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Cognitive and working memory training : perspectives from psychology, neuroscience, and human development
\"Cognitive and Working Memory Training assembles an interdisciplinary group of distinguished authors--all experts in the field--who have been testing the efficacy of cognitive and working memory training using a combination of behavioral, neuroimaging, meta-analytic, and computational modelling methods. This edited volume is a defining resource on the practicality and utility of the field of cognitive training research in general, and working memory training in particular. Importantly, one focus of the book is on the notion of transfer--namely, the extent to which cognitive training--be it through music, video-game play, or working memory demanding interventions at school--generalizes to learning and performance measures that were decidedly not part of the training regimen. As most cognitive scientists (and perhaps many casual observers) recognize, the notions of cognitive training and transfer have been widely controversial for many reasons, including disagreement over the reliability of outcomes and consensus on methodological \"best practices,\" and even the ecological validity of laboratory-based tests. This collection does not resolve these debates of course; but its contribution is to address them directly by creating an exchange in a single compendium among scientists who, in separate research publications, do not always reach the same conclusions. The book is organized around comprehensive overview chapters from different disciplinary perspectives--Cognitive Psychology (by Hicks and Engle), Neuroscience (by Kuchinsky and Haarmann), and Development (by Ling and Diamond)--that define major issues, terms, and themes in the field, with a pointed set of challenge questions to which other scientists respond in subsequent chapters. The goal of this volume is to educate. It is designed for students and researchers, and perhaps the armchair psychologist. Crucially, the contributors recognize that it is good for science to persistently confront our understanding of an area: Debate and alternative viewpoints, backed by theory, data, and inferences drawn from the evidence, is what advances scientific knowledge. This book probes established paradigms in cognitive training research, and the long-form of these chapters (not found in scientific journals) allows detailed exploration of the current state of the science. Such breadth intends to invite novel ways of thinking about the nature of cognitive and perceptual plasticity, which may enlighten either new efforts at training, new inferences about prior results, or both\"-- Provided by publisher.
Book
The Coevolution of Biomolecules and Prebiotic Information Systems in the Origin of Life: A Visualization Model for Assembling the First Gene
Prebiotic information systems exist in three forms: analog, hybrid, and digital. The Analog Information System (AIS), manifested early in abiogenesis, was expressed in the chiral selection, nucleotide formation, self-assembly, polymerization, encapsulation of polymers, and division of protocells. It created noncoding RNAs by polymerizing nucleotides that gave rise to the Hybrid Information System (HIS). The HIS employed different species of noncoding RNAs, such as ribozymes, pre-tRNA and tRNA, ribosomes, and functional enzymes, including bridge peptides, pre-aaRS, and aaRS (aminoacyl-tRNA synthetase). Some of these hybrid components build the translation machinery step-by-step. The HIS ushered in the Digital Information System (DIS), where tRNA molecules become molecular architects for designing mRNAs step-by-step, employing their two distinct genetic codes. First, they created codons of mRNA by the base pair interaction (anticodon–codon mapping). Secondly, each charged tRNA transferred its amino acid information to the corresponding codon (codon–amino acid mapping), facilitated by an aaRS enzyme. With the advent of encoded mRNA molecules, the first genes emerged before DNA. With the genetic memory residing in the digital sequences of mRNA, a mapping mechanism was developed between each codon and its cognate amino acid. As more and more codons ‘remembered’ their respective amino acids, this mapping system developed the genetic code in their memory bank. We compared three kinds of biological information systems with similar types of human-made computer systems.
Journal Article
Maternal-Fetal Microchimerism: Impacts on Offspring's Immune Development and Transgenerational Immune Memory Transfer
Maternal-fetal microchimerism is a fascinating phenomenon in which maternal cells migrate to the tissues of the offspring during both pregnancy and breastfeeding. These cells primarily consist of leukocytes and stem cells. Remarkably, these maternal cells possess functional potential in the offspring and play a significant role in shaping their immune system development. T lymphocytes, a cell population mainly found in various tissues of the offspring, have been identified as the major cell type derived from maternal microchimerism. These T lymphocytes not only exert effector functions but also influence the development of the offspring's T lymphocytes in the thymus and the maturation of B lymphocytes in the lymph nodes. Furthermore, the migration of maternal leukocytes also facilitates the transfer of immune memory across generations. Maternal microchimerism has also been observed to address immunodeficiencies in the offspring. This review article focuses on investigating the impact of maternal cells transported within maternal microchimerism on the immune system development of the offspring, as well as elucidating the effector functions of maternal cells that migrate through the placenta and breast milk to reach the offspring.
Journal Article
Sub-nanosecond heat-based logic, writing and reset in an antiferromagnetic magnetoresistive memory
Thermal logic aims to create thermal counterparts to electronic circuits. In this work, we investigate experimentally the response of an analog memory device based on a thin film of an antiferromagnetic metal CuMnAs to bursts of heat pulses generated by the absorption of femtosecond laser pulses at room ambient temperature. When a threshold temperature in the heat-based short-term memory of the device is exceeded, the output of the in-memory logic operations is transferred within the same device to a long-term memory, where it can be retrieved at macroscopic times. The long-term memory is based on magnetoresistive switching from a reference low-resistive uniform magnetic state to high-resistive metastable nanofragmented magnetic states. The in-memory heat-based logic operations and the conversion of the outputs into the electrically-readable long-term magnetoresistive memory were performed at sub-nanosecond time scales, making them compatible with the GHz frequencies of standard electronics. Finally, we demonstrate the possibility of rapidly resetting the long-term memory to the reference low-resistive state by heat pulses.
