Color-based object selection - for instance, looking for ripe tomatoes in the market - places demands on both perceptual and memory processes: it is necessary to form a stable perceptual estimate of surface color from a variable visual signal, as well as to retain multiple perceptual estimates in memory while comparing objects. Nevertheless, perceptual and memory processes in the color domain are generally studied in separate research programs with the assumption that they are independent. Here, we demonstrate a strong failure of independence between color perception and memory: the effect of context on color appearance is substantially weakened by a short retention interval between a reference and test stimulus. This somewhat counterintuitive result is consistent with Bayesian estimation: as the precision of the representation of the reference surface and its context decays in memory, prior information gains more weight, causing the retained percepts to be drawn toward prior information about surface and context color. This interaction implies that to fully understand information processing in real-world color tasks, perception and memory need to be considered jointly.

Performing music on the basis of reading a score requires reading ahead of what is being played in order to anticipate the necessary actions to produce the notes. Score reading thus not only involves the decoding of a visual score and the comparison to the auditory feedback, but also short-term storage of the musical information due to the delay of the auditory feedback during reading ahead. This study investigates the mechanisms of encoding of musical information in short-term memory during such a complicated procedure. There were three parts in this study. First, professional musicians participated in an electroencephalographic (EEG) experiment to study the slow wave potentials during a time interval of short-term memory storage in a situation that requires cross-modal translation and short-term storage of visual material to be compared with delayed auditory material, as it is the case in music score reading. This delayed visual-to-auditory matching task was compared with delayed visual-visual and auditory-auditory matching tasks in terms of EEG topography and voltage amplitudes. Second, an additional behavioural experiment was performed to determine which type of distractor would be the most interfering with the score reading-like task. Third, the self-reported strategies of the participants were also analyzed. All three parts of this study point towards the same conclusion according to which during music score reading, the musician most likely first translates the visual score into an auditory cue, probably starting around 700 or 1300 ms, ready for storage and delayed comparison with the auditory feedback.

Perception can be shaped by our expectations, which can lead to perceptual illusions. Similarly, long-term memories can be shaped to fit our expectations, which can generate false memories. However, it is generally assumed that short-term memory for percepts formed just 1 or 2 seconds ago accurately represents the percepts as they were at the time of perception. Here 4 experiments consistently show that within this timeframe, participants go from reliably reporting what was there (perceptual inference accurately reflecting the bottom-up input), to erroneously but with high confidence reporting what they expected to be there (memory report strongly influenced by top-down expectations). Together, these experiments show that expectations can reshape perceptual representations over short time scales, leading to what we refer to as short-term memory (STM) illusions. These illusions appeared when participants saw a memory display which contained real and pseudo-letters (i.e. mirrored letters). Within seconds after the memory display disappeared, high confidence memory errors increased substantially. This increase in errors over time indicates that the high confidence errors do not (purely) result from incorrect perceptual encoding of the memory display. Moreover, high confidence errors occurred mainly for pseudo-to-real letter memories, and much less often for real-to-pseudo-letter memories, indicating that visual similarity is not the primary cause of this memory-bias. Instead 'world knowledge' (e.g., which orientation letters usually have) appear to drive these STM illusions. Our findings support a predictive processing view of the formation and maintenance of memory in which all memory stages, including STM, involve integration of bottom-up memory input with top-down predictions, such that prior expectations can shape memory traces.

Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

NK cells are known as innate immune cells that lack immunological memory. Recently, it has been shown that NK cells remember encounters with chemical haptens that induce contact hypersensitivity and cytomegalovirus infection. Here, we show the existence of NK cell memory following HSV-2 infection. Stimulation with HSV-2 Ags led to higher IFNγ production in NK cells that were exposed 30 days previously to HSV-2, compared to NK cells from naïve mice. More importantly, this increased production of IFNγ in NK cells was independent of B- and T- lymphocytes and specific for the HSV-2 Ags. We also showed that previously exposed NK cells in a B- and T-lymphocyte free environment mediate protection against HSV-2 infection and they are necessary for the protection of mice against HSV-2 infection. Collectively, NK cells remember prior HSV-2 encounters independent of B- and T- lymphocytes leading to protection against HSV-2 mediated morbidity and mortality upon re-exposure.

