Memory Storage Research Articles

Thrombin Nanochannel Logic Gate Inspired by BioMemory.

The process of "reading" and "writing" in biomemory involves the transmission of electrical signals between neurons, with ligand-gated ion channels assuming a key role. The solid-state nanochannels exhibit certain similarities with neurons. Information transmission can be achieved by controlling the flow of ions within nanochannels, rendering them potentially suitable for simulating neuron behavior. Herein, thrombin (Thr) was chosen as the target protein, and a functionalized nanochannel sensing system was successfully constructed using DNA aptamers, enabling a highly sensitive Thr response with a detection limit of 0.221 fM. Simultaneously, based on Watson-Crick base pairing and programmable chain displacement reactions, controlled release and cyclic response of the target molecule were further achieved. This mechanism elucidates the rules governing specific input-output relationships, innovatively linking them with memory storage and recognition through the Thr-nanochannel logic gate, thereby realizing the reading of biomemory at the hardware level. In summary, the biological hybrid nanofluidic control device of this invention converts molecular events into electrical signals, providing potential avenues for establishing connections between the mechanisms of biomemory and solid-state nanochannel biosensing and recognition in the future.

OLM-SLAM: online lifelong memory system for simultaneous localization and mapping

Abstract Simultaneous Localization and Mapping is a fundamental task for robots in unknown environments. However, the poor generalization ability of learning-based algorithms in unknown environments hinders their widespread adoption. Additionally, artificial neural networks are subject to catastrophic forgetting. We propose a lifelong SLAM framework called OLM-SLAM that effectively solves the neural network catastrophic forgetting problem. To ensure the generalization of the neural network, this paper proposes a method for the sensitivity analysis of the network weight parameters. Meanwhile, inspired by human memory storage mechanisms, we design a dual memory storages mechanism that retains dynamic memory and static memory. A novel memory filtering mechanism is proposed to maximize image diversity within a fixed-size memory storage area addressing the problem of limited storage capacity of embedded devices in real-world situations. We have extensively evaluated the model on a variety of real-world datasets. Compared with CL-SLAM, the overall translation error of the test sequence is improved by 44.9%. The translation and rotation errors of Retention Ability were improved by 111.6% and 66.7%, respectively. The results demonstrate that OLM-SLAM can outperform previous methods of the same type, and OLM-SLAM has high retention ability when facing different sequences of the same type of environment.

Post-traumatic stress disorder: Cerebral and extracerebral processing of traumatic memories and treatment strategies

Post-traumatic stress disorder (PTSD) is a severe neuropsychiatric condition characterized by anxiety-related symptoms, including intrusive memories. However, the exact anatomic location of traumatic memories remains unclear. Traumatic imagery may involve the amygdala and posterior cingulate cortex, rather than the hippocampus. Besides the central nervous system, cells and even proteins can process information and store memories. For instance, >36% of cardiac transplant recipients inherit donor personality traits, emphasizing that tissues can store and recall memories. Moreover, physical therapists mention “musculofascial memories,” that is, the ability of muscles and fascia to retain the memory of past injuries and adapt their function accordingly. Immune cells record previous infections, demonstrating the broader perspective of memory storage in the body. At the cellular level, psychological stress induces premature cellular senescence, a survival program characterized by proliferation arrest; resistance to apoptosis; and a toxic secretome known as the senescence-associated secretory phenotype (SASP). SASP contains brain-derived neurotropic factor (BDNF), a neurotrophin linked to fear memory that is frequently elevated in the peripheral blood of patients with PTSD. Endothelial cells (ECs), the tiles paving the lumen of large and small vessels, age earlier than other cells in patients with severe mental illness, including PTSD. Senescent ECs release SASP directly into the systemic circulation, spreading senescence throughout the body. In this narrative review, we hypothesize that ECs store traumatic memories and SASP-associated BDNF activates traumatic imagery. We also discuss membrane lipid replacement, mitochondrial transplantation and transfer, and several natural and synthetic compounds that may counteract endothelial senescence and SASP.

Sequential replacement of PSD95 subunits in postsynaptic supercomplexes is slowest in the cortex.

