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The aim of this paper is to show how teachers apply teaching and learning strategies related to the principles of the nervous system's functions. In our view, understanding what constitutes good teaching is about identifying how it engages the underlying cognitive and neurosystemic processes within the human brain in relation to learning. Using a student self-assessment questionnaire, we have investigated several key processes involved in neurodidactics (excitation, perception, memory, and the use, transfer, and adaptation of information and/or actions). The sample consisted of 884 7–10th grade students. The results showed that students' excitation, understanding, and consolidation of educational material are directly related to the work of the teacher and the teaching strategies they apply to attract and stimulate the student's attention and to help the student to understand and remember information. The learning strategies used by the students reflect the learner's learning activity, i.e., the use and application of strategies that allow internal knowledge to emerge. The consolidation of the learning material and the learning strategies used by the students was statistically significantly higher among the female participants. There are significant differences between low- and high-achieving students in terms of the effectiveness of teaching strategies for consolidation and the learning strategies applied by learners. The paper provides practical recommendations for teachers. [ABSTRACT FROM AUTHOR]

During the socialization process in family and school contexts, children display a wide variety of social behaviors with parents and peers. Yet the developmental trajectory, the predictors and outcomes, and the neural basis of those social behaviors are largely under-investigated. To address these problems, we invited experts in the field to submit their latest findings to tell this story. The current Special Issue is a collection of papers highlighting the complexity for various social behaviors, with a focus on the complex mechanisms that link social behaviors to child socio-emotional adjustment and mediating/moderating factors among the associations. Thirteen papers illustrate empirical work in the field, two papers present new methodological concerns, and one paper that provides a comprehensive review of the literature. [ABSTRACT FROM AUTHOR]

In recent decades, the debate on consciousness has been conditioned by the idea of bottom-up emergence, which has influenced scientific research and raised a few obstacles to any attempt to bridge the explanatory gap. The analysis and explanation of vision conducted according to the accredited methodologies of scientific research in terms of physical stimuli, objectivity, methods, and explanation has encountered the resistance of subjective experience. Moreover, original Gestalt research into vision has generally been merged with cognitive neuroscience. Experimental phenomenology, building on the legacy of Gestalt psychology, has obtained new results in the fields of amodal contours and color stratifications, light perception, figurality, space, so-called perceptual illusions, and subjective space and time. Notwithstanding the outcomes and the impulse given to neuroscientific analyses, the research carried out around these phenomena has never directly confronted the issue of what it means to be conscious or, in other words, the nature of consciousness as self-referentiality. Research has tended to focus on the percept. Therefore, explaining the non-detachability of parts in subjective experience risks becoming a sort of impossible achievement, similar to that of Baron Munchausen, who succeeds in escaping unharmed from this quicksand by pulling himself out by his hair. This paper addresses how to analyze seeing as an undivided whole by discussing several basic dimensions of phenomenal consciousness on an experimental basis and suggesting an alternative way of escaping this quicksand. This mind-set reversal also sheds light on the organization and dependence relationships between phenomenology, psychophysics, and neuroscience. [ABSTRACT FROM AUTHOR]

Background: This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion: This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion: Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced version of our previously developed bio-optical transceiver, presenting a significant advancement in nanosensor technology. Using self-assembled polymers, this nanodevice is capable of electron detection while maintaining biocompatibility, an essential feature for in vivo medical biosensors. This enhancement finds significance in the field of infectious disease control, particularly in the early detection of respiratory viruses, including high-threat pathogens such as SARS-CoV-2. The proposed system harnesses bioluminescence by converting electric signaling to visible blue light, effectively opening the path of linking nano-sized mechanisms to larger-scale systems, thereby pushing the boundaries of in vivo biomedical sensing. The performance evaluation of our technology is analytical and is based on the use of Markov chains, through which we assess the bit error probability. The calculated improvements indicate that this technology qualifies as a forerunner in terms of supporting the communication needs of smaller, safer, and more efficient manufactured sensor technologies for in vivo medical applications. [ABSTRACT FROM AUTHOR]

