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Background: Social media in our networks have been exploited as dynamic learning tools and free platforms. Aims: The main objective of this study is to determine the impact of VARK learning styles (visual (V), aural (A), read/write (R), and kinesthetic (K)) in enhancing parasitological laboratory skills using social media and various learning theories. Methods: A research sample of 100 chemists working in Mega Alfa labs underwent online learning of laboratory parasitology skills via Facebook posts and WhatsApp dictated messages for an average of 7 weeks. All posts served various VARK learning styles and were designed based on Zeigarnik's effect (conducting information with tactical breaks), memory storage and retrieval strength theories (repetition of information). Trainees were classified according to their VARK learning style preferences and were evaluated through pre/post-tests. Data on VARK learning styles were summarized using frequency (count) and relative frequency (percentage). Data of pre-test and post-test scores were summarized using mean and standard deviation. T-test was used to compare pre-test and post-test scores. The difference between the pre-test results, the post-test results and the preferred learning style was analyzed using ANOVA with Tukey's post-hoc testing. P-values less than 0.05 were considered statistically significant. Results: In a total of 100 trainees, tri-modal and multimodal learning styles were preferred by 40% and 30% of the trainees respectively; on the contrary, the unimodal and bimodal learning styles were the least preferred. In the trimodal and multimodal groups, the post-test results showed significant increase when compared with the pre-test results. Also, using the ANOVA test and a Tukey's post-hoc comparison, the assemblage of multiple learning styles (tri-modal and multimodal) appeared to significantly improve the learning performance in the post-test results when compared with the unimodal and bimodal groups. Conclusion: The tri-modal and multimodal learning styles were found to influence the acquirement of the laboratory parasitology skills much better than the unimodal and bimodal learning styles. Kinesthetic learning should have a special emphasis in training. [ABSTRACT FROM AUTHOR]

Abstract Background Social media in our networks have been exploited as dynamic learning tools and free platforms. Aims The main objective of this study is to determine the impact of VARK learning styles (visual (V), aural (A), read/write (R), and kinesthetic (K)) in enhancing parasitological laboratory skills using social media and various learning theories. Methods A research sample of 100 chemists working in Mega Alfa labs underwent online learning of laboratory parasitology skills via Facebook posts and WhatsApp dictated messages for an average of 7 weeks. All posts served various VARK learning styles and were designed based on Zeigarnik’s effect (conducting information with tactical breaks), memory storage and retrieval strength theories (repetition of information). Trainees were classified according to their VARK learning style preferences and were evaluated through pre/post-tests. Data on VARK learning styles were summarized using frequency (count) and relative frequency (percentage). Data of pre-test and post-test scores were summarized using mean and standard deviation. T-test was used to compare pre-test and post-test scores. The difference between the pre-test results, the post-test results and the preferred learning style was analyzed using ANOVA with Tukey’s post-hoc testing. P-values less than 0.05 were considered statistically significant. Results In a total of 100 trainees, tri-modal and multimodal learning styles were preferred by 40% and 30% of the trainees respectively; on the contrary, the unimodal and bimodal learning styles were the least preferred. In the trimodal and multimodal groups, the post-test results showed significant increase when compared with the pre-test results. Also, using the ANOVA test and a Tukey’s post-hoc comparison, the assemblage of multiple learning styles (tri-modal and multimodal) appeared to significantly improve the learning performance in the post-test results when compared with the unimodal and bimodal groups. Conclusion The tri-modal and multimodal learning styles were found to influence the acquirement of the laboratory parasitology skills much better than the unimodal and bimodal learning styles. Kinesthetic learning should have a special emphasis in training.

Oxygen vacancy engineering in metal oxides is a propitious route to modulate their resistive switching properties for memory and neuromorphic applications. This review provides an account of the research works on tailoring RS behavior in oxide thin-film-based memristor devices by oxygen vacancy engineering. We discuss the recent research progress on controlling oxygen vacancy concentration in metal oxide thin films and its impact on their resistive switching properties for application in electronic memory and neuromorphic computing devices.