The concept that dimeric protein complexes in synapses can sequentially replace their subunits has been a cornerstone of Francis Crick's 1984 hypothesis, explaining how long-term memories could be maintained in the face of short protein lifetimes. However, it is unknown whether the subunits of protein complexes that mediate memory are sequentially replaced in the brain and if this process is linked to protein lifetime. We address these issues by focusing on supercomplexes assembled by the abundant postsynaptic scaffolding protein PSD95, which plays a crucial role in memory. We used single-molecule detection, super-resolution microscopy and MINFLUX to probe the molecular composition of PSD95 supercomplexes in mice carrying genetically encoded HaloTags, eGFP, and mEoS2. We found a population of PSD95-containing supercomplexes comprised of two copies of PSD95, with a dominant 12.7 nm separation. Time-stamping of PSD95 subunits in vivo revealed that each PSD95 subunit was sequentially replaced over days and weeks. Comparison of brain regions showed subunit replacement was slowest in the cortex, where PSD95 protein lifetime is longest. Our findings reveal that protein supercomplexes within the postsynaptic density can be maintained by gradual replacement of individual subunits providing a mechanism for stable maintenance of their organization. Moreover, we extend Crick's model by suggesting that synapses with slow subunit replacement of protein supercomplexes and long-protein lifetimes are specialized for long-term memory storage and that these synapses are highly enriched in superficial layers of the cortex where long-term memories are stored.

Unstable Frequencies

In this essay I describe an experimental wifi network sideBand which provided access to a simple message-board during the Content/Form workshop, held at Haus der Kulturen der Welt in January 2024, as part of transmediale. This is considered in relation to the collaborative infrastructure ServPub that was carefully assembled and maintained for the workshop. The production of the hardware and software required for the sideBand network is described with specific consideration of the types of memory and data storage systems that are utilised. The programming that provided message-board (and wider) functionality is also described. The conditions of this production along with the specific ways in which use of the network unfolds are considered in relation to Dunbar-Hester’s propagation and a Luddite framing, that includes sabotage and refusal. This is argued as producing other ways of understanding infrastructure and place, related to the feminist methodologies of ServPub.

Human-Computer Interaction, and Virtual Reality Applications for Memory Enhancement

Human-Computer Interaction (HCI) and Virtual Reality (VR) technologies are redefining the boundaries of cognitive enhancement, particularly in the domain of memory improvement. By immersing users in interactive virtual environments, VR offers unique opportunities to engage multiple sensory modalities and enhance memory encoding, storage, and retrieval processes. This research explores the integration of HCI principles and VR applications to develop innovative solutions for memory enhancement. Key areas of focus include the design of adaptive VR environments tailored to individual cognitive needs, the role of gamification in reinforcing memory, and the application of biofeedback for real-time user monitoring. The study also examines challenges such as user adaptability, accessibility, and the ethical considerations of using immersive technology for cognitive interventions. Through case studies and experimental findings, this research highlights the transformative potential of VR in memory training, particularly for educational purposes and memory rehabilitation. By merging HCI methodologies with VR technology, the study offers a comprehensive framework for enhancing memory, paving the way for innovative tools that benefit diverse populations.

Acts of Remembering: From a Radically Enactive Point of View

ABSTRACT Cognitivists and radically enactivists understand acts of remembering in fundamentally different ways. It is a hallmark of cognitivism to seek to identify some independent component that puts the memory – the what’s remembered – into acts of remembering. Such a component is variably characterized as: a stored memory, a stored content, or stored information. Accordingly, and on this basis, cognitivists seek to distinguish memories from acts of remembering. This paper raises doubts about the very idea that we can make sense of the real, independent existence of stored memory contents or stored memory information and, in its place, motivates adoption of a radically enactivist approach to understanding various acts of remembering.

Flexible magnetoelectric systems: Types, principles, materials, preparation and application

Recently, the rapid development of flexible electronic materials and devices has profoundly influenced various aspects of social development. Flexible magnetoelectric systems (FMESs), leveraging magnetoelectric coupling, hold vast potential applications in the fields of flexible sensing, memory storage, biomedicine, energy harvesting, and soft robotics. Consequently, they have emerged as a significant branch within the realm of flexible electronic devices. According to its working principle, FMES are divided into three categories: FMES based on magnetodeformation and piezoelectric effects, FMES based on giant magnetoresistive effect, and FMES based on electromagnetic induction. Although some articles have reviewed the first two types of FMES, there is a lack of systematic introduction of the FMES based on electromagnetic induction in existing studies, especially the development history and research status of the three types of FMES. Therefore, this paper systematically reviews the development history and research status of these three kinds of FMES and reveals the working principle and mode of the flexible magnetoelectric system from the perspective of the force-electricity-magnetism coupling mode. In addition, the material selection criteria, device manufacturing methods, and application fields of the FMES are also introduced. Finally, this review delves into the challenges and opportunities confronting the development of FMES, exploring the future development directions. This review aims to establish a theoretical foundation and provide methodological strategies for future research on FMES. It is anticipated to promptly address the current gap in this research field and facilitate the development of the flexible electronic family.

A combinatorial neural code for long-term motor memory.

Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood1-8. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of the lifespan of a mouse and show that learned actions are stably retained in combination with context, which protects existing memories from erasure during new motor learning. We established a continual learning paradigm in which mice learned to perform directional licking in different task contexts while we tracked motor cortex activity for up to six months using two-photon imaging. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories instead of modifying existing representations. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. Continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning.

An infralimbic cortex neuronal ensemble encoded during learning attenuates fear generalization expression.

Generalization allows previous experience to adaptively guide behavior when conditions change. The infralimbic (IL) subregion of the ventral medial prefrontal cortex plays a known role in generalization processes, although mechanisms remain unclear. A basic physical unit of memory storage and expression in the brain are sparse, distributed groups of neurons known as ensembles (i.e., the engram). Here, we set out to determine whether neuronal ensembles established in the IL during learning contribute to generalized responses. Generalization was tested in male and female mice by presenting a novel, ambiguous, tone generalization stimulus following Pavlovian defensive (fear) conditioning. The first experiment was designed to test a role for IL in generalization using chemogenetic manipulations. Results show IL regulates defensive behavior in response to ambiguous stimuli. IL silencing led to a switch in defensive state, from vigilant scanning to generalized freezing, while IL stimulation reduced freezing in favor of scanning. Leveraging activity-dependent "tagging" technology (ArcCreER T2 x eYFP system), a neuronal ensemble, preferentially located in IL Layer 2/3, was associated with the generalization stimulus. Remarkably, in the identical discrete location, fewer reactivated neurons were associated with the generalization stimulus at the remote timepoint (30 days) following learning. When an IL neuronal ensemble established during learning was selectively chemogenetically silenced, generalization increased. Conversely, IL neuronal ensemble stimulation reduced generalization. Overall, these data identify a crucial role for IL in suppressing generalized responses. Further, an IL neuronal ensemble, formed during learning, functions to later attenuate the expression of generalization in the presence of ambiguous threat stimuli.

Targeted memory reactivation with sleep disruption does not weaken week-old memories.

When memories are reactivated during sleep, they are potentially transformed and strengthened. However, disturbed sleep may make this process ineffective. In a prior study, memories formed shortly before sleep were weakened by auditory stimulation when that stimulation provoked memory reactivation while also disrupting sleep - a procedure known as targeted memory reactivation with sleep disruption (TMR-SD). Here we used TMR-SD to test whether memory weakening occurs for less-fragile memories. Participants first learned locations of 74 objects on a monitor. One week later, TMR-SD auditory cues linked with 50% of the previously learned object locations were presented during sleep. Even though the cues disturbed sleep, memories were not weakened when reactivated in this way, compared to when not reactivated. Whereas memory storage is vulnerable to disruption shortly after learning, this new evidence supports the notion that memory storage gradually gains resistance to the harm caused by reactivation combined with sleep disruption.

Mechanical Unfolding of Network Nodes Drives the Stress Response of Protein-Based Materials.

Biomaterials synthesized from cross-linked folded proteins have untapped potential for biocompatible, resilient, and responsive implementations, but face challenges due to costly molecular refinement and limited understanding of their mechanical response. Under a stress vector, these materials combine the gel-like response of cross-linked networks with the mechanical unfolding and extension of proteins from well-defined 3D structures to unstructured polypeptides. Yet the nanoscale dynamics governing their viscoelastic response remains poorly understood. This lack of understanding is further exacerbated by the fact that the mechanical stability of protein domains depends not only on their structure, but also on the direction of the force vector. To this end, here we propose a coarse-grained network model based on the physical characteristics of polyproteins and combine it with the mechanical unfolding response of protein domains, obtained from single molecule measurements and steered molecular dynamics simulations, to explain the macroscopic response of protein-based materials to a stress vector. We find that domains are about 10-fold more stable when force is applied along their end-to-end coordinate than along the other tethering geometries that are possible inside the biomaterial. As such, the macroscopic response of protein-based materials is mainly driven by the unfolding of the node-domains and rearrangement of these nodes inside the material. The predictions from our models are then confirmed experimentally using force-clamp rheometry. This model is a critical step toward developing protein-based materials with predictable response and that can enable applications for shape memory and energy storage and dissipation.

