes, they have different brain dynamics, which may be associated with a combination of deprived sensory input and more relaxed inner psychophysiological and cognitive states. Here, we study such states based on a previously established experimental methodology, with the aid of an electro-encephalography (EEG) array with 128 electrodes. We derived the Shannon Entropy (H) and Pearson's 1st Skewness Coefficient (PSk) from the power spectrum for the modalities of meditation and video watching, including 20 participants, 11 meditators and 9 non-meditators. The discriminating performance of the indices H and PSk was evaluated using Student's t-test. The results demonstrate a statistically significant difference between the mean H and PSk values during meditation and video watch modes. We show that the H index is useful to discriminate between Meditator and Non-Meditator participants during meditation over both the prefrontal and occipital areas, while the PSk index is useful to discriminate Meditators from Non-Meditators based on the prefrontal areas for both meditation and video modes. Moreover, we observe episodes of anti-correlation between the prefrontal and occipital areas during meditation, while there is no evidence for such anticorrelation periods during video watching. We outline directions of future studies incorporating further statistical indices for the characterization of brain states.

From the origins to the stream of consciousness and its neural correlates

There are now dozens of very different theories of consciousness, each somehow contributing to our understanding of its nature. The science of consciousness needs therefore not new theories but a general framework integrating insights from those, yet not making it a still-born "Frankenstein" theory. First, the framework must operate explicitly on the stream of consciousness, not on its static description. Second, this dynamical account must also be put on the evolutionary timeline to explain the origins of consciousness. The Cognitive Evolution Theory (CET), outlined here, proposes such a framework. This starts with the assumption that brains have primarily evolved as volitional subsystems of organisms, inherited from primitive (fast and random) reflexes of simplest neural networks, only then resembling error-minimizing prediction machines. CET adopts the tools of critical dynamics to account for metastability, scale-free avalanches, and self-organization which are all intrinsic to brain dynamics. This formalizes the stream of consciousness as a discrete (transitive, irreflexive) chain of momentary states derived from critical brain dynamics at points of phase transitions and mapped then onto a state space as neural correlates of a particular conscious state. The continuous/discrete dichotomy appears naturally between the brain dynamics at the causal level and conscious states at the phenomenal level, each volitionally triggered from arousal centers of the brainstem and cognitively modulated by thalamocortical systems. Their objective observables can be entropy-based complexity measures, reflecting the transient level or quantity of consciousness at that moment.

Cognition Without Neural Representation: Dynamics of a Complex System

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

Evolutionary Advantages of Stimulus-Driven EEG Phase Transitions in the Upper Cortical Layers

Spatio-temporal brain activity monitored by EEG recordings in humans and other mammals has identified beta/gamma oscillations (20-80 Hz), which are self-organized into spatio-temporal structures recurring at theta/alpha rates (4-12 Hz). These structures have statistically significant correlations with sensory stimuli and reinforcement contingencies perceived by the subject. The repeated collapse of self-organized structures at theta/alpha rates generates laterally propagating phase gradients (phase cones), ignited at some specific location of the cortical sheet. Phase cones have been interpreted as neural signatures of transient perceptual experiences according to the cinematic theory of brain dynamics. The rapid expansion of essentially isotropic phase cones is consistent with the propagation of perceptual broadcasts postulated by Global Workspace Theory (GWT). What is the evolutionary advantage of brains operating with repeatedly collapsing dynamics? This question is answered using thermodynamic concepts. According to neuropercolation theory, waking brains are described as non-equilibrium thermodynamic systems operating at the edge of criticality, undergoing repeated phase transitions. This work analyzes the role of long-range axonal connections and metabolic processes in the regulation of critical brain dynamics. Historically, the near 10 Hz domain has been associated with conscious sensory integration, cortical "ignitions" linked to conscious visual perception, and conscious experiences. We can therefore combine a very large body of experimental evidence and theory, including graph theory, neuropercolation, and GWT. This cortical operating style may optimize a tradeoff between rapid adaptation to novelty vs. stable and widespread self-organization, therefore resulting in significant Darwinian benefits.