Photochargeable semiconductors enable energy harvesting and storage in a single material. Charges separated upon absorption of photons can accumulate in highly energetic trap states if morphology, size, and chemical composition are appropriately chosen. For example, electrons can survive for several hours if hole scavengers are used to prevent their recombination with photogenerated holes, and their negative charge is balanced by positive counter‐ions. The first database of charge‐storing semiconductors is recently released, containing information from more than 50 publications within the past 40 years. Now, the database has been updated with more than 90 entries from the latest works on the topic. These materials have been largely utilized in the context of "dark photocatalysis", that is, redox reactions enabled by photocharged semiconductors long after cessation of light irradiation. Nevertheless, a variety of further potential applications have not received enough visibility, including memory storage, steel anti‐corrosion, sensors, and micromotors. In this review, the key figures of merit of photocharged semiconductors and the empirical relationships found between them is highlighted. After showing the latest advances in dark photocatalysis, it is discussed how other application fields may benefit from these materials. For each area, promising research directions based on the findings from the database are recommended. [ABSTRACT FROM AUTHOR]

In nonlinear dynamical systems with barriers/thresholds, the signal response against a weak external input signal is enhanced by an appropriate additive noise (stochastic resonance). In recent years, progress in the application of stochastic resonance shows that the existence of additive noise heightens the memory storage functions in memory elements using bistable oscillations even with extremely low power consumption. By not restricting the additive noise, the deterministic chaos (an internal fluctuation) induces a similar phenomenon known as chaotic resonance. Chaotic resonance appears in nonlinear dynamical systems and is accompanied by chaos–chaos intermittency, where the chaotic orbit intermittently transitions among separated attractor regions through attractor-merging bifurcation. Previously, a higher chaotic resonance sensitivity than that of stochastic resonance was reported in various types of systems. In this study, we hypothesize that chaotic-resonance-based memory devices can store information with lower power consumption than that of stochastic-resonance-based devices. To prove this hypothesis, we induced attractor-merging bifurcation in a cubic map system, which is the simplest model for the emergence of chaotic resonance. Thereafter, we adjusted the internal system parameter under a noise-free system as the chaotic resonance and applied stochastic noise similar to the condition for inducing stochastic resonance. The results of this study reveal that, even with weaker memory storage input signals, the former exhibits a higher memory storage capability than the latter. The approach using chaotic resonance could facilitate the development of memory devices that were hitherto restricted to the application of stochastic resonance.

The data-domain least-squares reverse time migration (LSRTM), a promising imaging method for obtaining a high-resolution reflectivity image, can be implemented by matching the de-migrated data to the observed one. However, LSRTM requires expensive computational costs in its implementation due to tremendous memory usage (direct storage on disk/memory) for saving source wavefields and repeatable forward/backward wavefield simulations with iterations compared to conventional RTM. Although the source wavefield reconstruction technique can be used to reduce the memory usage, additional computational cost is inevitable because it is implemented during backward wavefield simulation. To alleviate the computational burden, we have developed an efficient LSRTM scheme with local cross-correlation imaging condition (LSRTM-LC), which can use pre-saved source wavefields according to the window size. Because the window size is much shorter than the total recording time for wavefield extrapolation, storing source wavefields into computer memory can be feasible. In addition, the procedure for storing source wavefields is implemented only at the first iteration because the background velocity is fixed during iterations of LSRTM. To validate the feasibility of the LSRTM-LC scheme, we have carried out several numerical tests with synthetic datasets. Numerical tests demonstrated that LSRTM-LC can provide us with equivalent de-migrated data from saved source wavefields and the gradient vector obtained from LSRTM-LC compared to those obtained from the conventional LSRTM. As a result, LSRTM-LC can generate the same quality of reflectivity models as conventional LSRTM, however, it requires less computational costs compared to conventional LSRTM. [ABSTRACT FROM AUTHOR]

The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD. To this end, we propose a noise reduction method for Stochastic Variance Reduction gradient (SVRG), called N-SVRG, which uses small batches samples instead of all samples for the average gradient calculation, while performing an incremental update of the average gradient. In each round of iteration, a small batch of samples is randomly selected for the average gradient calculation, while the average gradient is updated by rounding of the past model gradients during internal iterations. By suitably reducing the batch size B, the memory storage as well as the number of iterations can be reduced. The experiments are compared with the state-of-the-art Mini-Batch SGD, AdaGrad, RMSProp, SVRG and SCSG, and it is demonstrated that N-SVRG outperforms SVRG and SASG, and is on par with SCSG. Finally, by exploring the relationship between the small values of different parameters n. B and k and the effectiveness of the algorithm, we prove that ourN-SVRG algorithm has some stability and can achieve sufficient accuracy even in the case of small batch size. The advantages and disadvantages of various methods are experimentally compared, and the stability of N-SVRG is explored by parameter settings. [ABSTRACT FROM AUTHOR]

