Memory Mechanisms Research Articles

Multielement Filament Memristor Enabling Multifunctional Neuromorphic Device

AbstractMemristors exhibit changes in internal resistance in response to external voltage, introducing new functionalities to electronic devices. This enables diverse applications in non‐volatile memory, neuromorphic devices, sensors, and computing systems, highlighting their growing importance in electronics. These applications leverage various mechanisms underlying memristors. Therefore, understanding these mechanisms and discovering new memristive mechanisms are essential for overcoming implementation challenges and developing emerging applications. Here, a new type of memristor is introduced comprising an Ag/Ag:Cu‐islands/HfO2/Pt structure, characterized by a hybrid mechanism and its potential for multifunctional applications. The memristor combines the metallic filament (Ag/Cu alloy) of the electrochemical metallization (ECM) mechanism with the oxygen vacancy filament of the valence change memory (VCM) mechanism, achieving both the high on/off ratio of ECM and the analog characteristics of VCM with enhanced reliability. Both resistive and threshold switching characteristics are shown by controlling the compliance current, making the device applicable to artificial synapses and neurons. Notably, this device exhibits heat‐responsive nociceptor characteristics, positioning it as a promising candidate for next‐generation neuromorphic devices.

GENA-LM: a family of open-source foundational DNA language models for long sequences

Abstract Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To address this need, we introduce GENA language model (GENA-LM), a suite of transformer-based foundational DNA language models capable of handling input lengths up to 36 000 base pairs. Notably, integrating the newly developed recurrent memory mechanism allows these models to process even larger DNA segments. We provide pre-trained versions of GENA-LM, including multispecies and taxon-specific models, demonstrating their capability for fine-tuning and addressing a spectrum of complex biological tasks with modest computational demands. While language models have already achieved significant breakthroughs in protein biology, GENA-LM showcases a similarly promising potential for reshaping the landscape of genomics and multi-omics data analysis. All models are publicly available on GitHub (https://github.com/AIRI-Institute/GENA_LM) and on HuggingFace (https://huggingface.co/AIRI-Institute). In addition, we provide a web service (https://dnalm.airi.net/) allowing user-friendly DNA annotation with GENA-LM models.

The legacies of A. O. Dennis Willows and Peter A. Getting: neuroscience research using Tritonia.

This review was inspired by a January 2024 conference held at Friday Harbor Laboratories, WA, honoring the pioneering work of A.O. Dennis Willows, who initiated research on the sea slug Tritonia diomedea (now T. exsulans). A chance discovery while he was a student at a summer course there has, over the years, led to many insights into the roles of identified neurons in neural circuits and their influence on behavior. Among Dennis's trainees was Peter Getting, whose later groundbreaking work on central pattern generators profoundly influenced the field and included one of the earliest uses of realistic modeling for understanding neural circuits. Research on Tritonia has led to key conceptual advances in polymorphic or multifunctional neural networks, intrinsic neuromodulation, and the evolution of neural circuits. It also has enhanced our understanding of geomagnetic sensing, learning and memory mechanisms, prepulse inhibition, and even drug-induced hallucinations. Although the community of researchers studying Tritonia has never been large, its contributions to neuroscience have been substantial, underscoring the importance of examining a diverse array of animal species rather than focusing on a small number of standard model organisms.

Enhancing Cybersecurity Attack Detection Using Multiplayer Battle Game Optimizer with Hybridization of Deep Learning Models