The study of nervous system architecture and its behavior is an interdisciplinary science related to "Neuroscience." It contains each and every detail related to neuroevolution, molecular and cellular biostructure, anatomy, and pharmacology. To understand this complex structure and architecture of nervous system, a fast, reliable, and advanced technology is required. Object recognition (OR) plays a vital role to understand and adapt the complex system in an easy way. Object recognition provides a computational and cognitive platform to link neuroscience with the human-machine interface for proper interaction in the medical field. OR-computational neuroscience deals with the neural pattern through different models, whereas OR-cognitive science helps to understand the behavior mechanism of neural architecture. The goal of this research is to explain how the brain interprets and processes information using electrical and chemical signals. The paper contains OR-based models in the field of neuroscience. It examines neural representations, neuronal type communication, and neural learning in depth. This paper provides an overview of OR-based cognitive computational neuroscience as well as the models that go with it. A thorough examination of the applications is presented, followed by a discussion of potential future paths. [ABSTRACT FROM AUTHOR]

The present article explores how the concept of metacognition has evolved, from its philosophical origins to current interdisciplinary applications. The paper transversally traces, how metacognitive theories have been integrated by various fields. First of all, is emphasized the progressive character of the development of the metacognitive theory; second, the features that establish it as an independent theory are highlighted. Finally, the article explores current and future concerns, trends and directions in research. Thus, the following discourse exploratively investigates how metacognition with its ancient history on ''thinking about thinking'', have been innovated by various current scientific fields. [ABSTRACT FROM AUTHOR]

The study of the human psyche has elucidated a bipartite structure of logic reflecting the quantum–classical nature of the world. Accordingly, we posited an approach toward studying the brain by means of the quantum–classical dynamics of a mixed Weyl symbol. The mixed Weyl symbol can be used to describe brain processes at the microscopic level and, when averaged over an appropriate ensemble, can provide a link to the results of measurements made at the meso and macro scale. Within this approach, quantum variables (such as, for example, nuclear and electron spins, dipole momenta of particles or molecules, tunneling degrees of freedom, and so on) can be represented by spinors, whereas the electromagnetic fields and phonon modes can be treated either classically or semi-classically in phase space by also considering quantum zero-point fluctuations. Quantum zero-point effects can be incorporated into numerical simulations by controlling the temperature of each field mode via coupling to a dedicated Nosé–Hoover chain thermostat. The temperature of each thermostat was chosen in order to reproduce quantum statistics in the canonical ensemble. In this first paper, we introduce a general quantum–classical Hamiltonian model that can be tailored to study physical processes at the interface between the quantum and the classical world in the brain. While the approach is discussed in detail, numerical calculations are not reported in the present paper, but they are planned for future work. Our theory of brain dynamics subsumes some compatible aspects of three well-known quantum approaches to brain dynamics, namely the electromagnetic field theory approach, the orchestrated objective reduction theory, and the dissipative quantum model of the brain. All three models are reviewed. [ABSTRACT FROM AUTHOR]

Symmetry belongs to one of the basic principles of the beauty of human and non-human objects since antiquity. Even though its significance has been verified by numerous theories and research studies, there is a number of papers suggesting that this principle may be false. The study identifies five major evolutionary principles, in the context of new approaches and research ambiguities based mainly on neuroscience and evolutionary psychology, that support the thesis that highlights the significance of symmetry in the perception and assessment of attractiveness: 1. symmetry as an honest signal of various health characteristics; 2. symmetry as proof of developmental stability; 3. effectiveness; 4. comprehensibility; 5. predictability. In the context of the mechanisms described above it also seeks possible explanations for the existence of contradictory research results related to the attractiveness of symmetry. The outcome of the study is the postulation of three hypotheses: 1. the naturalness hypothesis (symmetry is only attractive to the same degree that it naturally occurs in the subject); 2. the accent hypothesis (minor asymmetries do not disprove the principles of symmetry, they make them more visible); 3. the ecology hypothesis (the attractiveness of symmetry is conditioned by the situation and depends on the type of subject assessed) that allow us to integrate both past and contemporary (and putatively contradictory) research findings. The paper also provides proposals for the verification of the postulated hypotheses. [ABSTRACT FROM AUTHOR]

