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Age deficits in memory for individual episodes are well established. Less is known about how age affects another key memory function: the ability to form new conceptual knowledge. Here we studied age differences in concept formation in a category-learning paradigm with face-blend stimuli, using several metrics: direct learning of category members presented during training, generalization of category labels to new examples, and shifts in perceived similarity between category members that often follow category learning. We found that older adults were impaired in direct learning of training examples, but that there was no significant age deficit in generalization once we accounted for the deficit in direct learning. We also found that category learning affected the perceived similarity between members of the same versus opposing categories, and age did not significantly moderate this effect. Lastly, we compared traditional category learning to categorization after a learning task in which a category label (shared last name) was presented alongside stimulus-specific information (unique first names that individuated category members). We found that simultaneously learning stimulus-specific and category information resulted in decreased category learning, and that this decrement was apparent in both age groups.

People tend to better remember same-race faces relative to other-race faces (an "own-race" bias). We examined whether the own-race bias extends to associative memory, particularly in the identification and recall of information paired with faces. In Experiment 1, we presented white participants with own- and other-race faces which either appeared alone or accompanied by a label indicating whether the face was a "criminal" or a "victim". Results revealed an own-race facial recognition advantage regardless of the presence of associative information. In Experiment 2, we again paired same- and other-race faces with either "criminal" or "victim" labels, but rather than a recognition test, participants were asked to identify whether each face had been presented as a criminal or a victim. White criminals were better categorised than Black criminals, but race did not influence the categorisation of victims. In Experiment 3, white participants were presented with same- and other-race faces and asked to remember where the person was from, their occupation, and a crime they committed. Results revealed a recall advantage for the associative information paired with same-race faces. Collectively, these findings suggest that the own-race bias extends to the categorisation and recall of information in associative memory.

Children and adolescents with Williams-Beuren syndrome (WBS) have been described as having specific memory abilities and emotional reactivity. Although it is well established in the literature that emotion can enhance memory, no such studies have been undertaken in individuals with WBS. In three experiments, the present study tested whether the negative or positive emotional valence of stimuli can influence verbal, visual and visuo-spatial memory by specifically evaluating two memory components: item and associative memory. Sixteen 8- to 18-year-old individuals with WBS performed the first two experiments and, among them, twelve participated in the third. They were compared to equivalent groups of typically developing control children. Participants completed intentional-encoding tasks followed by immediate item recognition, associative recall or item recall tasks. Event-related potential measures during encoding and recognition of pictures were also added in the third experiment. Results demonstrated, for the first time, effects of emotions on visual item memory and visuo-spatial associative memory in individuals with WBS, that were similar to those observed in typically developing children. By combining behavioral and neural measures, our study provides new knowledge of the interaction between emotion and memory in WBS individuals, which seems to be unaffected by their atypical development.

Dividing attention (DA) between a memory task and a secondary task results in deficits in memory performance across a wide array of memory tasks, but these effects are larger when DA occurs at encoding than at retrieval. Although some research suggests the effects of DA are equal for item and associative memory, thereby suggesting that DA disrupts all components of an episode to the same extent, there have been relatively few studies directly examining the effects of DA on multiple features of the same episode. In addition, no studies have examined how DA may affect the stochastic dependency between multiple source dimensions of a given episode, which is central to theories of source memory, and episodic memory in general. Thus, in two experiments, we used a multidimensional source memory task-examining memory for items and multiple source features-and separately investigated how DA at encoding or at retrieval affects item memory, source memory, and joint source retrieval. DA was manipulated at encoding in Experiment 1 and at retrieval in Experiment 2. Whereas DA at encoding disrupted item memory, as well as source memory and source-source binding, though to a lesser extent, DA at retrieval did not affect any of these outcomes. Results are discussed in terms of levels of binding and the role of attention in encoding and retrieval of bounded episodic representations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The existing Pavlov associative memory circuit only realizes the simple conditioned reflex process, secondary conditional reflex can make the simple conditioning process more complicated and make the circuit more bionic, but there is a lack of relevant circuit implementation. In this paper, a Pavlov associative memory circuit with secondary conditional reflex is proposed by utilizing the memristor. The proposed circuit can respond to a conditional stimulus after initial learning and have two kinds of forgetting process. Besides, this circuit can indirectly establish conditioned reflexes through conditioned stimuli, instead of directly establishing conditioned reflex with unconditioned stimulus. The realization of secondary conditional reflex is confirmed in the PSPICE simulation results. Meanwhile, an extended classification circuit based on secondary conditioned reflex is proposed. Based on the features of objects as input, the output of the circuit is used to achieve the function of classification. The accuracy of application circuit proposed in this paper can be verified by the simulation results in PSPICE.