Cybersecurity is advancing and the rate of cybercrime, which is always rising. Advanced attacks are measured as the novel normal as they are one of the more normal and extensive. Cybersecurity threats have risen promptly in many areas like healthcare, smart homes, energy, automation, agriculture, and industrial processes. An intrusion detection system (IDS) discovers intrusions by analyzing attack designs or mining signatures from system packets. To assess an IDS model, use Machine Learning (ML) and deep learning (DL) approaches for recognizing data traffic into malicious and healthy. ML and DL techniques has earned an extensive interest on countless applications and domains of study, mostly in Cybersecurity. With computing power and hardware becoming more available, ML and DL systems can be employed in order to classify and analyze corrupt actors from a massive group of accessible data. This manuscript presents an Enhancing Detection of Cybersecurity Attack Using Multiplayer Battle Game Optimizer with Hybrid Deep Learning (EDCA-MBGOHDL) technique. The main intention of the EDCA-MBGOHDL technique is to provide a robust framework for cyberattack detection using deep learning integrated with a hyperparameter tuning approach. At first, the feature selection process is implemented by applying improved Harris hawk optimization (IHHO) algorithm for ensuring that only the most relevant features are fed into the model. Furthermore, the hybrid of convolutional neural network, bidirectional long short-term memory and attention mechanism (CNN-BiLSTM-AM) model is employed for the classification of cybersecurity threats. Eventually, the multiplayer battle game optimizer (MBGO) algorithm adjusts the hyperparameter values of the CNN-BiLSTM-AM classifier optimally and outcomes in greater classification performance. The wide range of analysis of the EDCA-MBGOHDL technique takes place using a benchmark dataset. The outcomes pointed out the superior performance of the EDCA-MBGOHDL system across existing models

Perineuronal nets on CA2 pyramidal cells and parvalbumin-expressing cells differentially regulate hippocampal dependent memory.

Perineuronal nets (PNNs) are a specialized extracellular matrix that surround certain populations of neurons, including (inhibitory) parvalbumin (PV) expressing-interneurons throughout the brain and (excitatory) CA2 pyramidal neurons in hippocampus. PNNs are thought to regulate synaptic plasticity by stabilizing synapses and as such, could regulate learning and memory. Most often, PNN functions are queried using enzymatic degradation with chondroitinase, but that approach does not differentiate PNNs on CA2 neurons from those on adjacent PV cells. To disentangle the specific roles of PNNs on CA2 pyramidal cells and PV neurons in behavior, we generated conditional knockout mouse strains with the primary protein component of PNNs, aggrecan (Acan), deleted from either CA2 pyramidal cells (Amigo2 Acan KO) or from PV cells (PV Acan KO). Male and female animals of each strain were tested for social, fear, and spatial memory, as well as for reversal learning. We found that Amigo2 Acan KO animals, but not PV Acan KO animals, had impaired social memory and reversal learning. PV Acan KOs, but not Amigo2 Acan KOs had impaired contextual fear memory. These findings demonstrate independent roles for PNNs on each cell type in regulating hippocampal-dependent memory. We further investigated a potential mechanism of impaired social memory in the Amigo2 Acan KO animals and found reduced input to CA2 from the supramammillary nucleus (SuM), which signals social novelty. Additionally, Amigo2 Acan KOs lacked a social novelty-related local field potential response, suggesting that CA2 PNNs may coordinate functional SuM connections and associated physiological responses to social novelty.Significance statement Perineuronal nets (PNNs) surround both inhibitory parvalbumin (PV)-expressing neurons and excitatory CA2 pyramidal neurons, but previous studies using enzymatic degradation cannot differentiate the relative roles of PNNs in the two populations. By conditionally deleting aggrecan (Acan) from CA2 pyramidal neurons without affecting PNNs on PV cells, and vice versa, we discovered distinct roles of PNNs on each cell type in behavior. Social memory, which requires CA2 activity, was impaired in mice lacking CA2 PNNs, but not in those lacking PV PNNs. Cognitive flexibility, assessed by reversal learning was also impaired in mice lacking CA2 PNNs, whereas contextual fear memory was impaired in those lacking PV PNNs. Thus, PNNs on each cell type differentially contribute to different forms of hippocampal-dependent memory.