The paper concentrates on the solitary waves that are retrievable from the generalized Boussinesq equation. The numerical simulations are displayed in the paper that gives a visual perspective to the model studied in neurosciences. The Laplace–Adomian decomposition scheme makes this visualization of the solitons possible. The numerical simulations are being reported for the first time using an elegant approach. The results would be helpful for neuroscientists and clinical studies in Medicine. The novelty lies in the modeling that is successfully conducted with an impressively small error measure. In the past, the model was integrated analytically only to recover soliton solutions and its conserved quantities. [ABSTRACT FROM AUTHOR]

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background. [ABSTRACT FROM AUTHOR]

This paper presents arguments and evidence from psychology and neuroscience supporting Lipton's 2004 claim that scientists create knowledge through an abductive process that he calls "Inference to the Best Explanation". The paper develops two conclusions. Conclusion 1 is that without conscious effort on our part, our brains use a process very similar to abduction as a powerful way of interpreting sensory information. To support Conclusion 1, evidence from psychology and neuroscience is presented that suggests that what we humans perceive through our senses is not reality, but rather, our 'brain's "best guess" of the causes of its sensory input. The implication of this best guessing is that our brains use a process very similar to abduction throughout our lives to inform us of what is happening in the world around us. In addition, an argument based on Darwinian evolution is presented claiming that our brains do an excellent job of interpreting sensory information from the outside world. (If they did not, we, as a species, could hardly have survived.) Combining these two claims leads to Conclusion 1. Building on Conclusion 1, Conclusion 2 is that Lipton and others are correct in claiming that scientists use abduction when creating scientific theories. Abduction must be strong, because Nature chose abduction for its own sensemaking purposes. This paper's contribution to knowledge is in pointing out that recent psychological and neuroscientific research has major implications for the philosophical world's confidence in the probable validity of abductive inference. The punchline is simple: Nature chose abduction! [ABSTRACT FROM AUTHOR]

Background: Circadian rhythms are important for all aspects of biology; virtually every aspect of biological function varies according to time of day. Although this is well known, variation across the day is also often ignored in the design and reporting of research. For this review, we analyzed the top 50 cited papers across 10 major domains of the biological sciences in the calendar year 2015. We repeated this analysis for the year 2019, hypothesizing that the awarding of a Nobel Prize in 2017 for achievements in the field of circadian biology would highlight the importance of circadian rhythms for scientists across many disciplines, and improve time-of-day reporting. Results: Our analyses of these 1000 empirical papers, however, revealed that most failed to include sufficient temporal details when describing experimental methods and that few systematic differences in time-of-day reporting existed between 2015 and 2019. Overall, only 6.1% of reports included time-of-day information about experimental measures and manipulations sufficient to permit replication. Conclusions: Circadian rhythms are a defining feature of biological systems, and knowing when in the circadian day these systems are evaluated is fundamentally important information. Failing to account for time of day hampers reproducibility across laboratories, complicates interpretation of results, and reduces the value of data based predominantly on nocturnal animals when extrapolating to diurnal humans. [ABSTRACT FROM AUTHOR]

This paper explores the connections between neurology andforesight in a cross-disciplinary discussion of how humans are able to imagine possible futures. Episodic foresight is identified as the primary neural mechanism that enables humans to construct images of the futures, providing explanatory power to further define and understand the processes that are invoked in futures studies andforesight activities. A top-levelframework for making explicit these processes is provided, identifying brain functions, openness to experience and temporal preferences as the primary processes that inform construction of new images. The paper provides a new perspective on the design of Futures Studies and Foresight processes and deepens our understanding of the nature of those processes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders. [ABSTRACT FROM AUTHOR]