Ternary content addressable memory (TCAM) is one type of associative memory and has been widely used in caches, routers, and many other mapping-aware applications. While the conventional SRAM-based TCAM is high speed and bulky, there have been denser but slower and less reliable nonvolatile TCAMs using nonvolatile memory (NVM) devices. Meanwhile, some CMOS TCAMs using dynamic memories have been also proposed. Although dynamic TCAM could be denser than the 16T SRAM TCAM and more reliable than the nonvolatile TCAMs, CMOS dynamic TCAMs still suffer from the row-by-row refresh energy and time overheads. In this article, we propose dynamic TCAM using nanoelectromechanical (NEM) relays (DyTAN), and utilize one-shot refresh (OSR) to solve the memory refresh problem. By exploiting the unique NEM relay characteristics, DyTAN outperforms the existing works in the balance between density, speed, and power efficiency. Compared with the 16T SRAM-based TCAM, the 5T CMOS dynamic TCAM, the 2T2R TCAM, and the 2FeFET TCAM, evaluations show that the proposed DyTAN reduces the write energy by up to $2.3\times $ , $1.3\times $ , $131\times $ , and $13.5\times $ , and improves the search energy-delay-product (EDP) by up to $12.7\times $ , $1.7\times $ , $1.3\times $ , and $2.8\times $ , respectively.

In this work, we present a four-transistor-two-resistor (4T2R) ternary content addressable memory (TCAM) bit cell based on the resistive memory (RRAM), comprising the conventional two-transistor-two-resistor (2T2R) cell with two additional comparison transistors. It can effectively amplify the match-line signal ratio (ML-ratio), lower the leakage current of the match cell ( ${I}_{{\mathrm {MATCH}}}$ ), and suppress the read disturbance. The proposed concept is silicon verified using the 180 nm CMOS technology with transition-metal-oxide (TMO) RRAM integrated at the back-end-of-line (BEOL). It achieves a considerable ML-ratio of 1860 and a low ${I}_{{\mathrm {MATCH}}}$ of 11.15 nA on average. In a typical search operation, it shows a negligible ML drop at the match case and a large ML swing range at the mismatch case. The SPICE simulation results further show it can support a long word-length (WDL) of 256 under a clock rate of 100 MHz for search operations, which demonstrates its promise for highly parallel nonvolatile TCAM.

In this paper, an ideal current source forgetting memristor model is proposed. Based on the model, three kinds of synapses with long- and short-term memory are designed: the series forgetting memristor synapse, the forgetting memristor bridge synapse written independently and the forgetting memristor bridge synapse written in batches. Combined with the forgetting property, the long- and short-term weight of the forgetting synapse can be controlled by the long- and short-term resistance of memristors. Compared the three forgetting synapses, the series forgetting memristor synapse has the lowest requirement for memristors, the forgetting memristor bridge synapse written independently is the most flexible, and the forgetting memristor bridge synapse written in batches is the most convenient. Compared with traditional synapses, forgetting synapses with long- and short-term memory have multi-weight storage. When forgetting synapses are applied to associative memory, it can be found more patterns are stored in the neural network and different patterns are recalled at different time due to the forgetting effect.