The Impacts of Psychology and Neuroscience on Consumers’ Behaviour

The intersection of psychology and neuroscience has revolutionized our understanding of consumer behavior, offering profound insights into the cognitive and emotional processes that drive decision-making. This research explores the multifaceted impacts of psychological theories and neuroscientific advancements on consumer behavior, emphasizing how factors such as perception, memory, emotions, and neural mechanisms shape purchasing decisions. By integrating behavioral economics, cognitive neuroscience, and social psychology, the study highlights the role of implicit biases, heuristics, and neurochemical responses in influencing consumer preferences. Furthermore, the research examines the implications of neuromarketing techniques, such as brain imaging and biometric analysis, in predicting and shaping consumer behavior. Ethical considerations, including privacy concerns and the potential manipulation of consumer choices, are critically addressed to ensure responsible applications of these insights. By synthesizing theoretical models with empirical evidence, this study provides a comprehensive framework for leveraging psychology and neuroscience to enhance marketing strategies, improve consumer satisfaction, and foster ethical business practices.

Pulse Dynamics in Reduced Graphene Oxide Electrolyte‐Gated Transistors: Charge Memory Effects and Mechanisms Governing the Ion‐To‐Electron Transduction

AbstractElectrolyte‐gated transistors (EGTs) are widely employed in bioelectronics due to their ability to bridge ionic and electronic phenomena in a single device. Among potential materials, reduced graphene oxide (rGO) has gained significant attention due to its ambipolar current response, quantum capacitance, and tunable conductivity. However, the rGO EGT dynamic behavior remains significantly unexplored. Here, the time‐dependent response of rGO EGTs is systematically investigated under gate voltage pulsing across different time scales (10 ms to 40 s) and amplitudes (up to |±0.8 V|). Significant charge memory is observed, particularly for long (40 s) pulses at 0.8 V, with effects also evident for shorter (1 s) and weaker stimuli (0.6 V). Multiple low‐level (0.1 V) fast pulsing (100 ms) further demonstrate charge retention post‐stimulation. All these characteristics are attributed to a complex interplay between ion entrapment within the rGO film, electrical double‐layer formation, and charge transfer processes. The stability of rGO EGTs under prolonged bias stress is also examined, aiming to contribute to the development of more robust devices. These findings revealed the complex role of electrolyte ions and electronic carriers governing the ion‐to‐electron transduction and charge memory effects in rGO EGTs, contributing to the advancement of the next‐generation bioelectronic devices.

Chromogenic Photonic Crystal Detectors for Monitoring Small Molecule Diffusion at Solid-Solid Interfaces Using Stimuli-Responsive Shape Memory Polymers.

In situ monitoring of small molecule diffusion at solid-solid interfaces is challenging, even with sophisticated equipment. Here, novel chromogenic photonic crystal detectors enabled by integrating bioinspired structural color with stimuli-responsive shape memory polymer (SMP) for detecting trace amounts of small molecule interfacial diffusion are reported. Colorless macroporous SMP membranes with deformed macropores can recover back to the "memorized" photonic crystal microstructures and the corresponding iridescent structural colors when triggered by diffused small molecules. Systematic experimental and theoretical investigations using various microscopes, optical spectroscopy and modeling, spatio-resolved energy-dispersive X-ray spectroscopy, and theoretical diffusion calculations confirm the diffusion-induced shape memory and chromogenic mechanisms. Importantly, proof-of-concept sensing of temporospatial-resolved diffusion of bioactive ingredients used in drug delivery, including anti-inflammatory methyl salicylate in pain relieving patches and vitamin E barriers loaded in contact lens, and phthalates plasticizers in commercial PVC products has been demonstrated. These innovative detectors are inexpensive, reusable, and easy to operate and deploy for both qualitative and quantitative analyses, promising for opening new avenues in biomedical research, threat detection, and monitoring of plastics, food, and environmental safety. Moreover, reconfigurable photonic crystals with micrometer-scale resolution, which are of great importance in tunable and integrated nanooptics, can be fabricated by diffusion-enabled microcontact printing.

DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction

Financial time series prediction is a fundamental problem in investment and risk management. Deep learning models, such as multilayer perceptrons, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), have been widely used in modeling time series data by incorporating historical information. Among them, LSTM has shown excellent performance in capturing long-term temporal dependencies in time-series data, owing to its enhanced internal memory mechanism. In spite of the success of these models, it is observed that in the presence of sharp changing points, these models fail to perform. To address this problem, we propose, in this article, an innovative financial time series prediction method inspired by the Deep Operator Network (DeepONet) architecture, which uses a combination of transformer architecture and a one-dimensional CNN network for processing feature-based information, followed by an LSTM based network for processing temporal information. It is therefore named the CNN–LSTM–Transformer (CLT) model. It not only incorporates external information to identify latent patterns within the financial data but also excels in capturing their temporal dynamics. The CLT model adapts to evolving market conditions by leveraging diverse deep-learning techniques. This dynamic adaptation of the CLT model plays a pivotal role in navigating abrupt changes in the financial markets. Furthermore, the CLT model improves the long-term prediction accuracy and stability compared with state-of-the-art existing deep learning models and also mitigates adverse effects of market volatility. The experimental results show the feasibility and superiority of the proposed CLT model in terms of prediction accuracy and robustness as compared to existing prediction models. Moreover, we posit that the innovation encapsulated in the proposed DeepONet-inspired CLT model also holds promise for applications beyond the confines of finance, such as remote sensing, data mining, natural language processing, and so on.

Time memory in social insects with a special focus on honey bees.

The ability to associate time and location with food sources is an evolutionary advantage for foraging animals. We find highly sophisticated time memory capabilities especially in social insects, which require efficient foraging capabilities for colony provisioning. Honey bees are perfectly suitable to study time memory mechanisms: they possess an elaborated time memory combined with a relatively simple neuronal clock network and a smaller gene set compared with the mouse model organism. This review provides a short overview majorly across insects, which have demonstrated time memory capabilities, with a focus on time-place learning, and describes basic properties as well as state-of-the-art research connecting time memory with the circadian clock at the behavioral, molecular and neuroanatomical level. Despite a long history of research on time memory of honey bees, putative connections between clock and time memory have only recently been identified and imply a rather complex regulation mechanism with multiple signaling pathways.

Heterosynaptic plasticity: one name for several phenomena

Synaptic plasticity, which refers to long-term changes in the efficiency of synaptic transmission in the form of potentiation and depression, is thought to be a cellular mechanism of learning and memory. Long-term potentiation and depression can be induced under a variety of experimental conditions using different induction protocols. One such example is a protocol that follows Hebb’s rule, where induction of plasticity requires paired activation of a pre- and postsynaptic neuron that occur within a narrow temporal window relative to each other. Such plasticity is called homosynaptic plasticity because the same (homo-, Greek prefix meaning “same, identical”) synapses that participated in the induction of plasticity undergo long-term changes. However, as numerous experiments have shown, synapses that were inactive during the induction of plasticity also undergo long-term changes. This process has been termed heterosynaptic (hetero – “other, different”) plasticity in mammalian studies. Historically, however, the term heterosynaptic plasticity first appeared in studies of mollusks, where plastic changes in synaptic transmission were caused by a combination of stimulation of “weak” and “strong” synaptic inputs. As was later shown, the potentiating effect of stimulating the “strong” input in this case was associated with the release of neuromodulators, primarily serotonin. This type of plasticity was later demonstrated in mammals, where it was termed modulatory plasticity. The review considers different types of heterosynaptic plasticity, cellular and molecular mechanisms of its induction and maintenance, and explains the reasons for some terminological confusion related to this phenomenon in the literature.

3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.

Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.

