Remote Memory Research Articles

Distinct Roles of Medial Prefrontal Cortex Subregions in the Consolidation and Recall of Remote Spatial Memories.

It is a common belief that memories, over time, become progressively independent of the hippocampus and are gradually stored in cortical areas. This view is mainly based on evidence showing that prefrontal cortex (PFC) manipulations impair the retrieval of remote memories, while hippocampal inhibition does not. More controversial is whether activity in the medial PFC is required immediately after learning to initiate consolidation. Another question concerns functional differences among PFC subregions in forming and storing remote memories. To address these issues, we directly contrasted the effects of loss-of-function manipulations of the anterior cingulate cortex (aCC) and the ventromedial PFC, which includes the infralimbic (IL) and prelimbic (PL) cortices, before testing and immediately after training on the ability of CD1 mice to recall the hidden platform location in the Morris water maze. We injected an AAV carrying the hM4Di receptor into the PL-IL or aCC. Interestingly, pretest administrations of clozapine-N-oxide (CNO; 3 mg/kg) revealed that the aCC, but not the PL-IL, was necessary to recall remote spatial information. Furthermore, systemic post-training administration of CNO impaired memory recall at remote, but not recent, time points in both groups. These findings revealed a functional dissociation between the two prefrontal areas, demonstrating that both the PL-IL and the aCC are involved in early consolidation of remote spatial memories, but only the aCC is engaged in their recall.

Long-term retention of real-world experiences in a patient with profound amnesia

The medial temporal lobe (MTL) is known to be critical for healthy memory function, but patients with MTL damage can, under certain circumstances, demonstrate successful learning of novel information encountered outside the laboratory. Here, we describe a patient, D.C., with extensive but focal bilateral MTL damage centering primarily on his hippocampus, whose memory for real-world experiences was assessed. Tests of remote memory indicated at least some capacity to retrieve specific details. To test his anterograde memory, he was taken on a tour of the NIH Clinical Center, with unique events occurring at each of ten specific locations. His memory for these events was tested after 1 h, and again after fifteen months. Initially, D.C. could not recall having participated in the tour, even when cued with photographs of specific places he had visited. However, he achieved 90% accuracy on a forced choice recognition test of old and new objects he encountered on the tour, and his recognition of these objects remained intact over a year later when he was tested once again. Subsequent recognition memory tests using novel picture stimuli in a standard laboratory-style computer task resulted in chance-level performance across multiple test formats and stimulus categories. These findings suggest a potentially privileged role for natural learning for long-term retention in a patient with severely damaged medial temporal lobes.

Semantics of Remote Direct Memory Access: Operational and Declarative Models of RDMA on TSO Architectures

Remote direct memory access (RDMA) is a modern technology enabling networked machines to exchange information without involving the operating system of either side, and thus significantly speeding up data transfer in computer clusters. While RDMA is extensively used in practice and studied in various research papers, a formal underlying model specifying the allowed behaviours of concurrent RDMA programs running in modern multicore architectures is still missing. This paper aims to close this gap and provide semantic foundations of RDMA on x86-TSO machines. We propose three equivalent formal models, two operational models in different levels of abstraction and one declarative model, and prove that the three characterisations are equivalent. To gain confidence in the proposed semantics, the more concrete operational model has been reviewed by NVIDIA experts, a major vendor of RDMA systems, and we have empirically validated the declarative formalisation on various subtle litmus tests by extensive testing. We believe that this work is a necessary initial step for formally addressing RDMA-based systems by proposing language-level models, verifying their mapping to hardware, and developing reasoning techniques for concurrent RDMA programs.

“Remote Memory Test Updated Version” for Social Events in Japan since 2010, Using Photographs of Famous People

“Remote Memory Test Updated Version” for Social Events in Japan since 2010, Using Photographs of Famous People

Circadian Rhythms in Conditioned Threat Extinction Reflect Time-of-Day Differences in Ventromedial Prefrontal Cortex Neural Processing.