PKMζ is an autonomously active PKC isoform crucial for the maintenance of synaptic long‐term potentiation (LTP) and long‐term memory. Unlike other kinases that are transiently stimulated by second messengers, PKMζ is persistently activated through sustained increases in protein expression of the kinase. Therefore, visualizing increases in PKMζ expression during long‐term memory storage might reveal the sites of its persistent action and thus the location of memory‐associated LTP maintenance in the brain. Using quantitative immunohistochemistry validated by the lack of staining in PKMζ‐null mice, we examined the amount and distribution of PKMζ in subregions of the hippocampal formation of wild‐type mice during LTP maintenance and spatial long‐term memory storage. During LTP maintenance in hippocampal slices, PKMζ increases in the pyramidal cell body and stimulated dendritic layers of CA1 for at least 2 hr. During spatial memory storage, PKMζ increases in CA1 pyramidal cells for at least 1 month, paralleling the persistence of the memory. During the initial expression of the memory, we tagged principal cells with immediate‐early gene Arc promoter‐driven transcription of fluorescent proteins. The subset of memory‐tagged CA1 cells selectively increases expression of PKMζ during memory storage, and the increase persists in dendritic compartments within stratumradiatum for 1 month, indicating long‐term storage of information in the CA3‐to‐CA1 pathway. We conclude that persistent increases in PKMζ trace the molecular mechanism of LTP maintenance and thus the sites of information storage within brain circuitry during long‐term memory. [ABSTRACT FROM AUTHOR]

The emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak‐path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak‐path current and improve efficiency, reliability, and stability may benefit next‐generation storage‐class memory (SCM). Moreover, conventional digital computers face the von‐Neumann bottleneck and the slowdown of transistors' scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak‐path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed. [ABSTRACT FROM AUTHOR]

The emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak‐path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak‐path current and improve efficiency, reliability, and stability may benefit next‐generation storage‐class memory (SCM). Moreover, conventional digital computers face the von‐Neumann bottleneck and the slowdown of transistors’ scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak‐path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed.

Fear is a response to impending threat that prepares a subject to make appropriate defensive responses, whether to freeze, fight, or flee to safety. The neural circuits that underpin how subjects learn about cues that signal threat, and make defensive responses, have been studied using Pavlovian fear conditioning in laboratory rodents as well as humans. These studies have established the amygdala as a key player in the circuits that process fear and led to a model where fear learning results from long-term potentiation of inputs that convey information about the conditioned stimulus to the amygdala. In this review, we describe the circuits in the basolateral amygdala that mediate fear learning and its expression as the conditioned response. We argue that while the evidence linking synaptic plasticity in the basolateral amygdala to fear learning is strong, there is still no mechanism that fully explains the changes that underpin fear conditioning.

Featured Application: This paper presents the solutions for real-time visual tasks such as visual tracking. Some real-life scenarios such as tracking specific targets can apply this method by extending ICCA. Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers. [ABSTRACT FROM AUTHOR]

Cortico-limbic brain activity associated with anger may be susceptible to nicotine and, thus, may contribute to smoking initiation and nicotine addiction. The purpose of the study was to identify the brain regions that are most reactive to nicotine and show the greatest association with anger task performance. Twenty adult nonsmokers (9 women, 11 men) participated in two laboratory sessions to assess brain metabolism with fluoro deoxy-glucose Positron Emission Topography (FDG-PET) in response to nicotine and placebo patches during an anger provocation task. Outcome variables for the anger provocation task were reaction time, intensity and length of retaliation. Reaction time was associated with nicotine-induced changes in the left thalamus. Length of retaliation was associated with a functionally linked set of cortical and subcortical structures such as right frontal lobe, right anterior cingulate (BA 24), right uncus, left parietal lobe, left BA 11, left cingulate, left BA 25, left amygdala, left BA 30, left BA 38 and BA 9. These findings reveal the underlying brain circuitry targeted by nicotine during anger provocation.