The Application Progress of Optogenetics in Neuroscience and Behavioral Studies

Optogenetics, as an emerging interdisciplinary technology, has enabled the precise manipulation of neuronal activity by combining optical and genetic approaches. This paper summarizes the application of optogenetics in neuroscience and behavioral research and introduces its research background and topics. The application of optogenetics in neuronal activity regulation, neural loop resolution, exploration of learning and memory mechanisms, and treating neurological diseases were focused on. By combining a literature review and case analysis, we analyze the core principles of optogenetics and the technical points of optogenetics and demonstrate their potential applications in different research scenarios. The types and properties of photosensitive proteins and their application strategies in experiments are detailed, demonstrating the unique advantages of optogenetics in revealing the mysteries of neurobiological processes. The results show that optogenetic technology provides a powerful tool to reveal the mysteries of neurobiological processes, but it still faces challenges such as light transmission efficiency and gene delivery safety in practical applications. This paper summarizes the advantages and limitations of optogenetics and prospects for its future direction, providing an important reference for the continuous development of neuroscience and behavioral research.

Intensity recognition of vortex ropes in draft tube of a prototype pump turbine using an optimized CNN-BiLSTM framework with multi-head self-attention mechanism

Swirling vortex rope in draft tube (DT) is a typical hydraulic instability of a pump turbine (PT) in the pumped storage plant (PSP). In view of the potential hazards of the vortex rope, accurate recognition of its intensity is of great significance to maintain the stable operation of the PT. Due to the limitations of shallow learning algorithms during intelligent recognition, an adaptive deep learning framework is innovatively proposed in this study. Firstly, the measured high-precision pressure fluctuation signals based on the prototype PT in a Chinese PSP that can reflect different intensities of vortex ropes in the DT are utilized as the input data. Secondly, a preliminary deep learning framework that integrates convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and multi-head self-attention mechanism (MHSAM) is constructed. Then, the Bayesian optimization algorithm (BOA) is utilized to adaptively determine several hyperparameters of the framework. And an adaptive BOA-CNN-BiLSTM-MHSAM framework is established to recognize different intensities of vortex ropes in the DT. Finally, the recognition performance of the proposed framework is demonstrated through comparing with other deep learning frameworks. And the recognition results illustrate that the proposed BOA-CNN-BiLSTM-MHSAM framework can be utilized to effectively recognize different intensities of vortex ropes in the DT. It will be a good technical reserve to improve the intelligent level of the monitoring system of the PSP.

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.

Enhancing text classification through novel deep learning sequential attention fusion architecture

<span lang="EN-US">Text classification is a pivotal task within natural language processing (NLP), aimed at assigning semantic labels to text sequences. Traditional methods of text representation often fall short in capturing intricacies in contextual information, relying heavily on manual feature extraction. To overcome these limitations, this research work presents the sequential attention fusion architecture (SAFA) to enhance the features extraction. SAFA combines deep long sort-term memory (LSTM) and multi-head attention mechanism (MHAM). This model efficiently preserves data, even for longer phrases, while enhancing local attribute understanding. Additionally, we introduce a unique attention mechanism that optimizes data preservation, a crucial element in text classification. The paper also outlines a comprehensive framework, incorporating convolutional layers and pooling techniques, designed to improve feature representation and enhance classification accuracy. The model's effectiveness is demonstrated through 2-dimensional convolution processes and advanced pooling, significantly improving prediction accuracy. This research not only contributes to the development of more accurate text classification models but also underscores the growing importance of NLP techniques.</span>

Targeted epigenome engineering

Cellular differentiation and homeostasis rely on complex mechanisms to control gene expression, enabling the different cell lineages of an organism to establish and then "memorize" different epigenetic states. The processes that control gene expression are centered on chromatin, a complex of DNA, histone proteins and RNA, whose structure is finely regulated. Targeted epigenomic engineering tools make it possible to interfere with and study these processes, revealing the logic of epigenetic memory mechanisms. This article reviews the main classes of targeted epigenome modification tools and illustrates how they can be used to better understand and modify the epigenome of cells, paving the way for potentially revolutionary therapeutic prospects.