Circadian rhythms in conditioned threat extinction emerge from a tissue-level circadian timekeeper, or local clock, in the ventromedial prefrontal cortex (vmPFC). Yet it remains unclear how this local clock contributes to extinction-dependent adaptations. Here we used single-unit and local field potential analyses to interrogate neural activity in the male rat vmPFC during repeated extinction sessions at different times of day. In association with superior recall of a remote extinction memory during the circadian active phase, vmPFC putative principal neurons exhibited phasic firing that was amplified for cue presentations and diminished at transitions in freezing behavior. Coupling of vmPFC gamma amplitude to the phase of low-frequency oscillations was greater during freezing than mobility, and this difference was augmented during the active phase, highlighting a time-of-day dependence in the organization of freezing- versus mobility-associated cell assemblies. Additionally, a greater proportion of vmPFC neurons were phase-locked to low-frequency oscillations during the active phase, consistent with heightened neural excitability at this time of day. Our results suggest that daily fluctuations in vmPFC excitability precipitate enhanced neural recruitment into extinction-based cell assemblies during the active phase, providing a potential mechanism by which the vmPFC local clock modulates circuit and behavioral plasticity during conditioned threat extinction.

Synaptic rearrangement of NMDA receptors controls memory engram formation and malleability in the cortex.

Initially hippocampal dependent, memory representations rely on a broadly distributed cortical network as they mature over time. How these cortical engrams acquire stability during systems-level memory consolidation without compromising their dynamic nature remains unclear. We identified a highly responsive "consolidation switch" in the synaptic composition of N-methyl-d-aspartate receptors (NMDARs), which dictates the progressive embedding and persistence of enduring memories in the rat cortex. Cortical GluN2B subunit-containing NMDARs were preferentially recruited upon encoding of associative olfactory memory to support neuronal allocation of memory engrams. As consolidation proceeds, a learning-induced redistribution of GluN2B subunit-containing NMDARs outward synapses increased synaptic GluN2A subunit contribution and enabled stabilization of remote memories. In contrast, synaptic reincorporation of GluN2B subunits occurred during subsequent forgetting. By manipulating the surface distribution of GluN2A and GluN2B subunit-containing NMDARs at cortical synapses, we uncovered that the rearrangement of GluN2B-containing NMDARs constitutes an essential tuning mechanism that determines the fate of cortical memory engrams and controls their malleability.

Advanced Techniques of Malware Evasion and Bypass in the Age of Antivirus

The use of antivirus software as the main line of protection against growing cyber threats highlights the necessity of comprehending and resolving its limits. This study provides light on the ease of use and accessibility of tools used by hackers by carefully examining the complex terrain of malware evasion and bypass tactics. The persistent evolution of malware evasion and bypass techniques presents a significant cybersecurity challenge. The main objective is to educate users about the ever-changing hazards and provide them with the knowledge they need to properly strengthen their digital defenses. The literature analysis highlights the necessity for continued attention by establishing a strong correlation between the effectiveness of evasion strategies and their age and popularity. While modern antivirus software shows strong resistance against a range of tried-and-true techniques when updated on a regular basis, the study reveals a crucial component in its testing. This entails applying simple yet effective tweaks to well-known evasion techniques, demonstrating their capacity to fool even the most recent antivirus software. A thorough examination of malware evasion tactics, including both on-desk and in-memory approaches, is given in the methods section. Packing, obfuscators, protectors, reflective DLL injection, remote process memory injection, process hollowing, and inline hooking are all covered in detail in this paper. Subsequently, the study delves deeper into distinct evasion strategies, such defensive evasion through direct system calls and sophisticated evasion tactics, showcasing malware developers' versatility in evading antivirus and endpoint detection and response (EDR) systems.

Remote and unsupervised digital memory assessments can reliably detect cognitive impairment in Alzheimer's disease.