This umbrella review aimed to systematically identify the peri-operative risk factors associated with post-operative cognitive dysfunction (POCD) using meta-analyses of observational studies. To date, no review has synthesised nor assessed the strength of the available evidence examining risk factors for POCD. Database searches from journal inception to December 2022 consisted of systematic reviews with meta-analyses that included observational studies examining pre-, intra- and post-operative risk factors for POCD. A total of 330 papers were initially screened. Eleven meta-analyses were included in this umbrella review, which consisted of 73 risk factors in a total population of 67,622 participants. Most pertained to pre-operative risk factors (74%) that were predominantly examined using prospective designs and in cardiac-related surgeries (71%). Overall, 31 of the 73 factors (42%) were associated with a higher risk of POCD. However, there was no convincing (class I) or highly suggestive (class II) evidence for associations between risk factors and POCD, and suggestive evidence (class III) was limited to two risk factors (pre-operative age and pre-operative diabetes). Given that the overall strength of the evidence is limited, further large-scale studies that examine risk factors across various surgery types are recommended. [ABSTRACT FROM AUTHOR]

This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke. [ABSTRACT FROM AUTHOR]

Deep learning is a popular topic among machine learning researchers nowadays, with great strides being made in recent years to develop robust artificial neural networks for faster convergence to a reasonable accuracy. Network architecture and hyperparameters of the model are fundamental aspects of model convergence. One such important parameter is the initial values of weights, also known as weight initialization. In this paper, we perform two research tasks concerned with the weights of neural networks. First, we develop three novel weight initialization algorithms inspired by the neuroscientific construction of the mammalian brains and then test them on benchmark datasets against other algorithms to compare and assess their performance. We call these algorithms the lognormal weight initialization, modified lognormal weight initialization, and skewed weight initialization. We observe from our results that these initialization algorithms provide state-of-the-art results on all of the benchmark datasets. Second, we analyze the influence of training an artificial neural network on its weight distribution by measuring the correlation between the quantitative metrics of skewness and kurtosis against the model accuracy using linear regression for different weight initializations. Results indicate a positive correlation between network accuracy and skewness of the weight distribution but no affirmative relation between accuracy and kurtosis. This analysis provides further insight into understanding the inner mechanism of neural network training using the shape of weight distribution. Overall, the works in this paper are the first of their kind in incorporating neuroscientific knowledge into the domain of artificial neural network weights. [ABSTRACT FROM AUTHOR]

This paper analyses new fact-finding methods in criminal proceedings, using state-of-the-art innovations in neuroscience and artificial intelligence (AI). It will outline the existing methods and explain their effects. Then it will address the criminal-law aspects of the use of such methods as evidence in criminal proceedings, with an emphasis on the assessment of their admissibility from the perspective of the right to a fair trial and the privilege against self-incrimination. This topic will be observed from the perspective of US and European law, highlighting the existing jurisprudence of the European Court of Human Rights (ECtHR) and the legal standards established by the court in relation to the privilege against self-incrimination. Based on this analysis, the authors will formulate a conclusion suggesting that the use of current AI technologies should be juxtaposed to the relevant benchmark of the privilege against self-incrimination as the requisite standard of the right to a fair trial. [ABSTRACT FROM AUTHOR]

From the very beginning of their life, human beings are immersed in a social and interactive environment that contributes to shaping their social and cognitive development under typical and at-risk conditions. In order to understand human development in its bidirectional relationship with the social environment, we need to develop a 'complexity-sensitive' approach in neuroscience. Recent advances have started to do so with the application of hyperscanning techniques which involve recording adult and child neural activity simultaneously and highlighting the presence of similar patterns of brain activity in the dyad. Numerous studies focused on typically developing children have been published in recent years with the application of this technique to different fields of developmental research. However, hyperscanning techniques could also be extremely beneficial and effective in studying development in atypical and clinical populations. Such application, namely translational hyperscanning, should foster the transition toward a two-brain translational neuroscience. In this paper, we envision how the application of hyperscanning to atypical and clinical child populations can inform family-centered care for children and their parents. [ABSTRACT FROM AUTHOR]