An associative memory is an artificial neural network model designed to store and recall input-output patterns pairs by association. A new method for obtaining two associative memories +M and -M is presented in this article, which uses a framework focused on two new binary operations boxplus and boxminus, and a unary operation called projection. The conditions to obtaining perfect recovery and the noise boundaries that the memories can tolerate are studied. Memories +M and -M are robust to additive and subtractive noise, respectively, both types converge in one-step and operate in heteroassociative and autoassociative modes. The performance of the proposed memories is tested against other memory models under identical conditions using the gamma binary distance for measuring similarity between binary patterns. The computer simulation results based on the average of 500 trials with the binary set of 26 lowercase letters of 7x7 pixel size, showed that the proposed memories in autoassociative mode exhibited a gamma binary distance of above .8, .75 and .7 by distorting up to 10 pixels by additive, subtractive, and mixed noise, respectively, which implied that the recovered images had a high similarity. In absence of noise the performance was excellent, i.e., the 100% of the recovered images were identical.

A tristate-nanoelectromechanical-switch-based ternary content-addressable memory (NEMTCAM) is proposed for the first time. In the proposed unit NEMTCAM cell, a single nanoelectromechanical (NEM) memory switch replaces two static random access memory cells. Due to the monolithic 3-D (M3D) integration and nonvolatile property of NEM memory switches, the proposed NEMTCAM achieves an 86.3% smaller area, 75.0% lower dynamic power consumption, and a 76.6% higher search speed than conventional ternary content-addressable memory (TCAM) in addition to a negligible static leakage current.

Lots of researchers have used memristors to realize the emotion model of associative memory. In previous works, researchers analyzed this associative memory from two perspectives—forgetting and variable learning rate. In the previous emotion model, neutral stimulus(message notification) and unconditioned reflex(good or bad message) were applied simultaneously. But the variable learning rate with time delay is not considered in the emotion model. When the unconditioned reflex lags behind the neutral stimulus, the associative memory can also be formed. This article proposes an emotion model of variable learning rate with time delay. We also consider three kinds of forgetting: only a stimulus of unconditioned reflex applied, only a neutral stimulus applied and neither stimulus of unconditioned reflex nor neutral stimulus applied. In the end, the software PSPICE is used to simulate the whole circuit. This paper provides an option to realize emotional learning based on memristor.

Alzheimer’s disease (AD), characterized by cognitive impairment, mainly affects middle-aged and elderly people. As the aging process of the world continues to intensify, AD harms people’s life, economy and society more and more seriously. Therefore, it has become an urgent problem to study the pathogenesis of AD and seek treatment on this basis. Hybrid synapse, autapse and spatial correlated noise in diverse neural activities have been investigated separately, however, theoretically understanding combination of them still has not been fully studied. Here in this paper, a neural network with multiple associative memory abilities is established from the perspective of the degeneration of associative memory ability in AD patients under the conditions of hybrid synapse, autapse and spatial correlated noise. In order to explore the pathogenesis, a synaptic loss and synaptic compensation model are established to analyze the associative memory ability of AD in different degrees of disease. The simulation results demonstrate the effectiveness of the proposed models and pave a way in the study of dynamic mechanism with higher bio-interpretability in neural networks.

The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities., The authors acknowledge financial support from the Spanish Ministry of Science and Technology, and the Agencia Espanola de Investigacion (AEI), Spain under grant FIS2017-84256-P (FEDER funds) and from the Consejeria de Conocimiento, Investigacion Universidad, Junta de Andalucia and European Regional Development Funds, Spain, Refs. SOMM17/6105/UGR and A-FQM-175UGR18. APM also acknowledges support from ``Obra Social La Caixa, Spain'' (ID 100010434 with code LCF/BQ/ES15/10360004) and from ZonMw, Netherlands and the Dutch Epilepsy Foundation, Netherlands, project number 95105006. SJ acknowledges support from the Alan Turing Institute under EPSRC, United Kingdom Grant No. EP/N510129/1.