Journal Article
Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning
The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs.
Journal Article
Hippocampal-Cortical Memory Trace Transfer and Reactivation Through Cell-Specific Stimulus and Spontaneous Background Noise
The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex exhibit coordinated reactivations during awake ripple events. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear due to the lack of unified models that include both the hippocampal and cortical networks. In this work, using a coupled biophysical model of both CA1 and prefrontal cortex, we investigate possible mechanisms of hippocampal-cortical memory trace transfer and the conditions that assist reactivation of the transferred memory traces in the prefrontal cortex. To validate our model, we first show that the local field potentials generated in the hippocampus and prefrontal cortex exhibit ripple range activities that are consistent with the recent experimental studies. Then we demonstrate that during ripples, sequence replays can successfully transfer the information stored in the hippocampus to the prefrontal cortex recurrent networks. We investigate possible mechanisms of memory retrieval in prefrontal cortex networks. Our results suggest that the stored memory traces in the prefrontal cortex network can be retrieved through two different mechanisms, namely the cell-specific input representing external stimuli and non-specific spontaneous background noise representing spontaneous memory recall events. Importantly, in both cases, the memory reactivation quality is robust to network connection loss. Finally, we find out that the quality of sequence reactivations is enhanced by both increased number of SWRs and an optimal background noise intensity, which tunes the excitability of neurons to a proper level. Our study presents a mechanistic explanation for the memory trace transfer from the hippocampus to neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.
Journal Article
Unlimited plasticity of embodied, cognitive subjects: a new playground for the UAL framework
Birch, Ginsburg, and Jablonka lay out a very convincing case for an important transition marker: unlimited associative learning (UAL). Especially welcome are the empirical predictions. I focus here not on the question of how to infer phenomenal consciousness from this behavioral metric, but on possible novel applications of this useful and fundamental framework. Specifically, I highlight two aspects of biology that are often not considered in philosophy of mind approaches that focus on natural species and evolutionary time scales. These are (1) the ability of minds and bodies to change drastically on the time scale of an individual experiencing subject, and (2) bioengineering of novel living forms with no evolutionary history at the organism level. Both of these aspects provide interesting new contexts within which to explore UAL and its implications.
Journal Article
Challenges and Applications of Emerging Nonvolatile Memory Devices
Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.
Journal Article
Correlations between components of the immune system version 1; peer review: 1 approved with reservations, 1 not approved
Background: No evidence of the possibility to alter a constituent of the immune system without directly affecting one of its associated components has been shown yet.
Methods: A schematic model was developed in which two triggers, fasting and splenectomy, were studied for their ability to affect the expression of cell membrane epitopes and the cytokine secretion of out-of-body autogeneic and syngeneic lymphocytes.
Results: Fasting decreased expression of CD8 and CD25 and increased TNFα levels. The effect of splenectomy as a trigger was investigated in non-fasting mice by comparing splenectomized and non-splenectomized mice. An increase in the CD8 expression and in TNFα, IFNg, and IL10 secretion was noted. The effect of splenectomy as a trigger was investigated in fasting mice by comparing splenectomized and non-splenectomized mice. Splenectomy had a significant effect on expression of CD25 and CD4 CD25 and on secretion of TNFα, IFNg, and IL10. To determine the effect of keeping the cells in an out-of-body location on the expression of lymphocyte epitopes, tubes kept on top of the cages of the fasting mice were compared with tubes kept on top of empty cages. A significant change in the CD8 expression was noted. To determine the effect of keeping cells in an out-of-body location on cytokine secretion, tubes kept on top of cages were tested for cytokine levels. A significant decrease was noted for the secretion of TNFα and IFNg.
Conclusions: The data obtained from this study characterized a system for induction of correlations between two components of the immune system without a transfer of mediators. The study showed that a mouse could affect cells at a distance and alter the expression of surface markers and cytokine secretion following two types of triggers: fasting and/or splenectomy. Thus, an out-of-body correlation can be induced between two components of the immune system.
Journal Article
Adaptive temporal mesh equidistribution for studying the singularly perturbed delay-in-time parabolic equations
This article intends to contribute a time-dependent mesh method to analyze the singularly perturbed delay parabolic partial differential equation exhibiting the boundary layer instabilities. In engineering control systems, past population effects in ecological models, stress responses in viscoelastic materials, and past pressures or velocities in complex fluid flows are all modeled by delayed partial differential equations. In the vicinity of the boundary layer, where the velocity field exerts a major influence, the solution exhibits sharp gradients. Because these gradients are steep, to obtain accurate solution a numerical method with dynamic mesh is obliged. To compute these singularities more accurately, we imposed adaptive moving mesh method that is a time-dependent self-adaptive method by imposing the center differences in the spatial direction and implicit Euler in the temporal direction. A stability analysis for the proposed scheme is performed using the energy technique. Numerical experiments are performed with various examples, and the approximated results support theoretical findings. Six numerical examples are presented with their errors and convergence orders.
Journal Article