The goal of the present study was to examine the extent to which working memory supports the maintenance of object locations during active spatial navigation. Participants were required to navigate a virtual environment and to encode the location of a target object. In the subsequent maintenance period they performed one of three secondary tasks that were designed to selectively load visual, verbal or spatial working memory subsystems. Thereafter participants re-entered the environment and navigated back to the remembered location of the target. We found that while navigation performance in participants with high navigational ability was impaired only by the spatial secondary task, navigation performance in participants with poor navigational ability was impaired equally by spatial and verbal secondary tasks. The visual secondary task had no effect on navigation performance. Our results extend current knowledge by showing that the differential engagement of working memory subsystems is determined by navigational ability.

Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually shifting from previous petrophysical models to deep learning models, which bring about obvious improvements in terms of accuracy and efficiency. However, it is difficult to achieve large amount of data acquisition required for deep learning due to the cost of detection, technical difficulties, and the limitations of complex geological parameters. To address the data shortage problem, a transfer learning prediction model based on long short-term memory neural networks has been proposed, and the model structure has been determined by parameter search and optimization methods in this paper. The proposed approach transfers knowledge from historical data to enhance new well prediction by sharing some parameters in the neural network structure. Moreover, the practicality and effectiveness of this method was tested by comparison based on two block datasets. The results showed that this method could significantly improve the prediction accuracy of the reservoir parameters in the event of data shortage.

Studies of the memory capabilities of nonhuman primates have consistently revealed a relative weakness for auditory compared to visual or tactile stimuli: extensive training is required to learn auditory memory tasks, and subjects are only capable of retaining acoustic information for a brief period of time. Whether a parallel deficit exists in human auditory memory remains an outstanding question. In the current study, a short-term memory paradigm was used to test human subjects' retention of simple auditory, visual, and tactile stimuli that were carefully equated in terms of discriminability, stimulus exposure time, and temporal dynamics. Mean accuracy did not differ significantly among sensory modalities at very short retention intervals (1-4 s). However, at longer retention intervals (8-32 s), accuracy for auditory stimuli fell substantially below that observed for visual and tactile stimuli. In the interest of extending the ecological validity of these findings, a second experiment tested recognition memory for complex, naturalistic stimuli that would likely be encountered in everyday life. Subjects were able to identify all stimuli when retention was not required, however, recognition accuracy following a delay period was again inferior for auditory compared to visual and tactile stimuli. Thus, the outcomes of both experiments provide a human parallel to the pattern of results observed in nonhuman primates. The results are interpreted in light of neuropsychological data from nonhuman primates, which suggest a difference in the degree to which auditory, visual, and tactile memory are mediated by the perirhinal and entorhinal cortices.

Breaks filled with different break activities often interrupt cognitive performance in everyday life. Previous studies have reported that both enhancing and deteriorating effects on challenging ongoing tasks such as working memory updating, depend on the type of break activity. However, neural mechanisms of these break-related alterations in working memory performance have not been studied, to date. Therefore, we conducted a brain imaging study to identify the neurobiological correlates of effects on the n-back working memory task related to different break activities. Before performing the n-back task in the magnetic resonance imaging (MRI) scanner, young adults were exposed to break activities in the MRI scanner involving (i) eyes-open resting, (ii) listening to music, and (iii) playing the video game "Angry Birds". Heart rate was measured by a pulse oximeter during the experiment. We found that increased heart rate during gaming as well as decreased relaxation levels after a video gaming break was related to poorer n-back task performance, as compared to listening to music. On the neural level, video gaming reduced supplementary motor area activation during working memory performance. These results may indicate that video gaming during a break may affect working memory performance by interfering with arousal state and frontal cognitive control functions.