Quantum-inspired neural network with hierarchical entanglement embedding for matching

Quantum-inspired neural networks (QNNs) have shown potential in capturing various non-classical phenomena in language understanding, e.g., the emgerent meaning of concept combinations, and represent a leap beyond conventional models in cognitive science. However, there are still two limitations in the existing QNNs: (1) Both storing and invoking the complex-valued embeddings may lead to prohibitively expensive costs in memory consumption and storage space. (2) The use of entangled states can fully capture certain non-classical phenomena, which are described by the tensor product with powerful compression ability. This approach shares many commonalities with the process of word formation from morphemes, but such connection has not been further exploited in the existing work. To mitigate these two limitations, we introduce a Quantum-inspired neural network with Hierarchical Entanglement Embedding (QHEE) based on finer-grained morphemes. Our model leverages the intra-word and inter-word entanglement embeddings to learn a multi-grained semantic representation. The intra-word entanglement embedding is employed to aggregate the constituent morphemes from multiple perspectives, while the inter-word entanglement embedding is utilized to combine different words based on unitary transformation to reveal their non-classical correlations. Both the number of morphemes and the dimensionality of the morpheme embedding vectors are far smaller than the counterparts of words, which would compress embedding parameters efficiently. Experimental results on four benchmark datasets of different downstream tasks show that our model outperforms strong quantum-inspired baselines in terms of effectiveness and compression ability.

High contrast stimuli-responsive fluorescence emitters based on carbazole-cyanostilbene derivatives with aggregation induced emission properties

Due to the aggregation induced emission (AIE) materials exhibiting obvious fluorescent emission changes under the external stimuli of heat, solvent, force and so on, significant efforts have been directed toward developing AIE-active materials with high contrast stimuli-responsive fluorescence behaviors. Herein, three electron-donor(D)-electron-acceptor(A) and D-A-D type organic fluorescence emitters of Cz-DC-tBu, Cz-DC-CN and Cz-DC-OMe were facilely synthesized using carbazole group as D unit and cyano group as A unit. They showed typical AIE features and exhibited remarkable color changes under mechanical force and in different polarity solvents, respectively. The stimuli-responsive mechanism of the compounds were investigated by powder X-ray Diffraction (PXRD) and density functional theory calculations (DFT), which revealed that the mechanofluorochromic behaviors were associated with the molecular packing modes, and the solvatochromic effects were caused by the intramolecular charge-transfer transition (ICT) effect. Significantly, it was found that Cz-DC-tBu showed high contrast stimuli-responsive behaviors through adjusting the substituent groups on the cyanostilbene unit with CN, OMe, and tBu groups, respectively. This study provided a new strategy for the design of AIE-active stimuli-responsive fluorescence emitters, and made them possible using in stress sensors, chemical detection, memory storage, anti-counterfeiting applications.

Advanced Intelligent Anti‐Counterfeiting in a Memory Storage Phosphor Through Response of Charge Carriers to Temperature

AbstractPersistent phosphor as a printing anti‐counterfeiting material has attracted strong attention owing to its inherent luminescent decay process and multi‐mode luminescence characteristics under external field stimulation. However, the dynamic persistent luminescence (PersL) pattern of a single material is only manifested in color changes and still faces the problem of being easily decrypted. Herein, a multi‐color luminescent material with a wide range distribution of traps is developed, which can emit strong white PersL. In addition, the phosphors show an intelligent response of thermo‐stimulated luminescence to ambient temperature via recalling the charging temperature of the traps. The PersL mechanism is proposed based on electron transfer between PbO6 and TbO6 octahedrons through the O bridging. Based on the intelligent response of thermo‐stimulated luminescence to temperature, high‐throughput multi‐channel luminescence anti‐counterfeiting can be achieved on a single phosphor recording layer. This study not only provides further insights into the PersL mechanism, but also opens a new door for constructing intelligent responsive anti‐counterfeiting materials.

Competitive plasticity to reduce the energetic costs of learning.

The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks which might require only a few synaptic updates, already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 108 synaptic updates. Therefore the brain has likely evolved to be able to learn using as little energy as possible. We explored the energy required for learning in feedforward neural networks. Based on a parsimonious energy model, we propose two plasticity restricting algorithms that save energy: 1) only modify synapses with large updates, and 2) restrict plasticity to subsets of synapses that form a path through the network. In biology networks are often much larger than the task requires, yet vanilla backprop prescribes to update all synapses. In particular in this case, large savings can be achieved while only incurring a slightly worse learning time. Thus competitively restricting plasticity helps to save metabolic energy associated to synaptic plasticity. The results might lead to a better understanding of biological plasticity and a better match between artificial and biological learning. Moreover, the algorithms might benefit hardware because also electronic memory storage is energetically costly.