Disinformation in politics, advertising, and mass communications has proliferated in recent years. Few counterargumentation strategies have proven effective at undermining a deceptive message over time. This article introduces the Poison Parasite Counter (PPC), a cognitive-science-based strategy for durably countering deceptive communications. The PPC involves inserting a strong (poisonous) counter-message, just once, into a close replica of a deceptive rival’s original communication. In parasitic fashion, the original communication then “hosts” the counter-message, which is recalled on each reexposure to the original communication. The strategy harnesses associative memory to turn the original communication into a retrieval cue for a negating counter-message. Seven experiments ( N = 3,679 adults) show that the PPC lastingly undermines a duplicitous rival’s original communication, influencing judgments of communicator honesty and favorability as well as real political donations.

In the present study, quantitative relations between Cognitive Emotion Regulation strategies (CERs) and EEG synchronization levels have been investigated for the first time. For this purpose, spectral coherence (COH), phase locking value and mutual information have been applied to short segments of 62-channel resting state eyes-opened EEG data collected from healthy adults who use contrasting emotion regulation strategies (frequently and rarely use of rumination&distraction, frequently and rarely use of suppression&reappraisal). In tests, the individuals are grouped depending on their self-responses to both emotion regulation questionnaire (ERQ) and cognitive ERQ. Experimental data are downloaded from publicly available data-base, LEMON. Regarding EEG electrode pairs that placed on right and left cortical regions, inter-hemispheric dependency measures are computed for non-overlapped short segments of 2 sec at 2 min duration trials. In addition to full-band EEG analysis, dependency metrics are also obtained for both alpha and beta sub-bands. The contrasting groups are discriminated from each other with respect to the corresponding features using cross-validated adaboost classifiers. High classification accuracies (CA) of 99.44% and 98.33% have been obtained through instant classification driven by full-band COH estimations. Considering regional features that provide the high CA, CERs are found to be highly relevant with associative memory functions and cognition. The new findings may indicate the close relation between neuroplasticity and cognitive skills.

Brain-inspired Hyperdimensional computing (HDC) has shown its effectiveness in low-power/energy designs for edge computing in the Internet of Things (IoT). Due to limited resources available on edge devices, multi-task learning (MTL), which accommodates multiple cognitive tasks in one model, is considered a more efficient deployment of HDC. However, as the number of tasks increases, MTL-based HDC (MTL-HDC) suffers from the huge overhead of associative memory (AM) and performance degradation. This hinders MTL-HDC from the practical realization on edge devices. This article aims to establish an MTL framework for HDC to achieve a flexible and efficient trade-off between memory overhead and performance degradation. For the shared-AM approach, we propose Dimension Ranking for Effective AM Sharing (DREAMS) to effectively merge multiple AMs while preserving as much information of each task as possible. For the independent-AM approach, we propose Dimension Ranking for Independent MEmory Retrieval (DRIMER) to extract and concatenate informative components of AMs while mitigating interferences among tasks. By leveraging both mechanisms, we propose a hybrid framework of Mul ti- Ta sking HDC, called MulTa-HDC. To adapt an MTL-HDC system to an edge device given a memory resource budget, MulTa-HDC utilizes three parameters to flexibly adjust the proportion of the shared AM and independent AMs. The proposed MulTa-HDC is widely evaluated across three common benchmarks under two standard task protocols. The simulation results of ISOLET, UCIHAR, and MNIST datasets demonstrate that the proposed MulTa-HDC outperforms other state-of-the-art compressed HD models, including SparseHD and CompHD, by up to 8.23% in terms of classification accuracy.

