Memory Modules Research Articles

Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling

The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth’s surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet’s robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net’s spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows.

Exploring individual differences in amygdala-mediated memory modulation.

Amygdala activation by emotional arousal during memory formation can prioritize events for long-term memory. Building upon our prior demonstration that brief electrical stimulation to the human amygdala reliably improved long-term recognition memory for images of neutral objects without eliciting an emotional response, our study aims to explore and describe individual differences and stimulation-related factors in amygdala-mediated memory modulation. Thirty-one patients undergoing intracranial monitoring for intractable epilepsy were shown neutral object images paired with direct amygdala stimulation during encoding with recognition memory tested immediately and one day later. Adding to our prior sample, we found an overall memory enhancement effect without subjective emotional arousal at the one-day delay, but not at the immediate delay, for previously stimulated objects compared to not stimulated objects. Importantly, we observed a larger variation in performance across this larger sample than our initial sample, including some participants who showed a memory impairment for stimulated objects. Of the explored individual differences, the factor that most accounted for variability in memory modulation was each participant's pre-operative memory performance. Worse memory performance on standardized neuropsychological tests was associated with a stronger susceptibility to memory modulation in a positive or negative direction. Sex differences and the frequency of interictal epileptiform discharges (IEDs) during testing also accounted for some variance in amygdala-mediated memory modulation. Given the potential and challenges of this memory modulation approach, we discuss additional individual and stimulation factors that we hope will differentiate between memory enhancement and impairment to further optimize the potential of amygdala-mediated memory enhancement for therapeutic interventions.

Targeted memory reactivation during sleep improves emotional memory modulation following imagery rescripting

Targeted Memory Reactivation (TMR) during sleep benefits memory integration and consolidation. In this pre-registered study, we investigated the effects of TMR applied during non-rapid eye movement (NREM) sleep following modulation and updating of aversive autobiographical memories using imagery rescripting (ImR). During 2–5 nights postImR, 80 healthy participants were repeatedly presented with either idiosyncratic words from an ImR updated memory during sleep (experimental group) or with no or neutral words (control groups) using a wearable EEG device (Mobile Health Systems Lab-Sleepband, MHSL-SB) [1] implementing a close-loop cueing procedure. Multivariate analysis were conducted to assess change score trajectories in five key emotional memory characteristics (positive and negative valence, emotional distress, arousal, and vividness) across assessments (timepoints, t) and between the study groups (TMR condition). While ImR showed significant effects on all memory characteristics (d = 0.76–1.66), there were significant additional improvements in the experimental group. Memories were significantly less vivid and afflicted with less emotional distress and arousal following ImR-words cueing. TMR during sleep in individuals’ homes was feasible and further improved some ImR’s adaptive memory effects. If replicated in clinical samples, TMR may be utilized to augment the effects of ImR and other clinical memory modulation procedures and create personalized treatment options. Such advances in emotional memory treatments are direly needed, as aversive memories are a salient feature across mental disorders, such as post-traumatic stress disorder (PTSD).

Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network

An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.

FC23 High induction dosing of risankizumab in patients with moderate-to-severe plaque leads to marked suppression of resident memory T cell number and function in resolved psoriatic skin

Abstract Risankizumab, a humanized monoclonal antibody that specifically inhibits IL-23, is approved to treat moderate-to-severe plaque psoriasis. In Phase 2, double-blinded, single-centre KNOCKOUT (NCT05283135) study, we evaluated higher-than-approved risankizumab induction doses (300 and 600 mg) for efficacy measures and modulation of lesional resident memory T cells (TRM), cells responsible for psoriasis recurrences. A total of 20 patients were enrolled, and 16 patients completed Week 52. Patients were randomized 1:1 to receive subcutaneous risankizumab 300 or 600 mg at Weeks 0, 4, and 16, with no further dosing. Static Physician’s Global Assessment (sPGA) was monitored throughout the study and reported here with modified non-responder imputation. At Week 16, 94.4% [n = 17, 95% confidence interval, CI (72.7, 99.9)] of all patients achieved sPGA 0/1 and 66.7% [n = 12, 95% CI (41.0, 86.7)] achieved sPGA 0. At Week 52, 36 weeks after the last dose, sPGA 0/1 and sPGA 0 responses were achieved by 61.1% [n = 11, 95% CI (35.7, 82.7)] and 44.4% [n = 8, 95% CI (21.5, 69.2)] of all patients, respectively. We also performed single-cell RNA-sequencing of biopsy samples at baseline and at Week 52 from both dose groups. In lesions of patients receiving the higher dose (600 mg) of risankizumab, the number of CD8+ tissue-resident memory T (TRM) cells was markedly reduced, with more marked suppression of the intercellular communication network between TRM and keratinocytes compared with the lower dose (300 mg) group. A shift towards increased crosstalk between Tregs and TRM, was noted, particularly in the 600 mg dose group. Mechanistically, the IL-23 blockade also impaired the expression of IL15 and IL7 in keratinocytes, which are known survival factors for CD8+ TRM. Furthermore, keratinocyte-derived TNFSF15 (TL1A), a known inflammatory signal for TH17 maintenance, was strongly attenuated in the 600 mg dose group compared with the 300 mg dose group. These findings suggest that the high and prolonged therapeutic clinical responses seen with high induction doses of risankizumab may be explained by the marked suppression of TRM number and function.