It is well established that adrenal stress hormone-induced activation of the basolateral complex of the amygdala (BLA) influences memory consolidation. The present experiments investigated the involvement of corticotropin-releasing hormone (CRH) in the BLA in modulating memory consolidation. Bilateral infusions of the CRH receptor antagonist [9-41]-alpha-helical CRH (0.3, 1.0, or 3.0 microg in 0.2 microl) administered into the BLA of male Sprague-Dawley rats immediately after aversively motivated inhibitory avoidance training produced dose-dependent impairment of 48-h retention performance. Because the CRH receptor antagonist infusions did not impair retention when administered into the BLA 3 h after training, the retention impairment selectively was due to time-dependent influences on memory consolidation. Furthermore, because immediate posttraining infusions of [9-41]-alpha-helical CRH into the adjacent central nucleus of the amygdala (CEA) were ineffective, the effect selectively involved the BLA. Immunocytochemistry showed that the aversive training stimulus of a single, brief footshock increased CRH levels in the CEA. These findings indicate that activation of CRH receptors in the BLA, likely by training-induced release of endogenous peptide originating from the CEA, participates in mediating stress effects on memory consolidation.

Digital signature algorithm (DSA) (resp. ECDSA) involves modular exponentiation (resp. scalar multiplication) of a public and known base by a random one-time exponent. In order to speed up this operation, well-known methods take advantage of the memorization of base powers (resp. base multiples). Best approaches are the Fixed-base radix-R method and the Fixed-base Comb method. In this paper, we present a new approach for storage/online computation trade-off, by using a multiplicative splitting of the digits of the exponent radix-R representation. We adapt classical algorithms for modular exponentiation and scalar multiplication in order to take advantage of the proposed exponent recoding. An analysis of the complexity for practical size shows that our proposed approach involves a lower storage for a given level of online computation. This is confirmed by implementation results showing significant memory saving, up to 3 times for the largest NIST standardized key sizes, compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Dielectric relaxations along with other optical and thermal properties of a room temperature ferroelectric liquid crystal (S) -1-[4'-(octyloxy carbonyl) phenyl]-4-[2-chloro-(4-benzamido acetyl) phenyl propionato]-Benzoate is reported in the low frequency range. The mesogen exhibits cholesteric, smectic A* and smectic C* phases identified by textures threaded, focal conic and broken focal conic respectively. It is interesting to note the wide thermal span of smectic C* ranging from 65 °C to -20 °C. A relaxation process in which the relaxation is suppressed by external bias voltage, named as Gold stone mode originated at around 500 Hz is investigated with respect to temperature in the entire thermal span of smectic C* phase and is analyzed by Cole-Cole plots. The relaxation frequency shifted to the lower side as the temperature is decreased indicating Arrhenius behaviour. The distribution parameter α for various relaxation processes is calculated. From the Arrhenius graph the activation energy of the smectic C* phase is elucidated. In addition, the thermal stability of three mesophases namely cholesteric, smectic A* and smectic C* is discussed. The bi-stable switching (ON and OFF states) of the mesogen are recorded. A token of evidence for memory storage is demonstrated through dielectric hysteresis data. Light filtering action in smectic C* phase is discussed. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.

Highlights • CDA was used to measure filtering of happy, angry and neutral faces from VWM. • VWM capacity affected filtering of emotional faces during a VWM task. • Individuals with high capacity were not distracted by emotional facial distractors. • Individuals with low capacity had difficulties in filtering angry and neutral faces. • This difficulty may relate to attention capture by potentially threatening faces. Abstract Emotional faces can serve as distractors for visual working memory (VWM) tasks. An event-related potential called contralateral delay activity (CDA) can measure the filtering efficiency of face distractors. Previous studies have investigated the influence of VWM capacity on filtering efficiency of simple neutral distractors but not of face distractors. We measured the CDA indicative of emotional face filtering during a VWM task related to facial identity. VWM capacity was measured in a separate colour change detection task, and participants were divided to high- and low-capacity groups. The high-capacity group was able to filter out distractors similarly irrespective of its facial emotion. In contrast, the low-capacity group failed in filtering the neutral and angry face distractors, while the filtering was efficient for the happy face distractors. The results indicate that potentially threatening faces are particularly difficult to filter if VWM capacity is limited. [ABSTRACT FROM AUTHOR]

Bearings are one of the most important components in many industrial machines. Effective bearing fault diagnosis and severity detection are critical for keeping the machines operate normally and safe. In this study, the problem of simultaneous bearing fault diagnosis and severity detection with deep learning is addressed. Existing solutions developed using deep learning rely on fault feature extraction using complicated signal processing techniques. They perform bearing fault diagnosis and severity detection separately and normally require extensive supervised fine tuning. This study presents an effective deep learning‐based solution using a large memory storage and retrieval (LAMSTAR) neural network. The developed approach can automatically extract self‐learned fault features and perform bearing fault diagnosis and severity detection simultaneously. The structure of the LAMSTAR network is determined by optimally selecting the sliding box size of the input time–frequency matrix. The effectiveness of the proposed approach is validated using data collected from rolling element bearing tests. [ABSTRACT FROM AUTHOR]

