Neuroscience Theory Research Articles

Applications of Random Matrix Theory in Neuroscience and Neural Network Analysis: Unraveling High-Dimensional Connectivity

Random Matrix Theory (RMT) has emerged as a powerful mathematical framework to study large-scale, high-dimensional data across various scientific fields. In neuroscience, it provides unparalleled tools to elucidate the functional connectivity and dynamic properties underlying neural activity. By modeling connectivity matrices, synaptic weight distributions, and neural correlation structures through random matrices, researchers can dissect large-scale neural systems’ stability, detect phase transitions in neural firing patterns, and explain emergent phenomena such as criticality in brain networks. Similarly, in the realm of artificial neural networks, RMT guides the understanding of weight initialization, spectral properties of the network’s Jacobian matrices, generalization, and optimization landscapes. This article provides a comprehensive introduction to Random Matrix Theory’s fundamental principles, their application in contemporary neuroscience, and their intersection with modern neural network paradigms. We introduce the theoretical underpinnings and methodological steps for applying RMT to neuroscience data, including sequentially numbered didactic equations. We further present an illustrative code snippet to generate and visualize RMT-based graphs, discussing the results and connecting them to cutting-edge research. In a detailed discussion, we explore the benefits, limitations, and future directions of RMT methodologies in neuroscience, including ongoing challenges and emerging frontiers. This work aims to equip researchers with a robust theoretical and methodological framework, encouraging the integration of RMT into the neuroscience toolbox and catalyzing further breakthroughs in understanding complex neural systems.

Neuroeducation and Cognitive Behavioral Interventions: A Narrative Review

This paper provides a comprehensive narrative review at the intersection of neuroeducation and cognitive behavioral interventions (CBI), exploring how insights from cognitive neuroscience can enhance educational practices. It traces the historical progression of neuroeducation, proposes theoretical frameworks linking reading disabilities with comorbid conditions, and details the methodology underpinning the review. Key themes include the role of neuroplasticity in learning, memory, and cognitive development, and the application of CBIs in managing anxiety and other psychological factors in students. The review emphasizes the need for integrating neuroscientific theories into education, the potential of gamified programs and specialized platforms, and the importance of teachers' emotional involvement in student learning. Furthermore, it highlights significant gaps in current research and offers practical recommendations for incorporating neuroeducational principles into teacher training and educational policies. The findings underscore the impact of narrative reviews in advancing neuroeducation, pinpoint key interdisciplinary collaborations, and advocate for ongoing research to support innovation and transformation in education.

Fears Worth Testing Out: A Systematic Review of the Neural Mechanisms of Treatment Outcome for Anxiety-Related Disorders.

With the advent of human neuroimaging, researchers were drawn to the idea that by better understanding the human brain, more effective mental health interventions could be developed. It has been more than 20years since the first functional magnetic resonance imaging (fMRI) studies were conducted to examine changes in brain activation with anxiety-related treatments and more than 60 studies have since been published in this vein. For the current review, we conduct a systematic review of this literature, focusing on adult studies using task-based fMRI to measure brain activation changes with pharmacologic or psychotherapy interventions for phobia, social anxiety disorder, panic disorder, generalized anxiety disorder, posttraumatic stress disorder, and obsessive-compulsive disorder. Neuroscientific theories of anxiety-related disorders and their treatment have focused on prefrontal-insula-amygdala networks. Treatment-related decreases in amygdala and/or anterior insula activation were identified as the most consistent finding across disorders, with the most consistent results reported for specific phobia. Directionality of change and specific regions implicated in the prefrontal cortex were inconsistent across studies. The potential importance for probing other networks and processes as mechanisms of anxiety treatment was recognized, such as striatal regions underlying inhibitory learning or reward responsivity. Future treatment-fMRI research related to anxiety disorders would benefit from larger sample sizes, use of more nuanced computational approaches, and increased focus on replication. There is continued promise that fMRI research will enhance our understanding of how treatments work and inform the evolution of more effective or personalized mental health treatment.

A review of trauma-informed neuroscientific theory to unpack the early childhood education teacher’s pastoral role in South Africa

A review of trauma-informed neuroscientific theory to unpack the early childhood education teacher’s pastoral role in South Africa

Lifelong Learning With Cycle Memory Networks.

Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In contrast, this work aims to explore knowledge-transferable lifelong learning without storing historical data and significant additional computational overhead. We demonstrate that existing data-free frameworks, including regularization-based single-network and structure-based multinetwork frameworks, face a fundamental issue of lifelong learning, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. We attribute it to the fact that the learning network capacity decreases while memorizing historical knowledge and conceptual confusion between the irrelevant old knowledge and the current task. Inspired by the complementary learning theory in neuroscience, we endow artificial neural networks with the ability to continuously learn without forgetting while recalling historical knowledge to facilitate learning new knowledge. Specifically, this work proposes a general framework named cycle memory networks (CMNs). The CMN consists of two individual memory networks to store short- and long-term memories separately to avoid capacity shrinkage and a transfer cell between them. It enables knowledge transfer from the long-term to the short-term memory network to mitigate conceptual confusion. In addition, the memory consolidation mechanism integrates short-term knowledge into the long-term memory network for knowledge accumulation. We demonstrate that the CMN can effectively address the anterograde forgetting on several task-related, task-conflict, class-incremental, and cross-domain benchmarks. Furthermore, we provide extensive ablation studies to verify each framework component. The source codes are available at: https://github.com/GeoX-Lab/CMN.

Delayed testing in directed forgetting dissociates active and passive forms of forgetting.

Across two experiments, we assessed the rates of relative forgetting following instructions to remember or forget information in an item-method directed forgetting paradigm across several retention intervals. In addition to the Forget and Remember cues, we also included Thought Substitution (TS) cues in the same design instructing participants to mentally shift to a different context on some study trials. TS cues have been shown to impair memory compared with Remember cues, but not as effectively as cues to Forget in item-method studies (Hubbard & Sahakyan, 2021). The results demonstrated that Forget cues produce accelerated rates of forgetting compared with Remember cues and showed that these differences are independent of initial learning rates, which were deliberately equated in Experiment 2. TS cued items showed faster forgetting than Remember cued items but were less effective than Forget cues and exhibited a more complex pattern likely reflecting individual differences. Thus, delayed testing demonstrated that active forgetting can have long-lasting effects on memory traces beyond initial suppression, in line with cognitive neuroscientific theory suggesting inhibition can produce lasting changes to memory traces. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploration on the improvement of EEG dataset collection and preprocessing methods based on neuroscience theory

This paper analyzes current methods of EEG dataset collection and preprocessing within the framework of neuroscientific theories and proposes potential improvements. Existing datasets are limited because they primarily capture specific stages of neural activity during continuous recitation, missing other crucial stages of language generation. This limitation impacts the performance of deep learning models used for EEG data classification. By examining the multi-stage process of language formation, the paper highlights the need for data collection methods that encompass all stagesconceptualization, grammatical encoding, lexicalization, morphological and phonological encoding, and articulation. The review also discusses the benefits of using nonlinear dynamic system analysis methods over traditional covariance or cross-correlation matrices to better capture the complexities of EEG data. Finally, it suggests treating EEG data at various stages of language formation as multimodal data, as a strategy to improve the comprehensiveness and practicality of EEG datasets, thereby enhancing the accuracy and practical application of EEG-based brain-computer interfaces.

The gamma-band activity model of the near-death experience: a critique and a reinterpretation.

Near-death experience (NDE) is a transcendent mental event of uncertain etiology that arises on the cusp of biological death. Since the discovery of NDE in the mid-1970s, multiple neuroscientific theories have been developed in an attempt to account for it in strictly materialistic or reductionistic terms. Therefore, in this conception, NDE is at most an extraordinary hallucination without any otherworldly, spiritual, or supernatural denotations. During the last decade or so, a number of animal and clinical studies have emerged which reported that about the time of death, there may be a surge of high frequency electroencephalogram (EEG) at a time when cortical electrical activity is otherwise at a very low ebb. This oscillatory rhythm falls within the range of the enigmatic brain wave-labelled gamma-band activity (GBA). Therefore, it has been proposed that this brief, paradoxical, and perimortem burst of the GBA may represent the neural foundation of the NDE. This study examines three separate but related questions concerning this phenomenon. The first problem pertains to the electrogenesis of standard GBA and the extent to which authentic cerebral activity has been contaminated by myogenic artifacts. The second problem involves the question of whether agents that can mimic NDE are also underlain by GBA. The third question concerns the electrogenesis of the surge in GBA itself. It has been contended that this is neither cortical nor myogenic in origin. Rather, it arises in a subcortical (amygdaloid) location but is recorded at the cortex via volume conduction, thereby mimicking standard GBA. Although this surge of GBA contains genuine electrophysiological activity and is an intriguing and provocative finding, there is little evidence to suggest that it could act as a kind of neurobiological skeleton for a phenomenon such as NDE.

Assessment of Construction Workers’ Spontaneous Mental Fatigue Based on Non-Invasive and Multimodal In-Ear EEG Sensors

Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites.

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

An In-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience.