Mechanisms of memory-supporting neuronal dynamics in hippocampal area CA3

Hippocampal CA3 is central to memory formation and retrieval. Although various network mechanisms have been proposed, direct evidence is lacking. Using intracellular Vm recordings and optogenetic manipulations in behaving mice, we found that CA3 place-field activity is produced by a symmetric form of behavioral timescale synaptic plasticity (BTSP) at recurrent synapses among CA3 pyramidal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not the DG was required to update place cell activity based on the animal's movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Theoretical analyses further highlight the superior memory storage capacity of such networks, especially when dealing with correlated input patterns. This evidence elucidates the cellular and circuit mechanisms of learning and memory formation in the hippocampus.

Study While You Sleep: Using Targeted Memory Reactivation as an Independent Research Project for Undergraduates.

Newly acquired information is stabilized into long-term memory through the process of consolidation. Memories are not static; rather, they are constantly updated via reactivation, and this reactivation occurs preferentially during Slow-Wave Sleep (SWS, also referred to as N3 in humans). Here we present a scalable neuroscience research investigation of memory reactivation using low-cost electroencephalogram (EEG) recording hardware and open-source software, for students and educators across the K-12 and higher education spectrum. The investigation uses a method called targeted memory reactivation (TMR), whereby auditory cues that were previously associated with learning are re-presented during sleep, triggering the recall of stored memories and (through this) strengthening these memories. We demonstrated the efficacy of this technique on seven healthy human subjects (ages 19-35). The subjects learned to play a spatial memory game on an app where they associated pictures (e.g., a clock) with locations on a grid while they listened to picture-appropriate sounds (e.g., "tic-toc"); next, they took a nap while undergoing EEG recordings. During SWS, half of the sounds from the game were replayed by the app, while half were substituted with non-learned sounds. Subjects then played the memory game again after waking. Results showed that spatial recall was improved more for cued than uncued memories, demonstrating the benefits of memory replay during sleep and suggesting that one may intervene in this process to boost recall of specific memories. This research investigation takes advantage of the importance of sleep for memory consolidation and demonstrates improved memory performance by cueing sounds during SWS.

Enhanced Fear Extinction Through Infralimbic Perineuronal Net Digestion: The Modulatory Role of Adolescent Alcohol Exposure.

Perineuronal nets (PNNs) are specialized components of the extracellular matrix that play a critical role in learning and memory. In a Pavlovian fear conditioning paradigm, degradation of PNNs affects the formation and storage of fear memories. This study examined the impact of adolescent intermittent ethanol (AIE) exposure by vapor inhalation on the expression of PNNs in the adult rat prelimbic (PrL) and infralimbic (IfL) subregions of the medial prefrontal cortex. Results indicated that following AIE, the total number of PNN positive cells in the PrL cortex increased in layer II/III but did not change in layer V. Conversely, in the IfL cortex, the number of PNN positive cells decreased in layer V, with no change in layer II/III. In addition, the intensity of PNN staining was significantly altered by AIE exposure, which narrowed the distribution of signal intensity, reducing the number of high and low intensity PNNs. Given these changes in PNNs, the next experiment assessed the effects of AIE and PNN digestion on extinction of a conditioned fear memory. In Air control rats, digestion of PNNs by bilateral infusion of Chondroitinase ABC (ChABC) into the IfL cortex enhanced fear extinction and reduced contextual fear renewal. In contrast, both fear extinction learning and contextual fear renewal remained unchanged following PNN digestion in AIE exposed rats. These results highlight the sensitivity of prefrontal PNNs to adolescent alcohol exposure and suggest that ChABC-induced plasticity is reduced in the IfL cortex following AIE exposure.

Photo‐synaptic Memristor Devices from Solution‐processed Ga2O3 Thin Films

AbstractHardware integration with biological synaptic function is the key to realizing brain‐like computing. Resistive Random Access Memory (RRAM), with a similar structure to biological synapses, are important candidate for the simulation of biological synaptic function. In this work, Ga2O3 film as a functional layer of RRAM is prepared by the solution method, and an RRAM‐based photo‐synaptic device with an Ag/Ga2O3/Si structure is constructed subsequently. The device exhibits excellent bipolar resistive switching characteristics, with the merits of a large storage window and long retention time. Furthermore, the devices generated excitatory postsynaptic currents (EPSC) and paired‐pulse facilitation (PPF) behaviors under light pulse stimulation, enabling the simulation of synaptic plasticity. The transformation of synaptic behavior from short‐term memory (STM) to long‐term memory (LTM) is achieved by observing the spike‐duration dependent plasticity (SDDP), spike‐intensity dependent plasticity (SIDP), spike‐number dependent plasticity (SNDP) and spike‐rate dependent plasticity (SRDP) characteristics of photonic synapses under different conditions. The device also simulates the process of successive “learning‐forgotten‐remembering”, revealing that RRAM‐based photonic synapses have great potential in the fields of artificial visual perception and memory storage.