Remote unsupervised cognitive assessments have the potential to complement and facilitate cognitive assessment in clinical and research settings. Here, we evaluate the usability, validity, and reliability of unsupervised remote memory assessments via mobile devices in individuals without dementia from the Swedish BioFINDER-2 study and explore their prognostic utility regarding future cognitive decline. Usability was rated positively; remote memory assessments showed good construct validity with traditional neuropsychological assessments and were significantly associated with tau-positron emission tomography and downstream magnetic resonance imaging measures. Memory performance at baseline was associated with future cognitive decline and prediction of future cognitive decline was further improved by combining remote digital memory assessments with plasma p-tau217. Finally, retest reliability was moderate for a single assessment and good for an aggregate of two sessions. Our results demonstrate that unsupervised digital memory assessments might be used for diagnosis and prognosis in Alzheimer's disease, potentially in combination with plasma biomarkers. Remote and unsupervised digital memory assessments are feasible in older adults and individuals in early stages of Alzheimer's disease. Digital memory assessments are associated with neuropsychological in-clinic assessments, tau-positron emission tomography and magnetic resonance imaging measures. Combination of digital memory assessments with plasma p-tau217 holds promise for prognosis of future cognitive decline. Future validation in further independent, larger, and more diverse cohorts is needed to inform clinical implementation.

Adult-born granule cells modulate CA2 network activity during retrieval of developmental memories of the mother.

Adult-born granule cells (abGCs) project to the CA2 region of the hippocampus, but it remains unknown how this circuit affects behavioral function. Here, we show that abGC input to the CA2 of adult mice is involved in the retrieval of remote developmental memories of the mother. Ablation of abGCs impaired the ability to discriminate between a caregiving mother and a novel mother, and this ability returned after abGCs were regenerated. Chemogenetic inhibition of projections from abGCs to the CA2 also temporarily prevented the retrieval of remote mother memories. These findings were observed when abGCs were inhibited at 4-6 weeks old, but not when they were inhibited at 10-12 weeks old. We also found that abGCs are necessary for differentiating features of CA2 network activity, including theta-gamma coupling and sharp wave ripples, in response to novel versus familiar social stimuli. Taken together, these findings suggest that abGCs are necessary for neuronal oscillations associated with discriminating between social stimuli, thus enabling retrieval of remote developmental memories of the mother by their adult offspring.

Adult-born granule cells modulate CA2 network activity during retrieval of developmental memories of the mother

Adult-born granule cells (abGCs) project to the CA2 region of the hippocampus, but it remains unknown how this circuit affects behavioral function. Here, we show that abGC input to the CA2 of adult mice is involved in the retrieval of remote developmental memories of the mother. Ablation of abGCs impaired the ability to discriminate between a caregiving mother and a novel mother, and this ability returned after abGCs were regenerated. Chemogenetic inhibition of projections from abGCs to the CA2 also temporarily prevented the retrieval of remote mother memories. These findings were observed when abGCs were inhibited at 4–6 weeks old, but not when they were inhibited at 10–12 weeks old. We also found that abGCs are necessary for differentiating features of CA2 network activity, including theta-gamma coupling and sharp wave ripples, in response to novel versus familiar social stimuli. Taken together, these findings suggest that abGCs are necessary for neuronal oscillations associated with discriminating between social stimuli, thus enabling retrieval of remote developmental memories of the mother by their adult offspring.

Design and performance evaluation of UCX for the Tofu Interconnect D on Fugaku towards efficient multithreaded communication

The increasing trend of manycore processors makes multithreaded communication more important to avoid costly global synchronization among cores. One of the representative approaches that require multithreaded communication is the global task-based programming model. In the model, a program is divided into tasks, and tasks are asynchronously executed by each node, and independent thread-to-thread communications are expected. However, the Message passing interface (MPI) based approach is not efficient because of design issues. In this research, we design and implement the utofu transport layer in an abstracted communication library called Unified communication-X (UCX) for efficient remote direct memory access (RDMA) based multithreaded communication on Tofu Interconnect D. The evaluation results on Fugaku show that UCX can significantly improve the multithreaded performance over MPI, while maintaining portability between systems thanks to UCX. UCX shows about 32.8 times lower latency than Fujitsu MPI with 24 threads in the multithreaded pingpong benchmark and about 37.8 times higher update rate than Fujitsu MPI with 24 threads on 256 nodes in multithreaded GUPs benchmark.

