Advanced Cognition Research Articles

Metal-organic framework single crystal for in-memory neuromorphic computing with a light control

Neuromorphic architectures, expanding the limits of computing from conventional data processing and storage to advanced cognition, learning, and in-memory computing, impose restrictions on materials that should operate fast, energy efficiently, and highly endurant. Here we report on in-memory computing architecture based on metal-organic framework (MOF) single crystal with a light control. We demonstrate that the MOF with inherent memristive behavior (for data storage) changes nonlinearly its electric response when irradiated by light. This leads to three and more electronic states (spikes) with 81 ms duration and 1 s refractory time, allowing to implement 40 bits s−1 optoelectronic data processing. Next, the architecture is switched to the neuromorphic state upon the action of a set of laser pulses, providing the text recognition over 50 times with app. 100% accuracy. Thereby, simultaneous data storage, processing, and neuromorphic computing on MOF, driven by light, pave the way for multifunctional in-memory computing architectures.

A mathematical theory of relational generalization in transitive inference

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

Ski-orienteering: a scientific perspective on a multi-dimensional challenge

Ski-orienteering, which combines cross-country skiing with orienteering, dates back to the late 1800s, with the first World Championships in 1975. While researchers have explored the physiological and biomechanical determinants of success in cross-country skiing and orienteering separately in detail, scientific knowledge concerning ski-orienteering remains limited. Based on the information that is presently available together with interviews with elite ski-orienteers, we explore here for the first time the historical development, physiological, biomechanical, and psychological demands, certain training strategies, and future prospects and challenges associated with this sport, including its potential to become an Olympic event. A demanding endurance sport (with racing times of 12–120 min), ski-orienteering requires both considerable aerobic and anaerobic capacity, as well as well-trained upper and lower body muscles. In addition, ski-orienteering demands advanced skiing technique on various types of terrain, with frequent changes between sub-techniques, on both wide and narrow tracks and with numerous turns on downhill terrain. Moreover, success in this sport requires accurate and rapid orienteering—the ability to navigate a complex network of ski tracks with numerous intersections/crossings in a manner designed to pass the multiple control points in the order indicated on the map as rapidly as possible, i.e., advanced spatial cognition and highly developed navigational skills. Thus, ski-orienteering requires training designed to improve both relevant physiological characteristics and orienteering skills, which should become the focus of future interdisciplinary research on this complex sport.

Utilizing Artificial Intelligence Application for Diagnosis of Oral Lesions and Assisting Young Oral Histopathologist in Deriving Diagnosis from Provided Features - A Pilot study.

AI in healthcare services is advancing every day, with a focus on uprising cognitive capabilities. Higher cognitive functions in AI entail performing intricate processes like decision-making, problem-solving, perception, and reasoning. This advanced cognition surpasses basic data handling, encompassing skills to grasp ideas, understand and apply information contextually, and derive novel insights from previous experiences and acquired knowledge. ChatGPT, a natural language processing model, exemplifies this evolution by engaging in conversations with humans, furnishing responses to inquiries. We aimed to understand the capability of ChatGPT in solving doubts pertaining to symptoms and histological features related to subject of oral pathology. The study's objective is to evaluate ChatGPT's effectiveness in answering questions pertaining to diagnoses. This cross-sectional study was done using an AI-based ChatGPT application that provides free service for research and learning purposes. The current version of ChatGPT3.5 was used to obtain responses for a total of 25 queries. These randomly asked questions were based on basic queries from patient aspect and early oral histopathologists. These responses were obtained and stored for further processing. The responses were evaluated by five experienced pathologists on a four point liekart scale. The score were further subjected for deducing kappa values for reliability. A total of 25 queries were solved by the program in the shortest possible time for an answer. The sensitivity and specificity of the methods and the responses were represented using frequency and percentages. Both the responses were analysed and were statistically significant based on the measurement of kappa values. The proficiency of ChatGPT in handling intricate reasoning queries within pathology demonstrated a noteworthy level of relational accuracy. Consequently, its text output created coherent links between elements, producing meaningful responses. This suggests that scholars or students can rely on this program to address reasoning-based inquiries. Nevertheless, considering the continual advancements in the program's development, further research is essential to determine its accuracy levels in future versions.

RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills.

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.

Generative AI and neural networks towards advanced robot cognition

Enhancing autonomy and applicability of robotic systems across diverse scenarios, requires efficient environment perception. Conventional vision systems are highly performing but limited to simple tasks, while AI based ones require extensive data collection, processing and training. This paper presents a framework leveraging generative AI and Neural Networks to implement a dynamically updateable perception system. A multimodal conditional Generative Adversarial Network generates large image datasets which are automatically annotated by a Large Multimodal Model. A Convolutional Neural Network performs further dataset augmentation. A case study on the inspection of aircraft fuel tanks is used to showcase the potential of the approach.

Bionic-inspired oil price prediction: Auditory multi-feature collaboration network

Predictions of the oil price are critical to support intelligent decision-making for individual investors, governments, and corporations, but a challenging task since there are complex nonlinear and random fluctuations involved in series. Additionally, most existing methods mainly focus on the data-layer fitting, and inherit uninterpretable design flowcharts and unclear layer functions. Based on the fact that living things evolve numerous advanced cognition processes after long-term interactions with environments, it is a promising problem to design bionic deep prediction networks. In this paper, an auditory-inspired multi-feature collaboration network (AMFC-Net) is explored to predict the oil price, and includes the feature extraction, brain memory, and comprehensive prediction blocks. Specifically, through imitating that complex sounds are captured by auditory canals and converted into electrical impulses, the first block adopts four typical activation functions to extract multi-group complementary features for detecting nonlinear changes. Through imitating that electrical impulses are transformed to the left and right hemispheres for handling and analyzing, the second block realizes gating and cooperation mechanisms to learn long short-term dependencies and highlight core information. Through imitating that information is sent to higher cerebral cortices for sensing the environment, the third block maps relationships from features to targets for producing final predictions. In summary, multi-channel convolution operations establish the imitations of both form (clear functional layer) and spirit (layer-by-layer collaboration), which not only ensure the prediction effectiveness but also improve the AMFC-Net interpretability. Three experiments (comparison with 12 machine learning methods), three experiments (comparison with 8 hybrid methods under decomposition plus ensemble framework), and seven discussions under two real-world oil price datasets all indicate that the proposed AMFC-Net is superior and suitable to predict the oil price.

Periodontitis is associated with altered brain structure and function in normal cognition middle-aged and elderly individuals.

Numerous studies have proposed that periodontitis is a potential risk factor for Alzheimer's disease. However, the association between periodontitis and brain normal cognition in aged and elderly individuals (NCs) is unclear. Such a link could provide clues to Alzheimer's disease development and strategies for early prevention. To explore the associations between periodontal condition and metrics of both brain structure and function among NCs with the help of multimodal magnetic resonance imaging (MRI). High-resolution T1-weighted structural data, resting-state functional-MRI data, and measures of periodontal condition were collected from 40 NCs. Cortical volume, thickness, and area as well as regional homogeneity were calculated with the aid of DPABISurf software. Correlation analyses were then conducted between each imaging metric and periodontal index. Consistent negative correlations were observed between severity of periodontitis (mild, moderate, severe) and cortical volume, area, and thickness, not only in brain regions that took charge of primary function but also in brain regions associated with advanced cognition behavior. Among participants with mild attachment loss (AL) and a shallow periodontal pocket depth (PPD), periodontal index was positively correlated with most measures of brain structure and function, while among participants with severe AL and deep PPD, periodontal index was negatively correlated with measures of brain structure and function (all p < .005 for each hemisphere). Our results demonstrate that periodontitis is associated with widespread changes in brain structure and function among middle-aged and elderly adults without signs of cognitive decline, which might be a potential risk factor for brain damage.