Enhanced Unmanned Surface Vehicle Path Planning Based on the Pair Barracuda Swarm Optimization Algorithm: Implementation and Performance in Thousand Island Lake

The path planning problem for unmanned surface vehicles (USVs) is related to multiobjective optimization, including shortest path, minimum energy consumption, and obstacle avoidance, making it particularly complex in multi-island and multiobstacle environments such as Thousand Island Lake. An enhanced path planning method for USVs based on the pair barracuda swarm optimization (PBSO) algorithm is proposed, and the complex water environment of Thousand Island Lake is taken as an example. The PBSO algorithm simulates the social behaviour of pair barracuda innovative and deep memory mechanisms, which can enhance the algorithm’s global search ability and local optimal escape ability in high-dimensional space. The probabilistic roadmap (PRM) method was initially used to model complex environments with multiple islands and obstacles. Moreover, four evaluation indicators were proposed to evaluate the performance of the obtained path: total navigation distance (TND), number of returns (NT), average turning angle (ATA), and minimum safe distance (MSD) from obstacles. The PBSO algorithm is used to optimize the initial path to reduce frequent turns and turning amplitudes during navigation. Path planning experiments were conducted on four simulated map environments with different ranges and complexities. Compared with state-of-the-art heuristic path planning methods, our method can identify the optimal path faster and has better stability. The enhanced USV path planning method based on the PBSO algorithm provides a new path planning strategy for the practical application of USVs under the real Thousand Island Lake.

Escape Path Planning for Unmanned Surface Vehicle Based on Blind Navigation Rapidly Exploring Random Tree* Fusion Algorithm.

To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process. Additionally, inspired by blind navigation principles, we propose an active collision mechanism that relies on continuous collisions to identify obstacles and adjust the next movement direction, thereby improving the efficiency of escape path planning. We also implement an obstacle memory mechanism to prevent exploration into erroneous areas during sampling, significantly increasing the success rate of escape and reducing the path length. We validate the proposed algorithm in a dedicated MATLAB environment, comparing its performance with existing RRT, RRT*, and APF-RRT* algorithms. Experimental results indicate that the improved algorithm achieves significant enhancements in both planning speed and path length compared to the other methods.

Gallic acid attenuates lipopolysaccharide - induced memory deficits, neurochemical changes, and peripheral alterations in purinergic signaling.

Neuroinflammation is associated with many neurological disorders. Gallic acid (GA) has attracted significant attention due to its biological properties, such as neuroprotective, anti-inflammatory, and antioxidant effects. In this study, we evaluated the effects of GA in memory, TNF-α levels, oxidative stress, and activities of acetylcholinesterase (AChE), Na+, K+-ATPase and Ca2+-ATPase in the brain of mice exposed to lipopolysaccharide (LPS). Additionally, we evaluated alterations in adenine nucleotides and nucleosides in the serum. Male mice were orally pretreated with vehicle or GA (50 or 100mg/kg) for 14 days. Between days 8 and 14, the animals also received LPS injection (250µg/kg) or saline. At the end of the experimental protocol, the animals were submitted to object recognition test, euthanized and cerebral cortex, hippocampus, striatum and blood were collected. LPS induced memory deficits, which were prevented by GA treatment. GA protected against LPS-induced oxidative damage in the cerebral cortex, hippocampus and striatum by reducing reactive oxygen species and nitrite levels, while increasing total thiol content and activities of antioxidant enzymes. GA also prevented LPS-induced alterations in AChE, Na+, K+-ATPase, and Ca2+-ATPase activities in brain structures. LPS elevated TNF-α levels in the hippocampus and cerebral cortex, which were attenuated by GA treatment. Furthermore, LPS caused a reduction in ADP and AMP hydrolysis and an increase in adenosine deamination in the serum, which were also prevented by GA. The effects of GA against neuroinflammation may be attributed to its potent antioxidant and anti-inflammatory properties, which modulate various pathways, including those involved in memory mechanisms.