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen's κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

During the development of the nervous system, the perinatal period is particularly sensitive as neuronal connections are still forming in the brain of the neonate. Alpha2-adrenergic receptors are overexpressed temporarily in proliferative zones in the developing brain, reaching a peak during the first postnatal week of life. Both stimulation and blocking of these receptors during this period alter the development of neural circuits, affecting synaptic connectivity and neuronal responses. They even affect motor and cognitive skills later on in the adult. It's especially important to look for the early neurological consequences resulting from such modifications, because they may go unnoticed. The main objective of the present study has been to reaffirm the importance of the maturation of alpha-adrenergic system in mice, by carrying out a comprehensive examination of motor, behavioral and cognitive effects in neonates, during early postnatal development, following chronic administration of the drug Clonidine, an alpha2 adrenergic system agonist. Our study shows that mice treated postnatally with clonidine present a temporal delay in the appearance of developmental markers, a slow execution of vestibular reflexes during first postnatal week of life and a blockade of the short term memory in the novel object recognition task. Shortly after the treatment the startle response is hyperreactive.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

Regular consumption of food enriched in omega3 polyunsaturated fatty acids (ω3 PUFAs) has been shown to reduce risk of cognitive decline in elderly, and possibly development of Alzheimer's disease. Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) are the most likely active components of ω3-rich PUFAs diets in the brain. We therefore hypothesized that exposing mice to a DHA and EPA enriched diet may reduce neuroinflammation and protect against memory impairment in aged mice. For this purpose, mice were exposed to a control diet throughout life and were further submitted to a diet enriched in EPA and DHA during 2 additional months. Cytokine expression together with a thorough analysis of astrocytes morphology assessed by a 3D reconstruction was measured in the hippocampus of young (3-month-old) and aged (22-month-old) mice. In addition, the effects of EPA and DHA on spatial memory and associated Fos activation in the hippocampus were assessed. We showed that a 2-month EPA/DHA treatment increased these long-chain ω3 PUFAs in the brain, prevented cytokines expression and astrocytes morphology changes in the hippocampus and restored spatial memory deficits and Fos-associated activation in the hippocampus of aged mice. Collectively, these data indicated that diet-induced accumulation of EPA and DHA in the brain protects against neuroinflammation and cognitive impairment linked to aging, further reinforcing the idea that increased EPA and DHA intake may provide protection to the brain of aged subjects.

Williams-Beuren syndrome (WBS) is a neurodevelopmental disorder caused by a heterozygous deletion of 26-28 genes at chromosome band 7q11.23. The complete deletion (CD) mouse model mimics the most common deletion found in WBS patients and recapitulates most neurologic features of the disorder along with some cardiovascular manifestations leading to significant cardiac hypertrophy with increased cardiomyocytes' size. Epigallocatechin-3-gallate (EGCG), the most abundant catechin found in green tea, has been associated with potential health benefits, both on cognition and cardiovascular phenotypes, through several mechanisms. We aimed to investigate the effects of green tea extracts on WBS-related phenotypes through a phase I clinical trial in mice. After feeding CD animals with green tea extracts dissolved in the drinking water, starting at three different time periods (prenatal, youth and adulthood), a set of behavioral tests and several anatomical, histological and molecular analyses were performed. Treatment resulted to be effective in the reduction of cardiac hypertrophy and was also able to ameliorate short-term memory deficits of CD mice. Taken together, these results suggest that EGCG might have a therapeutic and/or preventive role in the management of WBS.

Altered time reproduction is exhibited by patients with adult attention deficit hyperactivity disorder (ADHD). It remains unclear whether memory capacity influences the ability of adults with ADHD to reproduce time intervals.