Incredible Clinical Causes beyond a Five-Month-Old Boy's Poor Feeding Include His Rapid Brain Cognition and Preference for Certain Colors As Well As His Early Experience with Those Colors: Case Report

The study's abstract highlights the numerous clinical factors that contribute to a five-month-old boy's malnutrition in addition to inadequate feeding. These factors include color choice, early color vision, and mature brain cognition. Infants normally develop their sense of perception during their first year of life. This article emphasizes how a five-month-old baby can recognize colors, in particular red and blue ones. I also go through how, in very rare instances, the early development of color perception can have a clinical effect on feeding. This essay questions the widely held belief that babies under six months old can only recognize the colors white and black. This article presents the clinical databased evidence that a child can distinguish between the colors red and blue at the age of five months and how, due to his abnormally advanced brain cognition and color perception development, he may determine for himself which color to choose. This article looks at how infants' perceptions of color are unrestricted in terms of knowing which color they choose to feel at ease with during feeding. This clinical example can help you better understand how children learn to recognize and perceive color. This article paves the way for future clinical studies on color perception and the early brain development of cognition.

Assessing cognitive workloads of assembly workers during multi-task switching

Complex assembly tasks with multiple manual operations and steps often require rapid judgment and action under time pressure and cause most human-related errors. The task switching and action transitions are major sources of these errors. This study intends to implement an electroencephalography (EEG) approach to quantitatively evaluate the mental workload during task switching and transition. The time–frequency and spectrum analysis were utilized to compute and reflect the task demand between the intervals of individual tasks. This study developed an experiment to validate the proposed assessment approach and benchmark the results with the National Aeronautics and Space Administration task load index (NASA-TLX) subjective evaluation scale analysis. The results show that the average value of the power spectral densities (PSDs) of the gamma band signal of the AF4 channel and the beta band signal of Channel F3 show distinctive signal patterns among task stages and intervals. During the interval between the idling stage and the part selection stage, the peak of the PSD envelope increased from 18 to 27 Hz, suggesting advanced cognition increases the mental workload of the interval between different tasks. Therefore, the task switching period cannot be regarded as rest and need to be optimized with better task organization.

Research on Emotion Recognition Method of Flight Training Based on Multimodal Fusion

The emotional activities of the human body are mainly regulated by the autonomic nervous system, the central nervous system, and the advanced cognition of the human brain. This paper proposes an emotional state recognition method in pilot training tasks based on multimodal information fusion. A set of emotion perception recognition systems, a two-dimensional valence-arousal emotional model, and a multimodal information intelligent perception model were established based on the human-computer interaction mode during flight training; an intelligent perception system was designed to collect and intelligently perceive four kinds of peripheral physiological signals of pilots in real-time. And based on traditional machine learning models, a binary tree support vector machine was designed to optimize and improve the multimodal information co-integration decision model, which increased the accuracy of emotional state recognition in flight training by 37.58% on average. The experimental result showed that it realizes accurate monitoring and identification of real-time emotional state, helps to improve the effect of flight training and flight safety, maintains the efficiency of operation, and has important research significance and application prospects in the field of pilot training.

The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain.

The primate brain has unique anatomical characteristics, which translate into advanced cognitive, sensory, and motor abilities. Thus, it is important that we gain insight on its structure to provide a solid basis for models that will clarify function. Here, we report on the implementation and features of the Brain/MINDS Marmoset Connectivity Resource (BMCR), a new open-access platform that provides access to high-resolution anterograde neuronal tracer data in the marmoset brain, integrated to retrograde tracer and tractography data. Unlike other existing image explorers, the BMCR allows visualization of data from different individuals and modalities in a common reference space. This feature, allied to an unprecedented high resolution, enables analyses of features such as reciprocity, directionality, and spatial segregation of connections. The present release of the BMCR focuses on the prefrontal cortex (PFC), a uniquely developed region of the primate brain that is linked to advanced cognition, including the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset. Moreover, the inclusion of tractography data from diffusion MRI allows systematic analyses of this noninvasive modality against gold-standard cellular connectivity data, enabling detection of false positives and negatives, which provide a basis for future development of tractography. This paper introduces the BMCR image preprocessing pipeline and resources, which include new tools for exploring and reviewing the data.