Knowledge about how information is stored in neurons of animals and in the human brain is still incomplete. A hypothesis related to long-term changes in synaptic efficiency has strong experimental support, but does not seem to be able to explain all observations. It has recently been proposed that magnetite together with a prion-like protein could be involved in a tandem mechanism for storage of memory in neurons in which electric impulses are received and reshaped by the magnetite to a form which can be accepted by the protein. The magnetite crystals can be magnetized by an electrical impulse, but they cannot hold the magnetism, which drops to zero after each impulse. Therefore, magnetite cannot be the substance in which information is stored. In the present paper we explain how a tandem mechanism could function in a neuron in which magnetite is situated together with a prion-like protein close to the cell surface membrane of the axon. We assume in addition that the information is stored in special storage neurons. With this, we propose a new hypothesis for information storage in neurons which could operate in addition to synaptic plasticity, but perhaps in different neurons. [ABSTRACT FROM AUTHOR]

We report on the surfactant/additives free synthesis of single domain superparamagnetic Fe 3 O 4 nanoparticles (FNPs) by rapid microwave synthesis method. X-Ray diffraction (XRD) and Scanning Electron Microscopy (SEM) studies reveal that FNPs were crystallized in the magnetite (Fe 3 O 4) lattice and have granular shape with average particles size of ∼14 nm. The band gap of FNPs calculated from UV–Visible absorption spectrum is ∼2.3 eV. FNPs exhibit single domain superparamagnetic behaviour as confirmed from magnetic hysteresis loops. The nanocomposites of FNPs and β-phase of PVDF were prepared via mixed solvent approach using THF/DMF with different concentrations of FNPs. The rheological behaviour of the nanocomposites was studied with and without ageing in an applied magnetic field of 3500 Gauss for 3 h. Samples depicts shear thinning to shear thickening transition, decrease in zero shear viscosity and long frequency range viscoelastic behaviour as function of increasing FNP concentration. Increase in CST region, fluidity and complex modulus are the main consequences of magnetic aging. FNP-PVDF nanocomposites with such properties could find applications in flexible magnetic and mechanical films for soft body armor and memory storage. [Display omitted] • Surfactant/additive free microwave synthesis of superparamagnetic Fe 3 O 4 nanoparticles. • Controlled synthesis of granular shaped nanoparticles with uniform size distribution with average particles size ∼14 nm. • Tuning of shear and dynamic rheology of PVDF as a function of nanoparticle doping concentration and magnetic ageing. • PVDF: Fe3O4 nanocomposites may find applications in magnetic and mechanical films for soft body armor and memory storage. [ABSTRACT FROM AUTHOR]

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm. [ABSTRACT FROM AUTHOR]

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns.

Least-squares reverse-time migration (LSRTM), which involves several iterations of reverse-time migration (RTM) and Born modeling procedures, can provide subsurface images with better balanced amplitudes, higher resolution and fewer artifacts than standard migration. However, the same source wavefield is repetitively computed during the Born modeling and RTM procedures of different iterations. We developed a new LSRTM method with modified excitation-amplitude imaging conditions, where the source wavefield for RTM is forward propagated only once while the maximum amplitude and its excitation-time at each grid are stored. Then, the RTM procedure of different iterations only involves: (1) backward propagation of the residual between Born modeled and acquired data, and (2) implementation of the modified excitation-amplitude imaging condition by multiplying the maximum amplitude by the back propagated data residuals only at the grids that satisfy the imaging time at each time-step. For a complex model, 2 or 3 local peak-amplitudes and corresponding traveltimes should be confirmed and stored for all the grids so that multiarrival information of the source wavefield can be utilized for imaging. Numerical experiments on a three-layer and the Marmousi2 model demonstrate that the proposed LSRTM method saves huge computation and memory cost. [ABSTRACT FROM AUTHOR]