Remapping refers to a decorrelation of hippocampal representations of similar spatial environments. While it has been speculated that remapping may contribute to the resolution of episodic memory interference in humans, direct evidence is surprisingly limited. We tested this idea using high-resolution, pattern-based fMRI analyses. Here we show that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning. The magnitude of this learning-related decorrelation was predicted by the amount of pattern overlap during initial stages of learning, with greater initial overlap leading to stronger decorrelation. Finally, we show that remapped activity patterns carry relatively more information about learned episodic associations compared to competing associations, further validating the learning-related significance of remapping. Collectively, these findings establish a critical link between hippocampal remapping and episodic memory interference and provide insight into why remapping occurs., When two memories are similar, their encoding and retrieval can be disrupted by each other. Here the authors show that memory interference is resolved through abrupt remapping of activity patterns in the human hippocampal CA3 and dentate gyrus.

Associative memory is a widespread self-learning method in biological livings, which enables the nervous system to remember the relationship between two concurrent events. The significance of rebuilding associative memory at a behavior level is not only to reveal a way of designing a brain-like self-learning neuromorphic system but also to explore a method of comprehending the learning mechanism of a nervous system. In this paper, an associative memory learning at a behavior level is realized that successfully associates concurrent visual and auditory information together (pronunciation and image of digits). The task is achieved by associating the large-scale artificial neural networks (ANNs) together instead of relating multiple analog signals. In this way, the information carried and preprocessed by these ANNs can be associated. A neuron has been designed, named signal intensity encoding neurons (SIENs), to encode the output data of the ANNs into the magnitude and frequency of the analog spiking signals. Then, the spiking signals are correlated together with an associative neural network, implemented with a three-dimensional (3-D) memristor array. Furthermore, the selector devices in the traditional memristor cells limiting the design area have been avoided by our novel memristor weight updating scheme. With the novel SIENs, the 3-D memristive synapse, and the proposed memristor weight updating scheme, the simulation results demonstrate that our proposed associative memory learning method and the corresponding circuit implementations successfully associate the pronunciation and image of digits together, which mimics a human-like associative memory learning behavior.

In traditional neural network integration, people adopt Boosting, Bagging and other methods to integrate traditional neural networks. The integration is complex, time-consuming and laborious, difficult to popularize and apply. This paper is not a continuation of this method, but another integration which is called by us morphological neural network integration (MNNI) or morphological associative memory integration (MAMI). These networks used in MAMI are a network family, with 10 family members, unified in the morphological associative memory framework. Various morphological associative memory networks can be directly used as individual networks to learn and work separately, and then synthesize to draw conclusions. The results of some experiments show that this method is not only feasible in theory, but also effective in practice. It can avoid the complexity of traditional integration method, make the integration structure simple and clear, easy to operate, save time, and therefore is a method of neural network integration with research and application value. The contribution of this paper lies in that: (1) it proposed the concept and method of MNNI and, (2) verified the effectiveness of MNNI through experiments and, (3) it has the characteristics of simplicity, saving time and labor and cost, with a good application prospect and, (4) thus promoting the development of morphological neural networks in theory and practice.

Low-power IC design has become a priority in recent years because of the growing proliferation of portable battery-operated devices, bringing Static Random-Access Memory (SRAM) and Content Addressable Memory (CAM) into play. In today's SoCs, embedded SRAM units have become a necessary component. There is a lack of chips in the current world and to manufacture chips there is the requirement of Electronic Design Automation(EDA) tools that can perform better. In this paper, the main motive is to showcase the performance of open-source tools available currently which can still generate the required output with no cost. In this new era of fast mobile computing, traditional SRAM cell designs are power-demanding and underperforming. Rather than lowering manufacturing costs through high-volume production, specialty memory give cost-effective alternatives through architecture. Specialty memory devices enable the designer to address issues like board area, important timing, data flow bottlenecks, and so on in ways that high-volume regular memory devices cannot. Implementation of memory devices on Cadence environment and open-source environment to check the compatibility and compare the power, area, and delay of both 64-bit SRAM and CAM also analysing and validating the results of both the memory devices in this paper. For SRAM in a cadence environment, the calculated power, area, and slack have improved values, namely 0.145mW, 1104.3um2, and positive slack of 6636. Furthermore, the power for 64-bit CAM in a cadence context is nearly identical to those for an open-source environment ~0.8mW. In an open-source environment, the calculated slack for CAM is 4.74.