"Emergence", the phenomenon where a complex system displays properties, behaviours, or dynamics not trivially reducible to its constituent elements, is one of the defining properties of complex systems. Recently, there has been a concerted effort to formally define emergence using the mathematical framework of information theory, which proposes that emergence can be understood in terms of how the states of wholes and parts collectively disclose information about the system's collective future. In this paper, we show how a common, foundational component of information-theoretic approaches to emergence implies an inherent instability to emergent properties, which we call flickering emergence. A system may, on average, display a meaningful emergent property (be it an informative coarse-graining, or higher-order synergy), but for particular configurations, that emergent property falls apart and becomes misinformative. We show existence proofs that flickering emergence occurs in two different frameworks (one based on coarse-graining and another based on multivariate information decomposition) and argue that any approach based on temporal mutual information will display it. Finally, we argue that flickering emergence should not be a disqualifying property of any model of emergence, but that it should be accounted for when attempting to theorize about how emergence relates to practical models of the natural world. [ABSTRACT FROM AUTHOR]

Neuroimaging experiments in general, and EEG experiments in particular, must take care to avoid confounds. A recent TPAMI paper uses data that suffers from a serious previously reported confound. We demonstrate that their new model and analysis methods do not remedy this confound, and therefore that their claims of high accuracy and neuroscience relevance are invalid. [ABSTRACT FROM AUTHOR]

After decades of being ignored, the concept of beauty, as understood by the non-architect, has recently been making a comeback in architecture, not so much in the practice itself, as in appeals for design solutions that are more human-centered and not dictated by abstract principles. Architectural beauty needs to be evaluated from its effects on human health. This study discusses two diagnostic tools for measuring the degree of architectural "beauty" and presents the results of the pilot application of one of them. The goal is to use diagnostic imaging for evaluations. Analytical elements are introduced from disciplines with which practitioners are normally not familiar, such as artificial intelligence, medicine, neuroscience, visual attention and image-processing software, etc. In addition to the diagnostic tools, this paper ties related ideas on objective beauty into a novel synthesis. These results support the idea of a feasible, "objective" way to evaluate what the users will consider as beautiful, and set the stage for an upcoming larger study that will quantitatively correlate the two methods. [ABSTRACT FROM AUTHOR]

This article addresses missional spirituality, that is, what spirituality, individual and corporate, is appropriate and fruitful in churches which seek to participate in the mission of God. The paper engages with neuroscience around how the right and left hemispheres of the brain offer two ways of attending to the world; Iain McGilchrist asserts in The Master and his Emissary that left brain attention has predominated especially in Modernity. Two Christian responses which utilize these truths about the brain are critically examined, one from an American, broadly Evangelical school of thought and the other from the Anglican Solitary, Maggie Ross. Ross’ recovery of the apophatic stream of Christian spirituality via the right brain of “Deep Mind” is preferred without dismissing other approaches. The article delineates the “unintended consequences” of seeking silence before God who is, including the gift of “beholding” and how they connect to what is required of missional churches. Some brief suggestions are made as to what might be done to enhance the practice of silence in local churches. [ABSTRACT FROM AUTHOR]

Despite the recent success of neural network models in mimicking animal performance on various tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system requires us to characterize its parts, organization, and activities, and how those give rise to behaviors of interest. However, it remains controversial what it takes for a model to be mechanistic, and whether computational models such as neural networks qualify as explanatory on this approach. We argue that certain kinds of neural network models are actually good examples of mechanistic models, when an appropriate notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify an abstract level of description that is still detailed enough to be "runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals. Perhaps surprisingly, the abstractions required are just those already in use in experimental neuroscience and deployed in the construction of more familiar computational models — just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals. In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are. [ABSTRACT FROM AUTHOR]

There is a noticeable gap between results of cognitive neuroscientific research into basic mathematical abilities and philosophical and empirical investigations of mathematics as a distinct intellectual activity. The paper explores the relevance of a Wittgensteinian framework for dealing with this discrepancy. [ABSTRACT FROM AUTHOR]