According to the sequential hypothesis (SH) memories acquired during wakefulness are processed during sleep in two serial steps respectively occurring during slow wave sleep (SWS) and REM sleep. During SWS memories to be retained are distinguished from irrelevant or competing traces that undergo downgrading or elimination. Processed memories are stored again during REM sleep which integrates them with preexisting memories. The hypothesis received support from a wealth of EEG, behavioral, and biochemical analyses of trained rats. Further evidence was provided by independent studies of human subjects. SH basic premises, data, and interpretations have been compared with corresponding viewpoints of the synaptic homeostatic hypothesis (SHY). Their similarities and differences are presented and discussed within the framework of sleep processing operations. SHY’s emphasis on synaptic renormalization during SWS is acknowledged to underline a key sleep effect, but this cannot marginalize sleep’s main role in selecting memories to be retained from downgrading traces, and in their integration with preexisting memories. In addition, SHY’s synaptic renormalization raises an unsolved dilemma that clashes with the accepted memory storage mechanism exclusively based on modifications of synaptic strength. This difficulty may be bypassed by the assumption that SWS-processed memories are stored again by REM sleep in brain subnuclear quantum particles. Storing of memories in quantum particles may also occur in other vigilance states. Hints are provided on ways to subject the quantum hypothesis to experimental tests.

Introduction: Many evidences have suggested that the Basolateral Amygdala (BLA) are probably involved in emotional learning and modulation of spatial memory processes. The aim of this present study was assessment of the effect of reversible abolition of BLA on spatial memory processes in a place avoidance learning model in a stable environment. Methods and Materials: Long-Evans strain rats (280-320 gr.) were selected and cannulae aimed at the BLA were surgically implanted bilaterally. The mice were trained to avoid a 60° segment of the arena by punishing with a mild foot shock upon entering the area. The punished sector was defined by room cues during the place avoidance training, which occurred in a single 30-min session and the avoidance memory was assessed during a 30-min extinction trial after 24 hours. The time of the first entry and the number of entrances into the punished sector during extinction were used to measure the place avoidance memory. Bilateral injections of Tetrodotoxin (5ng/0.6ml per side) were used to inactivate the BLA 60 min before acquisition, immediately, 60 and 120 min after training, or 60 min before the retrieval test. Control mice were injected saline at the same time. Results : The results indicated that acquisition, consolidation (immediately, 60 min after training) and retrieval of spatial memory in stable arena were impaired (p0.05). Conclusion: We conclude that the Basolateral Amygdala (BLA) modulate spatial memory processes in place avoidance learning model in stable arena and this effect in regard to consolidation is time dependent.

Consolidation processes, involving synaptic and systems level changes, are suggested to stabilize memories once they are formed. At the synaptic level, dendritic structural changes are associated with long-term memory storage. At the systems level, memory storage dynamics between the hippocampus and anterior cingulate cortex (ACC) may be influenced by the number of sequentially encoded memories. The present experiment utilized Golgi-Cox staining and neuron reconstruction to examine recent and remote structural changes in the hippocampus and ACC following training on three different behavioural procedures.Rats were trained on one hippocampal-dependent task only (a water maze task), two hippocampal-dependent tasks (a water maze task followed by a radial arm maze task), or one hippocampal-dependent and one non-hippocampal-dependent task (a water maze task followed by an operant conditioning task). Rats were euthanized recently or remotely. Brains underwent Golgi-Cox processing and neurons were reconstructed using Neurolucida software (MicroBrightField, Williston, VT, USA). Rats trained on two hippocampal-dependent tasks displayed increased dendritic complexity compared to control rats, in neurons examined in both the ACC and hippocampus at recent and remote time points. Importantly, this behavioural group showed consistent, significant structural differences in the ACC compared to the control group at the recent time point. These findings suggest that taxing the demand placed upon the hippocampus, by training rats on two hippocampal-dependent tasks, engages synaptic and systems consolidation processes in the ACC at an accelerated rate for recent and remote storage of spatial memories.

New World primates play an important role in biomedical research. However, the literature still lacks information on many structural features of the brain in these species, particularly structures of the hippocampal formation that are related to long-term memory storage. This study was designed to provide information, for the first time, about the distribution and number of neurons expressing parvalbumin-immunoreactivity (PV-I) in the subregions of the hippocampal formation in Cebus apella, a New World primate species commonly used in biomedical research. Our results revealed that for several morphometric variables, PV-I cells differ significantly among the subregions CA1, CA2, CA3, and the hilus. Based upon our findings and those of other studies, we hypothesize that the proportional increase from monkeys to humans in PV-I cell density within CA1 is a factor contributing to the evolution of increased memory formation and storage. Am. J. Primatol. 77:449-461, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]