In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications is in several times quantity of neurons. Such neural paradigms are very perspective for processing, clustering, recognition, storing large size, strongly correlated, highly noised images and creating of uncontrolled learning machine. And since the basic operational functional nodes of EM are such vector-matrix or matrix-tensor procedures with continuous-logical operations as: normalized vector operations “equivalence”, “nonequivalence”, and etc. , we consider in this paper new conceptual approaches to the design of full-scale arrays of such neuron-equivalentors (NEs) with extended functionality, including different activation functions. Our approach is based on the use of analog and mixed (with special coding) methods for implementing the required operations, building NEs (with number of synapsis from 8 up to 128 and more) and their base cells, nodes based on photosensitive elements and CMOS current mirrors. Simulation results show that the efficiency of NEs relative to the energy intensity is estimated at a value of not less than 1012 an. op. / sec on W and can be increased. The results confirm the correctness of the concept and the possibility of creating NE and MIMO structures on their basis.

Aging impairs visual associative memories. Up to date, little is known about whether aging impairs auditory associative memories. Using the head-related-transfer function to induce perceived spatial locations of auditory phonemes, this study used an audiospatial paired-associates-learning (PAL) paradigm to assess the auditory associative memory for phoneme-location pairs in both younger and older adults. Both aging groups completed the PAL task with various levels of difficulty, which were defined by the number of items to be remembered. The results showed that compared with younger participants' performance, older participants passed fewer stages and had lower capacity of auditory associative memory. For maintaining a single audiospatial pair, no significant behavioral differences between the two aging grous werefound. However, when multiple sound-location pairs were required to be remembered, older adults made more errors and demonstrated a lower working memory capacity than younger adults. Our study indicates aging impairs audiospatial associative learning and memory.

The basolateral complex of the amygdala (BLA) is capable of modulating memory and is thought to do so via projections to regions such as the hippocampus. The present study used optogenetic stimulation of glutamatergic projection neurons in the BLA as rats learned object-context associations during a well-studied hippocampus-dependent memory task. Relative to a control condition, optogenetic BLA stimulation resulted in the accelerated acquisition of when stimulation was delivered following correct choices but not when it was delivered during the intertrial interval. These results extend prior examples of amygdala-mediated memory enhancement to a canonical example of hippocampus-dependent memory and provide an opportunity for future dissection of amygdalar modulation of object-context associative memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A complex-valued Hopfield neural network (CHNN) has been applied as a multistate neural associative memory. Although synchronous mode accelerates the recall process, a CHNN with a projection rule may be trapped at a cycle of length two. A hyperbolic-valued Hopfield neural network (HHNN) with a conventional projection rule converges to a fixed point in synchronous mode. A noise robust projection rule improves the noise tolerance of HHNN, though it is not able to be applied to an HHNN in synchronous mode. In this work, we proposed hybrid mode, that is, asynchronous mode after synchronous mode. The conventional projection rule is employed in synchronous mode, and the noise robust projection rule is employed in asynchronous mode. Thus, the HHNN in hybrid mode converges in both modes. The HHNN in hybrid mode is expected to provide better noise tolerance than an HHNN in synchronous mode and faster recall than an HHNN in asynchronous mode. Computer simulations imply that our expectations are achieved.

This paper addresses the robust stability of recurrent neural networks (RNNs) with time-varying delays and input perturbation, where the time-varying delays include discrete and distributed delays. By employing the new $\psi $ -type integral inequality, several sufficient conditions are derived for the robust stability of RNNs with discrete and distributed delays. Meanwhile, the robust boundedness of neural networks is explored by the bounded input perturbation and $\mathcal {L}^{1}$ -norm constraint. Moreover, RNNs have a strong anti-jamming ability to input perturbation, and the robustness of RNNs is suitable for associative memory. Specifically, when input perturbation belongs to the specified and well-characterized space, the results cover both monostability and multistability as special cases. It is revealed that there is a relationship between the stability of neural networks and input perturbation. Compared with the existing results, these conditions proposed in this paper improve and extend the existing stability in some literature. Finally, the numerical examples are given to substantiate the effectiveness of the theoretical results.