This paper explores a series of thoughts about the meaning of intelligence in neuroscience and computer science. This work aims to present an understandable definition that fits our contemporary artificial intelligence background. The research methodology of this essay lies in existing theories of artificial intelligence, focused on computer science and neuroscience. I analyze the relationship between intelligence and neuroscience and Hawkin's Thousand Brains Theory, an approach to show what it is an intelligent agent according to neuroscience. Here, the main result relies on the verification that intelligence is only possible in the neocortex. According to this result, the study performs a second critical analysis aiming to demonstrate why there is no artificial intelligence today. [ABSTRACT FROM AUTHOR]

Recent advances in neuro-imaging enable scientists to create brain network data that can lead to novel insights into neurocircuitry, and a better understanding of the brain's organization. These networks inherently involve a spatial component, depicting which brain regions are structurally, functionally or genetically related. Their visualization in 3D suffers from occlusion and clutter, especially with increasing number of nodes and connections, while 2D representations such as connectograms, connectivity matrices, and node-link diagrams neglect the spatio-anatomical context. Approaches to arrange 2D-graphs manually are tedious, species-dependent, and require the knowledge of domain experts. In this paper, we present a spatial-data-driven approach for layouting 3D brain networks in 2D node-link diagrams, while maintaining their spatial organization. The produced graphs do not need manual positioning of nodes, are consistent (even for sub-graphs), and provide a perspective-dependent arrangement for orientation. Furthermore, we provide a visual design for highlighting anatomical context, including the shape of the brain, and the size of brain regions. We present in several case-studies the applicability of our approach for different neuroscience-relevant species, including the mouse, human, and Drosophila larvae. In a user study conducted with several domain experts, we demonstrate its relevance and validity, as well as its potential for neuroscientific publications, presentations, and education. [Display omitted] • A novel method for generating Spatial-Data-Driven Layouts for neural networks of multiple species and perspectives. • No need to manually define brain region related constraints to generate anatomically feasible layouts. • Visual designs providing a consistent spatial context to the user to ease orientation and visual comparison of different brain networks. [ABSTRACT FROM AUTHOR]

Despite rapid advances in sensor development and technological miniaturization, it remains challenging to non-invasively record small-amplitude electrophysiological signals from an animal in its natural environment. Many advances in ecophysiology and biologging have arisen through sleep studies, which rely on detecting small signals over multiple days and minimal disruption of natural animal behavior. This paper describes the development of a surface-mounted system that has allowed novel electrophysiological recordings of sleep in wild marine mammals. We discuss our iterative design process by providing sensor-comparison data, detailed technical illustrations, and material recommendations. We describe the system's performance over multiple days in 12 freely moving northern elephant seals (Mirounga angustirostris) sleeping on land and in water in captivity and the wild. We leverage advances in signal processing by applying independent components analysis and inertial motion sensor calibrations to maximize signal quality across large (> 10 gigabyte), multi-day datasets. Our study adds to the suite of biologging tools available to scientists seeking to understand the physiology and behavior of wild animals in the context in which they evolved. [ABSTRACT FROM AUTHOR]

Motor learning generates synaptic neural connections that favour the motor environment and, also, various processes where our cognitive and executive functions intervene. Therefore, it is essential to know the different contributions that come from neuroscience linked to motor learning in a child. This study aimed to determine the fundamental contributions of neuroscience to motor learning in children. The methodology included a qualitative systematic review in the PubMed, Medline and Scopus databases. Of 479 related documents, 24 papers achieved the inclusion criteria (the learning mechanisms of motor skills and the different approaches to achieving meaningful learning). They were selected using the data collection methodology indicated by PRISMA®. The main results indicated that learning occurs based on experiences (cognitive, perceptual, motor, linguistic, neuronal, organic and cultural) and requires processes of adaptation, stabilization, and maturation of brain synchronization of vestibular, perceptual and visual processes. Children who receive motor intervention improve sustained attention, working memory, problem-solving and planning capacity. Motor and cognitive development are favoured by instructions as an essential tool. The implicit instructions present a higher benefit for children with lower motor skills. [ABSTRACT FROM AUTHOR]