Cerebral ischemia causes tissue death owing to occlusion of the cerebral blood vessels, and cerebral ischemia activates mitogen-activated protein kinase (MAPK) and induces secretion of pro-inflammatory cytokines. Adenosine A2A receptor agonist, polydeoxyribonucleotide (PDRN), suppresses the secretion of pro-inflammatory cytokines and exhibits anti-inflammatory effect. In the current study, the therapeutic effect of PDRN on cerebral ischemia was evaluated using gerbils. For the induction of cerebral ischemia, the common carotid arteries were exposed, and then aneurysm clips were used to occlude the common carotid arteries bilaterally for 7 minutes. In the PDRN-treated groups, the gerbils were injected intraperitoneally with 0.3 mL of saline containing 8 mg/kg PDRN, per a day for 7 days following cerebral ischemia induction. In order to confirm the participation of the adenosine A2A receptor in the effects mediated by PDRN, 8 mg/kg 7-dimethyl-1-propargylxanthine (DMPX), adenosine A2A receptor antagonist, was treated with PDRN. In the current study, induction of ischemia enhanced the levels of pro-inflammatory cytokines and increased phosphorylation of MAPK signaling factors in the hippocampus and basolateral amygdala. However, treatment with PDRN ameliorated short-term memory impairment by suppressing the production of pro-inflammatory cytokines and inactivation of MAPK signaling factors in cerebral ischemia. Furthermore, PDRN treatment enhanced the concentration of cyclic adenosine-3,5'-monophosphate (cAMP) as well as phosphorylation of cAMP response element-binding protein (p-CREB). Co-treatment of DMPX and PDRN attenuated the therapeutic effect of PDRN on cerebral ischemia. Based on these findings, PDRN may be developed as the primary treatment in cerebral ischemia.

The assessment of human spatial short-term memory has mainly been performed using visual stimuli and less frequently using auditory stimuli. This paper presents a framework for the development of SLAM-based Augmented Reality applications for the assessment of spatial memory. An AR mobile application was developed for this type of assessment involving visual and tactile stimuli by using our framework. The task to be carried out with the AR application is divided into two phases: 1) a learning phase, in which participants physically walk around a room and have to remember the location of simple geometrical shapes; and 2) an evaluation phase, in which the participants are asked to recall the location of the shapes. A study for comparing the performance outcomes using visual and tactile stimuli was carried out. Fifty-three participants performed the task using the two conditions (Tactile vs Visual), but with more than two months of difference (within-subject design). The number of shapes placed correctly was similar for both conditions. However, the group that used the tactile stimulus spent significantly more time completing the task and required significantly more attempts. The performance outcomes were independent of gender. Some significant correlations among variables related to the performance outcomes and other tests were found. The following significant correlations among variables related to the performance outcomes using visual stimuli and the participants' subjective variables were also found: 1) the greater the number of correctly placed shapes, the greater the perceived competence; 2) the more attempts required, the less the perceived competence. We also found that perceived enjoyment was higher when a higher sense of presence was induced. Our results suggest that tactile stimuli are valid stimuli to exploit for the assessment of the ability to memorize spatial-tactile associations, but that the ability to memorize spatial-visual associations is dominant. Our results also show that gender does not affect these types of memory tasks.

ɣ-aminobutyric acid (GABA) facilitator valproic acid may be able to curb memory disruption induced by morphine exposure.

Electrical restitution (ER) is a major determinant of repolarization stability and, under fast pacing rate, it reveals memory properties of the cardiac action potential (AP), whose dynamics have never been fully elucidated, nor their ionic mechanisms. Previous studies have looked at ER mainly in terms of changes in AP duration (APD) when the preceding diastolic interval (DI) changes and described dynamic conditions where this relationship shows hysteresis which, in turn, has been proposed as a marker of short-term AP memory and repolarization stability. By means of numerical simulations of a non-propagated human ventricular AP, we show here that measuring ER as APD versus the preceding cycle length (CL) provides additional information on repolarization dynamics which is not contained in the companion formulation. We focus particularly on fast pacing rate conditions with a beat-to-beat variable CL, where memory properties emerge from APD vs CL and not from APD vs DI and should thus be stored in APD and not in DI. We provide an ion-currents characterization of such conditions under periodic and random CL variability, and show that the memory stored in APD plays a stabilizing role on AP repolarization under pacing rate perturbations. The gating kinetics of L-type calcium current seems to be the main determinant of this safety mechanism. We also show that, at fast pacing rate and under otherwise identical pacing conditions, a periodically beat-to-beat changing CL is more effective than a random one in stabilizing repolarization. In summary, we propose a novel view of short-term AP memory, differentially stored between systole and diastole, which opens a number of methodological and theoretical implications for the understanding of arrhythmia development.