DEVELOPMENT OF A PROGRAM FOR CALCULATING THE PREDICTED OPERATION TIME OF GAS FILTERS

Objective: Development of software to calculate the predicted duration of protective action of gas filters for gas masks and respirators under specific operating conditions. Methods: The program for determining the predicted breakthrough time of gas filters was created using the integrated development environment Visual Studio Code. The algorithm embedded in the program is based on the mathematical equations of Wheeler-Jonas and the conversion of concentrations of chemical compounds into different dimensions. The real breakthrough time of gas filters was established on a laboratory stand for determining the absorption capacity, which meets the requirements of DSTU EN 14387:2021, and also provides the opportunity to use four test substances during testing, such as cyclohexane, sulfur dioxide, hydrogen sulfide or ammonia. Results: A software package was developed to calculate the predicted breakthrough time of gas filters for gas masks and respirators under specific operating conditions. It consists of four modules: initial data input, memory, output and visualization of information, and calculation of breakthrough time of gas filters. An algorithm for calculating the predicted level of filters breakthrough time was developed, which includes five steps: determining the hazard, concentration of harmful substances, required respirator protection class, humidity and air temperature, mode of operation; entering initial data for calculation; preliminary selection of a filter from the specified catalog located in the memory module; calculation of filter capacity and analysis with permissible values of the chemical substance with the calculation of breakthrough time; visualization of results and their storage in the memory module. Novelty: Establishing the dependency of the predicted breakthrough time of FRPA-G A1, B1, K1 filters on varying concentrations of chemicals in the air and the type of work performed, which allowed identifying a five-fold reduction in filter operation time during heavy work compared to light work. Practical Value: Results obtained by comparing the predicted breakthrough time using the developed software product with the programs of personal protective equipment manufacturers showed an error not exceeding 10%, due to the lack of accounting for uneven wear of the filter filler across its surface. Keywords: adsorption, gas filter, software, breakthrough time, substance concentration.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

A Respiratory Signal Monitoring Method Based on Dual‐Pathway Deep Learning Networks in Image‐Guided Robotic‐Assisted Intervention System

ABSTRACTBackgroundPercutaneous puncture procedures, guided by image‐guided robotic‐assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.MethodsWe developed an IGRI system with a coded structured light camera and a binocular camera. Our system incorporates dual‐pathway deep learning networks, combining convolutional long short‐term memory (ConvLSTM) and point long short‐term memory (PointLSTM) modules for real‐time respiratory signal monitoring.ResultsOur in‐house dataset experiments demonstrate the superior performance of the proposed network in accuracy, precision, recall and F1 compared to separate use of PointLSTM and ConvLSTM for respiratory pattern classification.ConclusionIn our IGRI system, a respiratory signal monitoring module was constructed with a binocular camera and dual‐pathway deep learning networks. The integrated respiratory monitoring module provides a basis for the application of respiratory gating technology to IGRI systems and enhances surgical safety by security mechanisms.