Activation of Gs Signaling in Cortical Astrocytes Does Not Influence Formation of a Persistent Contextual Memory Engram.

Formation and retrieval of remote contextual memory depends on cortical engram neurons that are defined during learning. Manipulation of astrocytic Gq and Gi associated G-protein coupled receptor (GPCR) signaling has been shown to affect memory processing, but little is known about the role of cortical astrocytic Gs-GPCR signaling in remote memory acquisition and the functioning of cortical engram neurons. We assessed this by chemogenetic manipulation of astrocytes in the medial prefrontal cortex (mPFC) of male mice, during either encoding or consolidation of a contextual fear memory, while simultaneously labeling cortical engram neurons. We found that stimulation of astrocytic Gs signaling during memory encoding and consolidation did not alter remote memory expression. In line with this, the size of the mPFC engram population and the recall-induced reactivation of these neurons was unaffected. Hence, our data indicate that activation of Gs-GPCR signaling in cortical astrocytes is not sufficient to alter memory performance and functioning of cortical engram neurons.

Scalable Distributed Inverted List Indexes in Disaggregated Memory

Memory disaggregation separates compute (CPU) and main memory resources into disjoint physical units to enable elastic and independent scaling. Connected via high-speed RDMA-enabled networks, compute nodes can directly access remote memory. This setting often requires complex protocols with many network roundtrips as memory nodes have near-zero compute power. In this paper, we design a scalable distributed inverted list index for disaggregated memory architectures. An inverted list index maps a set of terms to lists of documents that contain this term. Current solutions either partition the index horizontally or vertically with severe limitations in the disaggregated memory setting due to data and access skew, high network latency, or out-of-memory errors. Our method partitions lists into fixed-size blocks and spreads them across the memory nodes to balance skewed accesses. Block-based list processing keeps the memory footprint of compute nodes low and masks latency by interleaving remote accesses with expensive list operations. In addition, we propose efficient updates with optimistic concurrency control and read-write conflict detection. Our experiments confirm the efficiency and scalability of our method.

SECM: Securely and efficiently connections setup using RDMA-CM

Remote Direct Memory Access (RDMA) releases the potential of the data center as it bypasses the kernel. However, establishing and managing RDMA connections is a significant challenge due to the complex topology of RDMA networks. We present SECM, a secure and efficient RDMA Communication Management (CM) library, which can be used for distributed or resilient RDMA network connection setup. The main idea is to split the RDMA-CM connection setup phase, reuse resources between different connections, and perform multiple connection setups simultaneously in a pipelined manner. At the same time, adding message authentication codes to the CM protocol enables authentication of connection requests and prevents malicious connection requests. SECM establishes 16 connections in 1.42 times the time of 1 connection, tens or hundreds of times faster than verbs. SECM is compatible with existing commercial RNICs and provides services in a user-state-driven manner, with low CPU overhead, low memory usage, and no impact on the RDMA data level.

Software-based remote memory attestation using quantum entanglement

Software-based remote memory attestation is a method for determining the state of a remote device without relying on secure hardware. In classical computing devices, the method is vulnerable to proxy and authentication attacks, because an infected device has no means of preventing the leak of its cryptographic secrets. In this paper, we demonstrate how these attacks can be mitigated by making use of quantum effects, while remaining within the class of software-based methods. In particular, we make use of entanglement and the inability of an attacker to clone qubits. Our proposed protocol is lightweight and can be implemented by near-term Quantum Computing techniques. The resulting protocol has the unique feature of resisting collusion between two dishonest devices, one of which has unbounded computational resources.