A guideline for linking brain wave findings to the various aspects of discrete perception

Brain waves, determined by electrical and magnetic brain recordings (e.g., EEG and MEG), and fluctuating behavioral responses, determined by response time or accuracy measures, are frequently taken to support discrete perception. For example, it has been proposed that humans experience only one conscious percept per brain wave (e.g., during one alpha cycle). However, the proposed link between brain waves and discrete perception is typically rather vague. More importantly, there are many models and aspects of discrete perception and it is often not apparent in what theoretical framework brain wave findings are interpreted and to what specific aspects of discrete perception they relate. Here, we review different approaches to discrete perception and highlight issues with particular interpretations. We then discuss how certain findings on brain waves may relate to certain aspects of discrete perception. The main purpose of this meta‐contribution is to give a short overview of discrete models of perception and to illustrate the need to make explicit what aspects of discrete theories are addressed by what aspects of brain wave findings.

Increased Phase Cone Turnover in 80-250 Hz Bands Occurs in the Epileptogenic Zone During Interictal Periods

We found that phase cone clustering patterns in EEG ripple bands demonstrate an increased turnover rate in epileptogenic zones compared to adjacent regions. We employed 256 channel EEG data collected in four adult subjects with refractory epilepsy. The analysis was performed in the 80-150 and 150-250 Hz ranges. Ictal onsets were documented with intracranial EEG recordings. Interictal scalp recordings, free of epileptiform patterns, of 240-s duration, were selected for analysis for each subject. The data was filtered, and the instantaneous phase was extracted after the Hilbert transformation. Spatiotemporal contour plots of the unwrapped instantaneous phase with 1.0 ms intervals were constructed using a montage layout of the 256 electrode positions. Stable phase cone patterns were selected based on criteria that the sign of spatial gradient did not change for a minimum of three consecutive time samples and the frame velocity was consistent with known propagation velocities of cortical axons. These plots exhibited increased dynamical formation and dissolution of phase cones in the ictal onset zones, compared to surrounding cortical regions, in all four patients. We believe that these findings represent markers of abnormally increased cortical excitability. They are potential tools that may assist in localizing the epileptogenic zone.

The rhythm of memory: how breathing shapes memory function

The mammalian olfactory bulb displays a prominent respiratory rhythm, which is linked to the sniff cycle and is driven by sensory input from olfactory receptors in the nasal sensory epithelium. In rats and mice, respiratory frequencies occupy the same band as the hippocampal θ-rhythm, which has been shown to be a key player in memory processes. Hippocampal and olfactory bulb rhythms were previously found to be uncorrelated except in specific odor-contingency learning circumstances. However, many recent electrophysiological studies in both rodents and humans reveal a surprising cycle-by-cycle influence of nasal respiration on neuronal activity throughout much of the cerebral cortex beyond the olfactory system, including the prefrontal cortex, hippocampus, and subcortical structures. In addition, respiratory phase has been shown to influence higher-frequency oscillations associated with cognitive functions, including attention and memory, such as the power of γ-rhythms and the timing of hippocampal sharp wave ripples. These new findings support respiration's role in cognitive function, which is supported by studies in human subjects, in which nasal respiration has been linked to memory processes. Here, we review recent reports from human and rodent experiments that link respiration to the modulation of memory function and the neurophysiological processes involved in memory in rodents and humans. We argue that respiratory influence on the neuronal activity of two key memory structures, the hippocampus and prefrontal cortex, provides a potential neuronal mechanism behind respiratory modulation of memory.

Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach

We developed a framework to study brain dynamics under cognition. In particular, we investigated the spatiotemporal properties of brain state switches under cognition. The lack of electroencephalography stationarity is exploited as one of the signatures of the metastability of brain states. We correlated power law exponents in the variables that we proposed to describe brain states, and dynamical properties of non-stationarities with cognitive conditions. This framework was successfully tested with three different datasets: a working memory dataset, an Alzheimer disease dataset, and an emotions dataset. We discuss the temporal organization of switches between states, providing evidence suggesting the need to reconsider the piecewise model, in which switches appear at discrete times. Instead, we propose a more dynamically rich view, in which besides the seemingly discrete switches, switches between neighbouring states occur all the time. These micro switches are not (physical) noise, as their properties are also affected by cognition.