EKFNet: edge-based Kalman filter network for real-time EEG signal denoising

Objective.Signal denoising methods based on deep learning have been extensively adopted on electroencephalogram devices. However, they are unable to deploy on edge-based portable or wearable (P/W) electronics due to the high computational complexity of the existed models. To overcome such issue, we propose an edge-based lightweight Kalman filter network (EKFNet) that does not require manual prior knowledge estimation.Approach.Specifically, we construct a multi-scale feature fusion module to capture multi-scale feature information and implicitly compute the prior knowledge. Meanwhile, we design an adaptive gain estimation module that incorporates long short-term memory and sequential channel attention module to dynamically predict the Kalman gain. Furthermore, we present an optimization strategy utilizing operator fusion and constant folding to reduce the model's computational overhead and memory footprint.Main results. Experimental results show that the EKFNet reduces the sum of the square of the distances by at least 12% and improves the cosine similarity by at least 2.2% over the state-of-the-art methods. Besides, the model optimization shortens the inference time by approximately 3.3×. The code of our EKFNet is available athttps://github.com/cathnat/EKFNet.Significance.By integrating Kalman filter with deep learning, the approach addresses the parameter-setting challenges in traditional algorithms while reducing computational overhead and memory consumption, which exhibits a good tradeoff between algorithm performance and computing power.

Understanding the mechanisms of lateral parietal memory modulation in Alzheimer's Disease

Understanding the mechanisms of lateral parietal memory modulation in Alzheimer's Disease

Physics-guided TL-LSTM network for early-stage degradation trajectory prediction of lithium-ion batteries

Predicting early degradation trajectories of lithium-ion batteries is crucial in enhancing system reliability and promoting battery technology advancement. Existing data-driven methods often require large amounts of historical data and similar cycle information, which are not easily accessible in real-world applications. To this end, a physics-guided TL-LSTM network is proposed for degradation trajectory prediction, which integrates the physical degradation property into a transfer learning framework. Specifically, we first build a backbone network that combines a Long Short-Term Memory (LSTM) module and a Fully Connected (FC) module to perform fine-grained model training on the source domain. Then, the well-trained LSTM module is frozen and transferred to the target domain, and the FC module is fine-tuned using 30% of its early-cycle data. The entire network optimization process is guided by two inherent properties of battery degradation. Extensive comparative analysis demonstrates that our proposed approach outperforms existing methods in predicting early degradation trajectories.

Ketamine differentially affects implicit and explicit memory processes in rats.

Ketamine, a non-competitive NMDA receptor antagonist, produces antidepressant effects at subanesthetic doses. The therapeutic effect, however, is often accompanied by cognitive side effects, including memory impairments. Yet, the specific effects of ketamine on different processes of implicit and explicit memory remain to be elucidated. We examined the effect of an antidepressant dose of ketamine (10mg/kg, IP) on the encoding, retrieval, and modulation processes of fear memory and spatial memory in adult Wistar rats. Ketamine was administered before the fear acquisition, retrieval, or extinction procedures in a Pavlovian fear conditioning task. In another set of experiments, it was administered before the training, probe trial, or reversal training phases of the Morris Water Maze (MWM). The antidepressant dose of ketamine partially impaired fear extinction when administered before the acquisition or retrieval. In contrast, it facilitated memory modulation and decreased the escape latency in the first day of reversal training in the MWM when administered before the training or reversal training sessions. Encoding or retrieval performance in either type of memory was not affected. These findings show that ketamine does not impair the acquisition or retrieval processes of cued fear or spatial memory; but exerts differential effects on memory modulation of these implicit and explicit memory paradigms, by disrupting fear extinction and facilitating reversal spatial learning.

Federated Learning‐Based Mobile Traffic Prediction in Satellite‐Terrestrial Integrated Networks

ABSTRACTIntroductionWith the development and integration of satellite and terrestrial networks, mobile traffic prediction has become more important than before, which is the basis for service provision and resource scheduling when supporting various vertical applications. However, existing traffic prediction methods, especially deep learning‐based methods, require massive data for model training. Due to data privacy concerns, mobile traffic data are not easily shared among different parties, making it difficult to obtain a precise prediction model.MethodsTo mitigate the data leakage risk, a federated learning framework is proposed in this study for mobile traffic prediction in satellite‐terrestrial integrated networks to achieve a tradeoff between data privacy and prediction accuracy. In the proposed framework, local models are trained in base stations on the ground, and a global model is aggregated in the satellite edge server in space.ResultsA deep learning‐based prediction model with an adaptive graph convolutional network (AGCN) and long short‐term memory (LSTM) modules is proposed and validated in numerical experiments, which achieves the lowest prediction error with a real‐world traffic dataset when compared with other graph neural network (GNN) variants in the federated learning setting.ConclusionNumerical experiments with a real‐world mobile traffic dataset demonstrate the effectiveness of the proposed approach, which outperforms other GNN variants with lower prediction errors.