Method for scalable and performant GPU-accelerated simulation of multiphase compressible flow

Multiphase compressible flows are often characterized by a broad range of space and time scales, entailing large grids and small time steps. Simulations of these flows on CPU-based clusters can thus take several wall-clock days. Offloading the compute kernels to GPUs appears attractive but is memory-bound for many finite-volume and -difference methods, damping speedups. Even when realized, GPU-based kernels lead to more intrusive communication and I/O times owing to lower computation costs. We present a strategy for GPU acceleration of multiphase compressible flow solvers that addresses these challenges and obtains large speedups at scale. We use OpenACC for directive-based offloading of all compute kernels while maintaining low-level control when needed. An established Fortran preprocessor and metaprogramming tool, Fypp, enables otherwise hidden compile-time optimizations. This strategy exposes compile-time optimizations and high memory reuse while retaining readable, maintainable, and compact code. Remote direct memory access realized via CUDA-aware MPI and GPUDirect reduces halo-exchange communication time. We implement this approach in the open-source solver MFC [1]. Metaprogramming results in an 8-times speedup of the most expensive kernels compared to a statically compiled program, reaching 46% of peak FLOPs on modern NVIDIA GPUs and high arithmetic intensity (about 10 FLOPs/byte). In representative simulations, a single NVIDIA A100 GPU is 7-times faster compared to an Intel Xeon Cascade Lake (6248) CPU die, or about 300-times faster compared to a single such CPU core. At the same time, near-ideal (97%) weak scaling is observed for at least 13824 GPUs on OLCF Summit. A strong scaling efficiency of 84% is retained for an 8-times increase in GPU count. Collective I/O, implemented via MPI3, helps ensure the negligible contribution of data transfers (<1% of the wall time for a typical, large simulation). Large many-GPU simulations of compressible (solid-)liquid-gas flows demonstrate the practical utility of this strategy.

Phenotypic Insights Into Anti-IgLON5 Disease in IgLON5-Deficient Mice.

Anti-IgLON5 disease is an autoimmune neurodegenerative disorder characterized by various phenotypes, notably sleep and movement disorders and tau pathology. Although the disease is known to be associated with the neuronal cell adhesion protein IgLON5, the physiologic function of IgLON5 remains elusive. There are conflicting views on whether autoantibodies cause loss of function, activation of IgLON5, or inflammation-associated neuronal damage, ultimately leading to the disease. We generated IgLON5 knockout (-/-) mice to investigate the functions of IgLON5 and elucidate the pathomechanism of anti-IgLON5 disease. IgLON5 knockout (-/-) mice underwent behavioral tests investigating motor function, psychiatric function (notably anxiety and depression), social and exploratory behaviors, spatial learning and memory, and sensory perception. Histologic analysis was conducted to investigate tau aggregation in mice with tauopathy. IgLON5-/- mice had poorer performance in the wire hang and rotarod tests (which are tests for motor function) than wild-type mice. Moreover, IgLON5-/- mice exhibited decreased anxiety-like behavior and/or hyperactivity in behavior tests, including light/dark transition test and open field test. IgLON5-/- mice also exhibited poorer remote memory in the contextual fear conditioning test. However, neither sleeping disabilities assessed by EEG nor tau aggregation was detected in the knockout mice. These results suggest that IgLON5 is associated with activity, anxiety, motor ability, and contextual fear memory. Comparing the various phenotypes of anti-IgLON5 disease, anti-IgLON5 disease might partially be associated with loss of function of IgLON5; however, other phenotypes, such as sleep disorders and tau aggregation, can be caused by gain of function of IgLON5 and/or neuronal damage due to inflammation. Further studies are needed to elucidate the role of IgLON5 in the pathogenesis of anti-IgLON5 diseases.

A remote digital memory composite to detect cognitive impairment in memory clinic samples in unsupervised settings using mobile devices