Changing teacher educator cognition within a collaborative teacher education programme for CLIL: A case study in China

This study reports on a six-month collaborative teacher education programme designed to support the implementation of Content and Language Integrated Learning (CLIL) in the Chinese primary education context. ‘Activity theory’ was used as an analytical framework to investigate the changes in cognition regarding CLIL experienced by two teacher educators – one at district level and the other at school level – and the factors that contributed to shifts in their cognition. Thematic analysis was conducted on data consisting of semi-structured interviews, field notes, a reflective report, and project meeting minutes. Results indicate that for both teacher educators, the programme functioned as an activity system that facilitated the emergence of advanced cognition of CLIL over the six months of the project, and did so along two distinct trajectories, one focusing on pedagogical applications of CLIL tenets and the other centring on implementing CLIL as an educational policy. The results also show how the collaborative nature of the teacher education programme fostered the expression of diverse views as well as shared motives through frequent interaction and co-construction of knowledge. As such, it offers a viable alternative to more traditional top-down approaches that are used to foster the professional development of teacher educators.

Epigenetic regulation of human-specific gene expression in the prefrontal cortex

BackgroundChanges in gene expression levels during brain development are thought to have played an important role in the evolution of human cognition. With the advent of high-throughput sequencing technologies, changes in brain developmental expression patterns, as well as human-specific brain gene expression, have been characterized. However, interpreting the origin of evolutionarily advanced cognition in human brains requires a deeper understanding of the regulation of gene expression, including the epigenomic context, along the primate genome. Here, we used chromatin immunoprecipitation sequencing (ChIP-seq) to measure the genome-wide profiles of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 acetylation (H3K27ac), both of which are associated with transcriptional activation in the prefrontal cortex of humans, chimpanzees, and rhesus macaques.ResultsWe found a discrete functional association, in which H3K4me3HP gain was significantly associated with myelination assembly and signaling transmission, while H3K4me3HP loss played a vital role in synaptic activity. Moreover, H3K27acHP gain was enriched in interneuron and oligodendrocyte markers, and H3K27acHP loss was enriched in CA1 pyramidal neuron markers. Using strand-specific RNA sequencing (ssRNA-seq), we first demonstrated that approximately 7 and 2% of human-specific expressed genes were epigenetically marked by H3K4me3HP and H3K27acHP, respectively, providing robust support for causal involvement of histones in gene expression. We also revealed the co-activation role of epigenetic modification and transcription factors in human-specific transcriptome evolution. Mechanistically, histone-modifying enzymes at least partially contribute to an epigenetic disturbance among primates, especially for the H3K27ac epigenomic marker. In line with this, peaks enriched in the macaque lineage were found to be driven by upregulated acetyl enzymes.ConclusionsOur results comprehensively elucidated a causal species-specific gene-histone-enzyme landscape in the prefrontal cortex and highlighted the regulatory interaction that drove transcriptional activation.

Gaze following in Archosauria-Alligators and palaeognath birds suggest dinosaur origin of visual perspective taking.

Taking someone else's visual perspective marks an evolutionary shift in the formation of advanced social cognition. It enables using others' attention to discover otherwise hidden aspects of the surroundings and is foundational for human communication and understanding of others. Visual perspective taking has also been found in some other primates, a few songbirds, and some canids. However, despite its essential role for social cognition, visual perspective taking has only been fragmentedly studied in animals, leaving its evolution and origins uncharted. To begin to narrow this knowledge gap, we investigated extant archosaurs by comparing the neurocognitively least derived extant birds-palaeognaths-with the closest living relatives of birds, the crocodylians. In a gaze following paradigm, we showed that palaeognaths engage in visual perspective taking and grasp the referentiality of gazes, while crocodylians do not. This suggests that visual perspective taking originated in early birds or nonavian dinosaurs-likely earlier than in mammals.