Memristive Neural Network Circuit of Negative Emotion Inhibition With Self‐Repair and Memory

ABSTRACTThe generated mechanism of negative emotion involves the prefrontal cortex, hippocampus, and amygdala in the brain. Based on the biological mechanism, this paper proposes a memristive neural network circuit of negative emotion inhibition with self‐repair and memory. The proposed memristive neural network circuit consists of five modules: memory (hippocampus) module, inhibition (prefrontal cortex) module, damage detection module, repair module, and output (amygdala) module. The memory module does not respond to a small negative signal, but a large negative signal will cause the memory module to form memories. If the large negative signal repeats, the memory module will output a memory signal to the inhibition module. The inhibition module receives the negative signal and the memory signal and then generates an inhibition signal. When the negative signal is small, the inhibition module outputs normally. When the large negative signal is applied for the first time, the inhibition module becomes damaged and the output is abnormal. As the memristor in the inhibition module has exceeded its damaged threshold, the damage detection module will generate a damaged signal to the repair module. The repair module will restore the memristance of the memristor in the inhibition module after receiving the damaged signal. If the large negative signal is applied again, the memory signal from the memory module will help the inhibition module to output normally. The output module receives the negative signal and the inhibition signal and then generates the negative emotion signal appropriately. The PSPICE simulation results show that the hippocampus generates memories after learning and transmits them to the prefrontal cortex, and then the prefrontal cortex inhibits the amygdala to generate the negative emotion appropriately. The proposed memristive neural network circuit can be applied to the robots, which can flexibly alter the intensity of the negative emotion expressed by the robots.

Video Anomaly Detection Based on Global–Local Convolutional Autoencoder

Video anomaly detection (VAD) plays a crucial role in fields such as security, production, and transportation. To address the issue of overgeneralization in anomaly behavior prediction by deep neural networks, we propose a network called AMFCFBMem-Net (appearance and motion feature cross-fusion block memory network), which combines appearance and motion feature cross-fusion blocks. Firstly, dual encoders for appearance and motion are employed to separately extract these features, which are then integrated into the skip connection layer to mitigate the model’s tendency to predict abnormal behavior, ultimately enhancing the prediction accuracy for abnormal samples. Secondly, a motion foreground extraction module is integrated into the network to generate a foreground mask map based on speed differences, thereby widening the prediction error margin between normal and abnormal behaviors. To capture the latent features of various models for normal samples, a memory module is introduced at the bottleneck of the encoder and decoder structures. This further enhances the model’s anomaly detection capabilities and diminishes its predictive generalization towards abnormal samples. The experimental results on the UCSD Pedestrian dataset 2 (UCSD Ped2) and CUHK Avenue anomaly detection dataset (CUHK Avenue) demonstrate that, compared to current cutting-edge video anomaly detection algorithms, our proposed method achieves frame-level AUCs of 97.5% and 88.8%, respectively, effectively enhancing anomaly detection capabilities.

Self-Powered Potentiometric Sensor with Relational Operation Function to Capture Concentration Excursions.

Self-powered potentiometric sensors spontaneously respond to activity changes of target species without the need for an external power source. Here, a self-powered potentiometric sensing approach is described that may store concentration perturbations that occur before the sensor readout through a combination of capacitors and diodes. Two channels, termed "more than" and "less than" operators, are utilized as memory modules in the sensor circuit to record positive and negative concentration excursions, respectively. Each channel is constructed with a capacitor-diode pair in which each diode is connected to a capacitor in the opposite direction to prevent unwanted capacitor discharge. With this design, only potential variations that agree with the polarity of the diode may pass and be stored in the capacitor. A limitation of the principle is that the conductivity of the diode is very small if the voltage across it diminishes over time as it approaches the equilibrium value. To address this, the forward voltage is increased by about 1 V by switching from an initial Ag/AgCl reference electrode (RE) to a Zn/Zn2+ element. The device may be used to monitor whether a concentration excursion has occurred in the time leading up to the signal readout in a semiquantitative manner. The approach also differentiates pH excursions of different durations (20, 40, 60 min). As an example, four different pH excursions of 20 min duration were successfully distinguished in river water samples with amplitudes of 1 to 4 pH units relative to the case without pH perturbation.

Attention and the forward testing effect.