Remote monitoring of cognition holds the promise to facilitate case-finding in clinical care and the individual detection of cognitive impairment in clinical and research settings. In the context of Alzheimer’s disease, this is particularly relevant for patients who seek medical advice due to memory problems. Here, we develop a remote digital memory composite (RDMC) score from an unsupervised remote cognitive assessment battery focused on episodic memory and long-term recall and assess its construct validity, retest reliability, and diagnostic accuracy when predicting MCI-grade impairment in a memory clinic sample and healthy controls. A total of 199 participants were recruited from three cohorts and included as healthy controls (n = 97), individuals with subjective cognitive decline (n = 59), or patients with mild cognitive impairment (n = 43). Participants performed cognitive assessments in a fully remote and unsupervised setting via a smartphone app. The derived RDMC score is significantly correlated with the PACC5 score across participants and demonstrates good retest reliability. Diagnostic accuracy for discriminating memory impairment from no impairment is high (cross-validated AUC = 0.83, 95% CI [0.66, 0.99]) with a sensitivity of 0.82 and a specificity of 0.72. Thus, unsupervised remote cognitive assessments implemented in the neotiv digital platform show good discrimination between cognitively impaired and unimpaired individuals, further demonstrating that it is feasible to complement the neuropsychological assessment of episodic memory with unsupervised and remote assessments on mobile devices. This contributes to recent efforts to implement remote assessment of episodic memory for case-finding and monitoring in large research studies and clinical care.

Accurate and fast congestion feedback in MEC-enabled RDMA datacenters

Mobile edge computing (MEC) is a novel computing paradigm that pushes computation and storage resources to the edge of the network. The interconnection of edge servers forms small-scale data centers, enabling MEC to provide low-latency network services for mobile users. Nowadays, Remote Direct Memory Access (RDMA) has been widely deployed in such data centers to reduce CPU overhead and network latency. Plenty of congestion control mechanisms have been proposed for RDMA data centers, aiming to provide low-latency data delivery and high throughput network services. However, our fine-grained experimental analysis reveals that existing congestion control mechanisms still have performance limitations due to inappropriate congestion notifications and the long congestion feedback cycle. In this paper, we propose Mercury, which is an accurate and fast congestion feedback mechanism. Mercury comprises two key components: (1) the state-driven congestion detection and (2) the window-based congestion notification. Specifically, the state-driven congestion detection monitors the queue length and the number of packets received at the switch egress port when the PFC is triggered. It determines the states of egress ports and identifies flows that really contribute to congestion. Then, window-based congestion notification calculates the window sizes for congested flows and rapidly returns Congestion Notification Packets (CNPs) with the window information to the sender. It facilitates the rate adjustment of congested flows. Mercury is compatible with existing RDMA CC mechanisms and can be easily implemented in switches. We employ real-world data sets and conduct both micro-benchmark and large-scale simulations to evaluate the performance of Mercury. The results indicate that, thanks to the accurate and fast congestion feedback, Mercury achieves a reduction in the 99th tail flow completion time by up to 45.1%, 41.8%, 38.7%, 30.9%, and 37.9% compared with Timely, DCQCN, DCQCN+TCD, PACC, and HPCC, respectively.

CK2 negatively regulates the extinction of remote fear memory

Cognitive behavioral therapy, rooted in exposure therapy, is currently the primary approach employed in the treatment of anxiety-related conditions, including post-traumatic stress disorder (PTSD). In laboratory settings, fear extinction in animals is a commonly employed technique to investigate exposure therapy; however, the precise mechanisms underlying fear extinction remain elusive. Casein kinase 2 (CK2), which regulates neuroplasticity via phosphorylation of its substrates, has a significant influence in various neurological disorders, such as Alzheimer’s disease and Parkinson’s disease, as well as in the process of learning and memory. In this study, we adopted a classical Pavlovian fear conditioning model to investigate the involvement of CK2 in remote fear memory extinction and its underlying mechanisms. The results indicated that the activity of CK2 in the medial prefrontal cortex (mPFC) of mice was significantly upregulated after extinction training of remote cued fear memory. Notably, administration of the CK2 inhibitor CX-4945 prior to extinction training facilitated the extinction of remote fear memory. In addition, CX-4945 significantly upregulated the expression of p-ERK1/2 and p-CREB in the mPFC. Our results suggest that CK2 negatively regulates remote fear memory extinction, at least in part, by inhibiting the ERK-CREB pathway. These findings contribute to our understanding of the underlying mechanisms of remote cued fear extinction, thereby offering a theoretical foundation and identifying potential targets for the intervention and treatment of PTSD.