Functional magnetic resonance imaging (fMRI) has been a cornerstone of cognitive neuroscience since its invention in the 1990s. The methods that we use for fMRI data analysis allow us to test different theories of the brain, thus different analyses can lead us to different conclusions about how the brain produces cognition. There has been a centuries-long debate about the nature of neural processing, with some theories arguing for functional specialization or localization (e.g., face and scene processing) while other theories suggest that cognition is implemented in distributed representations across many neurons and brain regions. Importantly, these theories have received support via different types of analyses; therefore, having students implement hands-on data analysis to explore the results of different fMRI analyses can allow them to take a firsthand approach to thinking about highly influential theories in cognitive neuroscience. Moreover, these explorations allow students to see that there are not clearcut "right" or "wrong" answers in cognitive neuroscience, rather we effectively instantiate assumptions within our analytical approaches that can lead us to different conclusions. Here, I provide Python code that uses freely available software and data to teach students how to analyze fMRI data using traditional activation analysis and machine-learning-based multivariate pattern analysis (MVPA). Altogether, these resources help teach students about the paramount importance of methodology in shaping our theories of the brain, and I believe they will be helpful for introductory undergraduate courses, graduate-level courses, and as a first analysis for people working in labs that use fMRI.

APPLYING BLOOM’S TAXONOMY IN NEUROSCIENCE: A PRACTICAL EXAMPLES

Bloom's Taxonomy serves as a structured framework in neuroscience education, guiding educators in designing, implementing, and assessing learning objectives across various cognitive levels.This abstract explores the practical application of Bloom's Taxonomy in teaching neuroscience concepts, offering insights into effective student engagement.In neuroscience education, Bloom's Taxonomy is invaluable for curriculum development, instructional strategies, and assessment methods. At the foundational "remembering" level, students recall neuroanatomical structures, physiological processes, or terminology. For example, they identify brain regions or recall neurotransmitter functions.Progressing to the "understanding" level, students comprehend neuroscientific principles.This entails explaining action potential propagation or neural pathways in sensory perception. Educators use case studies, discussions, or multimedia for deeper understanding.At the "applying" level, students apply neuroscience knowledge to solve real-world problems. They analyze clinical cases and propose interventions based on neuroanatomy and physiology understanding."Analyzing" prompts critical evaluation of neuroscience theories or research. This involves literature reviews, study critiques, or experiment design.Finally, at the highest level of "creating," students are challenged to synthesize their understanding of neuroscience concepts to generate novel ideas, designs, or solutions. This could entail developing research proposals, designing educational materials, or proposing innovative treatments for neurological disorders.

PSYCHODYNAMIC PSYCHOTHERAPY FOR TIME DISTORTION IN ATTENTION DEFICIT HYPERACTIVITY DISORDER

Psychodynamic psychotherapy does not feature in current treatment guidelines for attention‐deficit hyperactivity disorder (ADHD) in the United Kingdom. However, there is some psychoanalytic understanding of the disorder, which is corroborated in current neuroscientific theory, and supports clinicians in thinking beyond the behaviourally biased diagnosis and treatment models. One of the core features of ADHD is distortions in chronoception. This clinical paper therefore explores the psychoanalytic and neuroscientific theories of time perception as a way of exploring a discrete element of the complex idea of executive functioning more generally. It supports therapists approaching such executive dysfunction and to reconsider the treatment guidelines. A case example is used to explore how a transference‐, affect‐ and relationally‐focussed therapy can effect lasting change for people with ADHD in a way that behavioural and medication approaches do not. It is argued that such an intervention can improve oscillatory attention between subjective‐ and objective‐time and increase the capacity to self‐contain emotions. This brings patients into alignment with reality and restarts their psychic development towards themselves, others, the parental couple and grieving, which stalled in infancy. The internalised schema of the 50‐minute therapy session also provides a frame of reference for future tasks.

The unpleasantness of thinking: A meta-analytic review of the association between mental effort and negative affect.

Influential theories in psychology, neuroscience, and economics assume that the exertion of mental effort should feel aversive. Yet, this assumption is usually untested, and it is challenged by casual observations and previous studies. Here, we meta-analyze (a) whether mental effort is generally experienced as aversive and (b) whether the association between mental effort and aversive feelings depends on population and task characteristics. We meta-analyzed a set of 170 studies (from 125 articles published in 2019-2020; 358 different tasks; 4,670 unique subjects). These studies were conducted in a variety of populations (e.g., health care employees, military employees, amateur athletes, college students; data were collected in 29 different countries) and used a variety of tasks (e.g., equipment testing tasks, virtual reality tasks, cognitive performance tasks). Despite this diversity, these studies had one crucial common feature: All used the NASA Task Load Index to examine participants' experiences of effort and negative affect. As expected, we found a strong positive association between mental effort and negative affect. Surprisingly, just one of our 15 moderators had a significant effect (effort felt somewhat less aversive in studies from Asia vs. Europe and North America). Overall, mental effort felt aversive in different types of tasks (e.g., tasks with and without feedback), in different types of populations (e.g., university-educated populations and non-university-educated populations), and on different continents. Supporting theories that conceptualize effort as a cost, we suggest that mental effort is inherently aversive. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PreCNet: Next-Frame Video Prediction Based on Predictive Coding.

Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao and Ballard (1999) into a modern deep learning framework while remaining maximally faithful to the original schema. The resulting network we propose (PreCNet) is tested on a widely used next-frame video prediction benchmark, which consists of images from an urban environment recorded from a car-mounted camera, and achieves state-of-the-art performance. Performance on all measures (MSE, PSNR, and SSIM) was further improved when a larger training set (2M images from BDD100k) pointed to the limitations of the KITTI training set. This work demonstrates that an architecture carefully based on a neuroscience model, without being explicitly tailored to the task at hand, can exhibit exceptional performance.

EXPLORING CONCUSSION RECOVERY THROUGH THE LENS OF THE FREE ENERGY PRINCIPLE AND MARKOV BLANKET THEORY

This paper delves into the application of the Free Energy Principle (FEP) and the concept of the Markov blanket in understanding the neurocognitive implications of concussion. The FEP, a unifying theory in neuroscience, posits that the brain functions to minimize free energy, equating to a reduction in surprise or uncertainty regarding sensory inputs. The Markov blanket, defining the boundary between a system and its environment, is integral to this framework, particularly in understanding how the brain processes and responds to sensory information. We explore how a concussion might disrupt the brain's predictive processing and its ability to minimize free energy, leading to increased prediction errors and cognitive deficits. This disruption is hypothesized to manifest as an inability to accurately predict sensory inputs, resulting in impaired cognitive functions post-concussion. The paper also discusses the potential of neuroplasticity in concussion recovery, framed within the FEP as the brain's effort to re-establish minimized free energy under new constraints. We propose a methodological approach combining neuroimaging and computational modeling to empirically test these hypotheses. This theoretical exploration offers novel insights into the mechanisms underlying concussion-induced cognitive impairments and suggests new avenues for therapeutic intervention and rehabilitation strategies.

Reward prediction error neurons implement an efficient code for reward.

We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.

Excitatory and inhibitory neuronal synapse unit: A novel recurrent cell for time series prediction

Time series prediction is one of the most challenging topics in real-world applications. Most studies undertook advanced mathematics and computer techniques above benchmark methods but often unable to guarantee forecasting models’ generalization and reliability. This paper proposed a biologically motivated recurrent unit based on neuronal synaptic activity mechanism and chaotic behaviors in deep learning domain called Excitatory and Inhibitory Neural Synapse unit (EINS). The major contribution of this research is to create a flexible and robust recurrent unit based on neuroscience theory. It conducted experiments on three real-world time series forecast examples including finance, household power, weather for generalization and robustness evaluation using eight state-of-the-art recurrent architecture units as baseline models to compare convergence rate and forecasting accuracy. Experimental results showed that EINS achieved satisfactory performances in time series prediction reliability and effectiveness.

Video kills the radio star: Copyright and the human versus artificial creativity war

AbstractThis article contributes to the dynamic debate surrounding the intersection of artificial intelligence (AI) and copyright law, offering a fresh perspective that builds upon interdisciplinary analyses. Focusing on the cognitive processes underpinning creativity in both human and AI contexts, the study draws a detailed parallel between Vincent Van Gogh's iconic “Starry Night” and its AI‐generated counterpart generated through DeepDream technology. Central to the investigation is the application of psychological and neuroscientific theories to understand and compare the creative processes in humans and AI. Based on such exercise, the article first examines whether art generated with AI, devoid of human emotions and motivations yet capable of mimicking human creative cognitive processes, qualifies for copyright protection. The analysis suggests that the similarities between human and AI creativity, particularly in their cognitive structuring, could render the work “original” according to different jurisdictional standards and interpretation of copyright law. Second, the article investigates whether AI infringes copyright if protected material is used for its training and processing. This question becomes particularly relevant in light of recent legal actions against AI‐artwork generators in California, which raise issues of potential infringement by AI using latent diffusion techniques on existing artworks. The discussion provides an original perspective that can advance the ongoing debate on the use of copyrighted material for AI training. The paper aims to contribute to the ongoing debate about AI and copyright by challenging the traditional human‐centric view of authorship in copyright law. The article argues for a nuanced understanding that acknowledges the complex nature of creativity, transcending the binary division between human and artificial sources. This approach is critical in redefining legal frameworks, ensuring they are adaptive to the evolving landscape of AI capabilities. At the same time, the article addresses the implications of AI drawing inspiration from existing art, recognizing the need to balance different stakeholders' interests when drawing policy considerations. Ultimately, the goal is to provide a layered perspective that not only deepens the legal discourse but also respects and fosters the coexistence and mutual advancement of both human and artificial creativity in the digital age, in line with the purpose of copyright.

Predictive coding with spiking neurons and feedforward gist signaling.

Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.