Memory retrieval affects subsequent memory in both positive (e.g., the testing effect) and negative (e.g., retrieval-induced forgetting [RIF]) ways, and can be contrasted with other forms of memory modification (e.g., study-based encoding). Divided attention substantially impairs study-based encoding but has a modest effect on retrieval. What of the subsequent learning consequences of retrieval? Earlier studies indicate that certain positive effects (i.e., the testing effect) are not reduced by distraction, whereas negative effects (i.e., RIF) are eliminated. The present study assessed an indirect (positive) effect of retrieval-the forward testing effect (FTE), in which prior retrieval or retrieval attempts enhance subsequent learning. Two experiments examined the role of attention in both the standard FTE and the pretesting effect. In Experiment 1, participants learned three study lists through retrieval practice or restudy, followed by a fourth study list. Prior retrieval practice enhanced subsequent new learning more than restudy (i.e., the standard FTE), and to a similar degree under full attention (FA) and divided attention (DA). In Experiment 2, participants learned cue-target word pairs by either studying the pair or guessing the target when shown a cue (i.e., pretesting) followed by the correct pair. Pretesting enhanced memory more than just studying to a similar degree under FA and DA. In sum, both forms of the FTE were unaffected by distraction, indicating that these positive consequences of retrieval are not based on controlled processes but instead appear to be relatively obligatory consequences of retrieval (or retrieval attempts). These results also have relevance for specific accounts of the standard FTE and the pretesting effect.

Multi-module echo state network with variable skip length for chaotic time series prediction

Echo state networks (ESNs) have been extensively applied in time series prediction problems. However, the memory-nonlinearity trade-off problem severely limits the ability of ESNs to deal with chaotic time series prediction problems. In this study, a multi-module echo state network with variable skip length (MESN-VSL) is proposed to address this problem. First, the reservoir is divided into a nonlinear mapping module and multiple linear memory modules based on the idea of memory and nonlinearity separation. This idea can effectively balance the memory-nonlinearity trade-off problems. Second, a multi-module mechanism with skip length is put forward to model the characteristics of chaotic time series. The skip length and the number of linear memory modules of the MESN-VSL model are automatically determined based on the idea of phase-space reconstruction. Finally, the experimental results further demonstrate that the MESN-VSL model is superior to some existing models in chaotic time series prediction.

Memory-enhanced hierarchical transformer for video paragraph captioning

Video paragraph captioning aims to describe a video that contains multiple events with a paragraph of generated coherent sentences. Such a captioning task is full of challenges since the high requirements for visual–textual relevance and semantic coherence across the captioning paragraph of a video. In this work, we introduce a memory-enhanced hierarchical transformer for video paragraph captioning. Our model adopts a hierarchical structure, where the outer layer transformer extracts visual information from a global perspective and captures the relevancy between event segments throughout the entire video, while the inner layer transformer further mines local details within each event segment. By thoroughly exploring both global and local visual information at the video and event levels, our model can provide comprehensive visual feature cues for promising paragraph caption generation. Additionally, we design a memory module to capture similar patterns among event segments within a video, which preserves contextual information across event segments and updates its memory state accordingly. Experimental results on two popular datasets, ActivityNet Captions and YouCook2, demonstrate that our proposed model can achieve superior performance, generating higher quality caption while maintaining consistency in the content of video.

Effects of NMDA glutamatergic receptors pharmacological stimulation of the ventral tegmental area on the memory deficits induced by maternal deprivation

Maternal deprivation (MD) is a potent stressor during early life and can lead to behavioral changes during adulthood. Several neurochemical mechanisms underlying MD-induced stress have been proposed; among them is the damage caused to dopaminergic neurons in the ventral tegmental area (VTA). We hypothesized that pharmacological stimulation of dopaminergic neurons in VTA by the infusion of an N-Methyl-D-Aspartate (NMDA) receptors agonist (used considering the wide distribution of these glutamatergic receptors in the VTA neurons) can reverse MD-induced memory deficits. Here, we demonstrated that MD affects male and female rats distinctly, with females being more resilient to early-life stress. Furthermore, NMDA pharmacological stimulation of the VTA promotes object recognition (OR) memory persistence in male and female non-MD rats. In males, infusion of NMDA into the VTA immediately after the learning session reverses recognition memory deficits related to MD. Although MD female rats have not shown deficits in OR memory consolidation, the NMDA infusion immediately after the learning session promotes memory persistence. We verified that MD leads to memory deficits in adult male rats, while the females are resilient to early life stress. Furthermore, NMDA pharmacological stimulation of dopaminergic VTA neurons reveals the dopaminergic modulation of OR memory in MD rats, even in females that did not exhibit memory deficits.