Design and evaluation of a global workspace agent embodied in a realistic multimodal environment.

As the apparent intelligence of artificial neural networks (ANNs) advances, they are increasingly likened to the functional networks and information processing capabilities of the human brain. Such comparisons have typically focused on particular modalities, such as vision or language. The next frontier is to use the latest advances in ANNs to design and investigate scalable models of higher-level cognitive processes, such as conscious information access, which have historically lacked concrete and specific hypotheses for scientific evaluation.

A comparison of visual and auditory EEG interfaces for robot multi-stage task control.

Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy-most commonly electroencephalography-based-the two most common choices are to provide either auditory or visual stimuli to the user-each with their respective pros and cons.

Association of bidirectional network cores in the brain with perceptual awareness and cognition.

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions.

Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms.

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms.

System Integrated Information.

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause-effect structure unfolded from a maximally irreducible substrate (a Φ-structure).

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data.

Quantifying brain state transition cost via Schrödinger Bridge.

Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost.

Frequency-specific task modulation of human brain functional networks: A fast fMRI study.

How coherent neural oscillations are involved in task execution is a fundamental question in neuroscience. Although several electrophysiological studies have tackled this issue, the brain-wide task modulation of neural coherence remains uncharacterized. Here, with a fast fMRI technique, we studied shifts of brain-wide neural coherence across different task states in the ultraslow frequency range (0.

A general spectral decomposition of causal influences applied to integrated information.

Background: Quantifying interactions among many neurons is fundamental to understanding system-level phenomena such as attention, learning and even conscious experience. Causal influences in the brain, quantified as integrated information, are thought to support subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences, for example using Granger causality, can reveal frequency-specific influences that are not observed in the time domain.

Regulation of cortical activity and arousal by the matrix cells of the ventromedial thalamic nucleus.

The "non-specific" ventromedial thalamic nucleus (VM) has long been considered a candidate for mediating cortical arousal due to its diffuse, superficial projections, but direct evidence was lacking. Here, we show in mice that the activity of VM calbindin1-positive matrix cells is high in wake and REM sleep and low in NREM sleep, and increases before cortical activity at the sleep-to-wake transition. Optogenetic stimulation of VM cells rapidly awoke all mice from NREM sleep and consistently caused EEG activation during slow wave anesthesia, while arousal did not occur from REM sleep.

Differentiation Analysis of Continuous Electroencephalographic Activity Triggered by Video Clip Contents.

While viewing a video clip, we experience a wide variety of contents, from low-level features of the images to high-level ideas such as the storyline. Each change in our experience must be supported by some corresponding change in neurophysiological activity. Differentiation analysis, which quantifies the differences in brain activity by measuring the distances between observed brain states, was applied here to continuous high-density electroencephalographic data recorded while participants watched short video clips.

Functional split brain in a driving/listening paradigm.

We often engage in two concurrent but unrelated activities, such as driving on a quiet road while listening to the radio. When we do so, does our brain split into functionally distinct entities? To address this question, we imaged brain activity with fMRI in experienced drivers engaged in a driving simulator while listening either to global positioning system instructions (integrated task) or to a radio show (split task). We found that, compared with the integrated task, the split task was characterized by reduced multivariate functional connectivity between the driving and listening networks.

Mapping Multiplex Hubs in Human Functional Brain Networks.

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions.

Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study.

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of 'TV noise' frames, triggers always the same experience--of seeing 'TV noise'--even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject's experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli.

Frequency-specific network topologies in the resting human brain.

A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both "communities" and "hubs" exist simultaneously in the brain's functional connectivity network (FCN), as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signal changes (0.

A NIRS-fMRI study of resting state network.

Resting state functional connectivity, which is defined as temporal correlation of spontaneous activity between diverse brain regions, has been reported to form resting state networks (RSNs), consisting of a specific set of brain regions, based on functional magnetic resonance imaging (fMRI). Recently, studies using near-infrared spectroscopy (NIRS) reported that NIRS signals also show temporal correlation between different brain regions. The local relationship between NIRS and fMRI signals has been examined by simultaneously recording these signals when participants perform tasks or respond to stimuli.

Frequency-specific functional connectivity in the brain during resting state revealed by NIRS.

Analyses of spontaneous hemodynamic fluctuations observed on functional magnetic resonance imaging (fMRI) have revealed the existence of temporal correlations in signal changes between widely separated brain regions during the resting state, termed "resting state functional connectivity." Recent studies have demonstrated that these correlations are also present in the hemodynamic signals measured by near infrared spectroscopy (NIRS). However, it is still uncertain whether frequency-specific characteristics exist in these signals.