Prof B Ramamurthi was a pioneer of Indian neurosurgery and a major force in the development of Indian neuroscience. Founding the Madras Institute of Neurology and later the A Lakshmipathi Neurosurgical Centre (ALNC), both at Madras (or Chennai as it is now called), he developed centres of excellence in his career that spanned over five decades. During this period of time he made Madras, a destination for neurosurgery and neuroscience. Along with his colleagues a large number of publications were produced which influenced the world. Notable among his contributions were those in Stereotaxy for movement disorders, epilepsy, pain and psychiatric illness. He also had notable contributions in brain tumours especially acoustic neurinomas and pituitary tumours. His papers on the low incidence of aneurysms is still quoted widely. Head injuries formed a major part of the neurosurgical work and major contributions were made in that field too. As a developing country with socio-economic issues, infections of the nervous system were seen commonly. His publications on tuberculomas of the brain are noteworthy. He was intrigued by the neurophysiological basis of consciousness. He writings on the subject reflect his attempt to bring together ancient eastern thoughts and concepts of consciousness and life and western science. In the later part of his career he spoke on ethics in and the changing milieu of neurosurgery. While contributions to spinal surgery were not seen in the first half of his career, he along with his colleagues from ALNC published original articles on spinal surgery especially tumours and OPLL. Prof B Ramamurthi, has not only influenced, taught and mentored, during his lifetime, a great many neuroscientists, but he also continues to do so through his publications which continue to be relevant in todays world. A glimpse into his contributions show us how without the technology of today a lot was achieved - and we need to see that, to inspire us to achieve more and to strive for greater heights. [ABSTRACT FROM AUTHOR]

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications. • The most groundbreaking advances in theoretical and applied Artificial Intelligence. • Deep Learning in real-world tasks, such as clinical diagnostics or robotics. • Several applications are presented, reviewed and discussed. • State-of-the-art in AI methods, models and applications. • New scientific discoveries successfully transferred to real-life applications. [ABSTRACT FROM AUTHOR]

The most fundamental emotional systems that show trait control are evolutionarily old and extensively conserved. Psychology in general has benefited from non-human neuroscience and from the analytical simplicity of behaviour in those with simpler nervous systems. It has been argued that integration between personality, psychopathology, and neuroscience is particularly promising if we are to understand the neurobiology of human experience. Here, we provide some general arguments for a non-human approach being at least as productive in relation to personality, psychopathology, and their interface. Some early personality theories were directly linked to psychopathology (e.g., Eysenck, Panksepp, and Cloninger). They shared a common interest in brain systems that naturally led to the use of non-human data; behavioural, neural, and pharmacological. In Eysenck's case, this also led to the selective breeding, at the Maudsley Institute, of emotionally reactive and non-reactive strains of rat as models of trait neuroticism or trait emotionality. Dimensional personality research and categorical approaches to clinical disorder then drifted apart from each other, from neuropsychology, and from non-human data. Recently, the conceptualizations of both healthy personality and psychopathology have moved towards a common hierarchical trait perspective. Indeed, the proposed two sets of trait dimensions appear similar and may even be eventually the same. We provide, here, an introduction to this special issue of Personality Neuroscience , where the authors provide overviews of detailed areas where non-human data inform human personality and its psychopathology or provide explicit models for translation to human neuroscience. Once all the papers in the issue have appeared, we will also provide a concluding summary of them. [ABSTRACT FROM AUTHOR]

