Explore the vast range of titles available.

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well.

The finding of power law scaling in neural recordings lends support to the hypothesis of critical brain dynamics. However, power laws are not unique to critical systems and can arise from alternative mechanisms. Here, we investigate whether a common time-varying external drive to a set of Poisson units can give rise to neuronal avalanches and exhibit apparent criticality. To this end, we analytically derive the avalanche size and duration distributions, as well as additional measures, first for homogeneous Poisson activity, and then for slowly varying inhomogeneous Poisson activity. We show that homogeneous Poisson activity cannot give rise to power law distributions. Inhomogeneous activity can also not generate perfect power laws, but it can exhibit approximate power laws with cutoffs that are comparable to those typically observed in experiments. The mechanism of generating apparent criticality by time-varying external fields, forces or input may generalize to many other systems like dynamics of swarms, diseases or extinction cascades. Here, we illustrate the analytically derived effects for spike recordings in vivo and discuss approaches to distinguish true from apparent criticality. Ultimately, this requires causal interventions, which allow separating internal system properties from externally imposed ones.

Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a \"Mexican hat\" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.

Ultra-slow fluctuations are a hallmark of spontaneous cortical activity. We examine the hypothesis that these dynamics arise from recurrent neuronal networks operating near a phase-transition point, a state marked by “critical slowing down”. In such networks, a subtle shift toward criticality should selectively amplify slow fluctuations, providing a lever that can switch the cortex from quiet rest into self-generated behavior. Using a simple random recurrent network, we reproduce this amplification effect. The resulting spectra closely match intracranial electroencephalography from human visual cortex recorded during rest and during category-specific visual free recall. In particular, the model captures the experimentally observed enhancement of slow fluctuations during recall. These simulations reveal a parsimonious mechanism that explains spontaneous ultra-slow activity and enables rapid transitions between spontaneous states, suggesting that dynamic tuning toward criticality may be a general strategy by which cortical networks enter a generative mode.

Sensory deprivation has long been known to cause hallucinations or “phantom” sensations, the most common of which is tinnitus induced by hearing loss, affecting 10–20% of the population. An observable hearing loss, causing auditory sensory deprivation over a band of frequencies, is present in over 90% of people with tinnitus. Existing plasticity-based computational models for tinnitus are usually driven by homeostatic mechanisms, modeled to fit phenomenological findings. Here, we use an objective-driven learning algorithm to model an early auditory processing neuronal network, e.g., in the dorsal cochlear nucleus. The learning algorithm maximizes the network’s output entropy by learning the feed-forward and recurrent interactions in the model. We show that the connectivity patterns and responses learned by the model display several hallmarks of early auditory neuronal networks. We further demonstrate that attenuation of peripheral inputs drives the recurrent network towards its critical point and transition into a tinnitus-like state. In this state, the network activity resembles responses to genuine inputs even in the absence of external stimulation, namely, it “hallucinates” auditory responses. These findings demonstrate how objective-driven plasticity mechanisms that normally act to optimize the network’s input representation can also elicit pathologies such as tinnitus as a result of sensory deprivation.

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one's memory of past stimuli. Thus, these responses may serve as a probe of different individuals' remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework which suggests a more general, theoretically justifiable principle for biological and cognitive phenomena. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects' recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons and the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). MsCr measures reflect the behavior of conventional criticality measures (such as the branching parameter) across temporal scale. We applied MsCr to MEG and EEG data in several telling degraded information processing scenarios. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity: MsCr fingerprints showed deviance in the four states of compromised information processing examined in this study, disorders of consciousness, mild cognitive impairment, schizophrenia and even during pre-ictal activity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

Given the interest in improving executive functions, the present study examines a promising combination of two training techniques: neurofeedback training (NFT) and working memory training (WMT). NFT targeted increasing the amplitude of individual’s upper Alpha frequency band at the parietal midline scalp location (Pz), and WMT consisted of an established computerized protocol with working memory updating and set-shifting components. Healthy participants (n = 140) were randomly allocated to five combinations of training, including visual search training used as an active control training for the WMT; all five groups were compared to a sixth silent control group receiving no training. All groups were evaluated before and after training for resting-state electroencephalogram (EEG) and behavioral executive function measures. The participants in the silent control group were unaware of this procedure, and received one of the training protocols only after study has ended. Results demonstrated significant improvement in the practice tasks in all training groups including non-specific influence of NFT on resting-state EEG spectral topography. There was only a near transfer effect (improvement in working memory task) for WMT, which remained significant in the delayed post-test (after 1 month), in comparison to silent control group but not in comparison to active control training group. The NFT + WMT combined group showed improved mental rotation ability both in the post-training and in the follow-up evaluations. This improvement, however, did not differ significantly from that in the silent control group. We conclude that the current training protocols, including their combination, have very limited influence on the executive functions that were assessed in this study.

Motor imagery (MI) based brain computer interfaces (BCI) detect changes in brain activity associated with imaginary limb movements, and translate them into device commands. MI based BCIs require training, during which the user gradually learns how to control his or her brain activity with the help of feedback. Additionally, machine learning techniques are frequently used to boost BCI performance and to adapt the decoding algorithm to the user's brain. Thus, both the brain and the machine need to adapt in order to improve performance. To study the utility of co-adaptive training in the BCI paradigm and the time scales involved, we investigated the performance of 18 subjects in two groups, in a 4-day MI experiment using EEG recordings. One group (control, n=9 subjects) performed the BCI task using a fixed classifier based on MI data from day 1. In the second group (experimental, n=9 subjects), the classifier was regularly adapted based on brain activity patterns during the experiment days. We found that the experimental group showed a significantly larger change in performance following training compared to the control group. Specifically, although the experimental group exhibited a decrease in performance between days, it showed an increase in performance within each day, which compensated for the decrease. The control group showed decreases both within and between days. A correlation analysis in subjects who had a notable improvement in performance following training showed that performance was mainly associated with modulation of power in the $\\alpha$ frequency band. To conclude, continuous updating of the classification algorithm improves the performance of subjects in longitudinal BCI training.

During flight, spatial disorientation (SD) commonly occurs when a pilot’s perception conflicts with the aircraft’s actual motion, attitude, or position. A prevalent form of SD is the somatogyral illusion, which is elicited by constant speed rotation and causes a false perception of motion in the opposite direction when the rotation ceases. This research aimed to investigate changes in brain activity that occur when experiencing a somatogyral illusion by simulating conditions closely mimicking flight conditions to gain insight into how to better manage this illusion during flight. In the study, 23 volunteers were isolated from external stimuli to promote somatogyral illusion induction while seated in a Barany (rotating) chair. The study employed electroencephalogram (EEG) and eye-tracking glasses to monitor brain activity and eye movements, respectively. Participants reported their perceived motion direction using a joystick, allowing us to compare a reference condition to that of the illusion. Results indicate a significant decrease (34%) in theta power (4–7.5 Hz) over the left frontal region during the illusion, complemented by the occurrence of nystagmus in 72% of the trials. These findings align with previous studies linking SD and theta band changes, suggesting implications for EEG-based identification of SD in flight.