Imagination as a fundamental function of the hippocampus.

Imagination is a biological function that is vital to human experience and advanced cognition. Despite this importance, it remains unknown how imagination is realized in the brain. Substantial research focusing on the hippocampus, a brain structure traditionally linked to memory, indicates that firing patterns in spatially tuned neurons can represent previous and upcoming paths in space. This work has generally been interpreted under standard views that the hippocampus implements cognitive abilities primarily related to actual experience, whether in the past (e.g. recollection, consolidation), present (e.g. spatial mapping) or future (e.g. planning). However, relatively recent findings in rodents identify robust patterns of hippocampal firing corresponding to a variety of alternatives to actual experience, in many cases without overt reference to the past, present or future. Given these findings, and others on hippocampal contributions to human imagination, we suggest that a fundamental function of the hippocampus is to generate a wealth of hypothetical experiences and thoughts. Under this view, traditional accounts of hippocampal function in episodic memory and spatial navigation can be understood as particular applications of a more general system for imagination. This view also suggests that the hippocampus contributes to a wider range of cognitive abilities than previously thought. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

Cell-type profiling in salamanders identifies innovations in vertebrate forebrain evolution.

The evolution of advanced cognition in vertebrates is associated with two independent innovations in the forebrain: the six-layered neocortex in mammals and the dorsal ventricular ridge (DVR) in sauropsids (reptiles and birds). How these innovations arose in vertebrate ancestors remains unclear. To reconstruct forebrain evolution in tetrapods, we built a cell-type atlas of the telencephalon of the salamander Pleurodeles waltl. Our molecular, developmental, and connectivity data indicate that parts of the sauropsid DVR trace back to tetrapod ancestors. By contrast, the salamander dorsal pallium is devoid of cellular and molecular characteristics of the mammalian neocortex yet shares similarities with the entorhinal cortex and subiculum. Our findings chart the series of innovations that resulted in the emergence of the mammalian six-layered neocortex and the sauropsid DVR.

Perception‐to‐Cognition Tactile Sensing Based on Artificial‐Intelligence‐Motivated Human Full‐Skin Bionic Electronic Skin (Adv. Mater. 31/2022)

Electronic Skin In article number 2202622, by integrating the triboelectric effect with the supercapacitive iontronic effect, Yang Li, Guozhen Shen, and co-workers report an artificial intelligence (AI)-motivated full-skin bionic (FSB) e-skin based on bionic vellus hair and epidermis–dermis–hypodermis double interlocked microcone structures and demonstrate its smart application prospects from tactile perception to advanced intelligent tactile cognition.

Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System.

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.

Perception-to-Cognition Tactile Sensing Based on Artificial-Intelligence-Motivated Human Full-Skin Bionic Electronic Skin.

Traditional electronic skin (e-skin), due to the lack of human-brain-like thinking and judging capability, is powerless to accelerate the pace to the intelligent era. Herein, artificial intelligence (AI)-motivated full-skin bionic (FSB) e-skin consisting of the structures of human vellus hair, epidermis-dermis-hypodermis, is proposed. Benefiting from the double interlocked layered microcone structure and supercapacitive iontronic effect, the FSB e-skin exhibits ultrahigh sensitivity of 8053.1 kPa-1 (<1kPa), linear sensitivity of 3103.5 kPa-1 (1-34kPa), and fast response/recovery time of <5.6ms. In addition, it can realize the evolution from tactile perception to advanced intelligent tactile cognition after being equipped with a "brain". First, static/dynamic contactless tactile perception is achieved based on the triboelectric effect of the vellus hair bionics. Second, the supercapacitive iontronic effect based structural bionics of the epidermis-dermis-hypodermis and a five-layer multilayer perception (MLP) enable the general intelligent tactile cognition of gesture cognition and robot interaction. Most importantly, by making full use of the FSB e-skin with a six-layer MLP neural network, an advanced intelligent material cognition system is developed for real-time cognition of the object material species and locations via one contact, which surpasses the capability of humans.