Nondual awareness: Consciousness-as-such as non-representational reflexivity

I introduce arguments toward a non-representational reflexivity theory of consciousness-as-such to address one of the key issues in the science of consciousness today: lack of understanding of the nature of consciousness itself. An expanded map of consciousness is outlined, which includes, in addition to the well-known contents of awareness and levels of arousal, the indeterminate substrate and consciousness-as-such or nondual awareness. The central idea presented is that consciousness-as-such is a non-conceptual nondual awareness, whose essential property is non-representational reflexivity. This property makes consciousness-as-such phenomenologically, cognitively and neurobiologically a unique kind, different from and irreducible to any contents, functions and states, including the indeterminate substrate. Our previous hypothesis on the precuneus network for nondual awareness is further discussed in relation to non-representational reflexivity, and in the light of other hypotheses on the neural correlates of consciousness-as-such.

Oscillatory Patterns of Phase Cone Formations near to Epileptic Spikes Derived from 256-Channel Scalp EEG Data

Our objective was to determine if there are any distinguishable phase cone clustering patterns present near to epileptic spikes. These phase cones arise from episodic phase shifts due to the coordinated activity of cortical neurons at or near to state transitions and can be extracted from the high-density scalp EEG recordings. The phase cone clustering activities in the low gamma band (30-50 Hz) and in the ripple band (80-150 Hz) were extracted from the analytic phase after taking Hilbert transform of the 256-channel high density (dEEG) data of adult patients. We used three subjects in this study. Spatiotemporal contour plots of the unwrapped analytic phase with 1.0 ms intervals were constructed using a montage layout of 256 electrode positions. Stable phase cone patterns were selected based on the criteria that the sign of the spatial gradient did not change for at least three consecutive time samples and the frame velocity was within the range of propagation velocities of cortical axons. These plots exhibited dynamical formation of phase cones which were higher in the seizure area as compared with the nearby surrounding brain areas. Spatiotemporal oscillatory patterns were also visible during ±5 sec period from the location of the spike. These results suggest that the phase cone activity might be useful for noninvasive localization of epileptic sites and also for examining the cortical neurodynamics near to epileptic spikes.

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening

Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the absence of sonification theory, extensibility demonstrates relevant methods across case studies. The interactive sonification framework foregrounds three factors: reproducible system implementation for generating sonification; interactive mechanisms enhancing a listener's multisensory observations; and reproducible data from models that characterize emergent behavior. Supramodal attention research suggests interactive exploration with auditory feedback can generate context for recognizing irregular patterns and transient dynamics. The sonification framework provides circular causality as a signal pathway for modeling a listener interacting with emergent behavior. The extensible sonification model adopts a data acquisition pathway to formalize functional symmetry across three subsystems: Experimental Data Source, Sound Generation, and Guided Exploration. To differentiate time criticality and dimensionality of emerging dynamics, tuning functions are applied between subsystems to maintain scale and symmetry of concurrent processes and temporal dynamics. Tuning functions accommodate sonification design strategies that yield order parameter values to render emerging patterns discoverable as well as rehearsable, to reproduce desired instances for clinical listeners. Case studies are implemented with two computational models, Chua's circuit and Swarm Chemistry social agent simulation, generating data in real-time that exhibits emergent behavior. Heuristic Listening is introduced as an informal model of a listener's clinical attention to data sonification through multisensory interaction in a context of structured inquiry. Three methods are introduced to assess the proposed sonification framework: Listening Scenario classification, data flow Attunement, and Sonification Design Patterns to classify sound control. Case study implementations are assessed against these methods comparing levels of abstraction between experimental data and sound generation. Outcomes demonstrate the framework performance as a reference model for representing experimental implementations, also for identifying common sonification structures having different experimental implementations, identifying common functions implemented in different subsystems, and comparing impact of affordances across multiple implementations of listening scenarios.