Continuous evolution in neuroscience and neurotechnology underlines the need for continuous updating of knowledge, with significant implications for individuals, society and healthcare professionals. Braincomputer interface (BCI) is one of these emerging technologies, facilitating direct communication between the brain and external devices, avoiding the usual neuromuscular pathways. Brain-computer interface CI is not limited to the medical field, but has extensive applications in various aspects of human life, including entertainment, gaming, education, self-control, and marketing. However, along with its benefits, BCI also presents challenges, including technological, neurological, and ethical ones. This abstract summarizes the importance and diversity of BCIs' use in human-machine interaction, highlighting their implications in various fields and highlighting the challenges associated with this promising technology. In the paper, we used EEG (Electroencephalogram) as the primary method of recording brain electrical activity in real time. EEG is a non-invasive technology that uses electrodes placed on the scalp to detect and record electrical signals emitted by neural activity in the brain. To use the EEG in the context of BCI headphones for controlling people with disabilities, we selected a standard set of electrodes and applied a special conductive gel to ensure good conductivity of electrical signals. We took care to position the electrodes at strategic points of the scalp according to the international electroencephalography system (10-20) to record signals from cortical regions relevant for motor control and communication. We developed testing protocols and experimental paradigms that involved participants performing specific tasks, such as controlling a cursor on a screen or selecting mental commands, while recording their brain's EEG activity. We used an EEG amplifier to amplify and filter the recorded signals and specialized software to analyzeand interpret EEG data to detect specific patterns and signals that indicate movement intentions or mental commands. The results obtained from the EEG recording were used to train algorithms to decode participants' intentions and calibrate the BCI headphones so that they correctly transmit the commands generated by EEG signals. By using EEG in this work, we were able to provide an efficient and accurate interface between brain activity and BCI headphones, allowing participants to control devices with great accuracy and reliability. [ABSTRACT FROM AUTHOR]

Bereavement is a common human experience that often involves significant impacts on psychological, emotional and even cognitive functioning. Though various psychological theories have been proposed to conceptualize the grief process, our current understanding of the underlying neurocognitive mechanisms of grief is limited. The present paper proposes a neurocognitive model to understand phenomena in typical grief, which links loss-related reactions to underlying learning and executive processes. We posit that the competitive relationship between the basal ganglia (BG) and circuitry involving the medial temporal lobe (MTL) underlies common cognitive experiences in grief such as a sense of "brain fog." Due to the intense stressor of bereavement, we suggest that these two systems' usually flexible interactive relationship become imbalanced. The resulting temporary dominance of either the BG or the MTL system is then manifested in perceived cognitive changes. Understanding the underlying neurocognitive mechanism in grief could inform ways to best support bereaved individuals. • Bereavement has significant impacts on psychological, emotional, and cognitive functioning, however, our current understanding of the underlying neurocognitive mechanisms of grief is limited. • A neurocognitive model, which links loss-related reactions to underlying learning and executive processes is proposed to understand phenomena in typical grief. • The basal ganglia and circuitry involving the medial temporal lobe underlie common cognitive experiences in grief. • The competitive relationship between these two systems becomes imbalanced due to the intense stressor of bereavement. • Understanding the underlying neurocognitive mechanism in grief could inform ways to best support bereaved individuals. [ABSTRACT FROM AUTHOR]

As the number of EEG papers increases, so too do the number of guidelines for how to report what has been done. However, current guidelines and checklists appear to have limited adoption, as systematic reviews have shown the journal article format is highly prone to errors, ambiguities and omissions of methodological details. This is a problem for transparency in the scientific record, along with reproducibility and metascience. Following lessons learned in the high complexity fields of aviation and surgery, we conclude that new tools are needed to overcome the limitations of written methodology descriptions, and that these tools should be developed through community consultation to ensure that they have the most utility for EEG stakeholders. As a first step in tool development, we present the ARTEM-IS Statement describing what action will be needed to create an Agreed Reporting Template for Electroencephalography Methodology - International Standard (ARTEM-IS), along with ARTEM-IS Design Guidelines for developing tools that use an evidence-based approach to error reduction. We first launched the statement at the LiveMEEG conference in 2020 along with a draft of an ARTEM-IS template for public consultation. Members of the EEG community are invited to join this collective effort to create evidence-based tools that will help make the process of reporting methodology intuitive to complete and foolproof by design. [Display omitted] [ABSTRACT FROM AUTHOR]