The Levenshtein’s channel model for substitution errors is relevant in information retrieval where information is received through many noisy channels. In each of the channels there can occur at most $t$ errors and the decoder tries to recover the information with the aid of the channel outputs. Recently, Yaakobi and Bruck considered the problem where the decoder provides a list instead of a unique output. If the underlying code $C\subseteq \mathbb {F} _{2}^{n}$ has error-correcting capability $e$ , we write $t=e+\ell $ , ( $\ell \ge 1$ ). In this paper, we provide new (constant) bounds on the size of the list. In particular, we give using the Sauer-Shelah lemma the upper bound $\ell +1$ on the list size for large enough $n$ provided that we have a sufficient number of channels. We also show that the bound $\ell +1$ is the best possible. Most of our other new results rely on constant weight codes.

Field-programmable gate array (FPGA)-based ternary content-addressable memories (TCAMs) are constantly evolving in terms of hardware, power consumption, and speed. One disadvantage of these emulated TCAMs is its poor update-latency. Traditional FPGA-based TCAMs have an update-latency of ${N}$ clock cycles compared to the lookup-latency of one clock cycle, where ${N}$ is the depth of TCAM. Later, the update-latency is improved to ${t}$ clock cycles, where ${t}$ is the number of don’t care bits. In this letter, we presented two mechanisms for updating FPGA-based TCAM and successfully implemented on Xilinx Virtex-6 FPGA: an accelerated MUX-Update mechanism and a cost-effective LUT-Update mechanism. MUX-Update provides an update-latency of ${W}+1$ clock cycles by using only three input/output (I/O) pins, whereas ${W}$ is the width of TCAM. LUT-Update yields a constant update-latency of 2 clock cycles, independent of the size of TCAM, by using ${W}$ I/O pins.

How do we form mental links between related items? Forming associations between representations is a key feature of episodic memory and provides the foundation for learning and guiding behavior. Theories suggest that spatial context plays a supportive role in episodic memory, providing a scaffold on which to form associations, but this has mostly been tested in the context of autobiographical memory. We examined the memory boosting effect of spatial stimuli in memory using an associative inference paradigm combined with eye-tracking. Across two experiments, we found that memory was better for associations that included scenes, even indirectly, compared to objects and faces. Eye-tracking measures indicated that these effects may be partly mediated by greater fixations to scenes compared to objects, but did not explain the differences between scenes and faces. These results suggest that scenes facilitate associative memory and integration across memories, demonstrating evidence in support of theories of scenes as a spatial scaffold for episodic memory. A shared spatial context may promote learning and could potentially be leveraged to improve learning and memory in educational settings or for memory-impaired populations.

A trainable analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by weighted directional paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. The proposed network can be trained using either the modified SPSA or BPTT algorithms to ensure that all the weights are symmetric. Simulation results show that the presence of hidden nodes increases the network’s memory capacity. Using EXOR as an example, the network can be trained to be a dynamic classifier. Using A, U, T, S as training characters, the network was trained to be an associative memory. Simulation results show that the network can perform perfect re-creation of noisy images. Its recreation performance has higher noise tolerance than the standard Hopfield Network and the Restricted Boltzmann Machine. Simulation results also illustrate the importance of feedback iteration in implementing associative memory to re-create from noisy images.

The purpose of this intervention study is to reveal the extent to which memory-related aptitudes are implicated in the second language (L2) fluency development fostered by task repetition. English L2 learners are engaged in oral narrative tasks three times per day under two different 3-day task repetition schedules: blocked (Day 1: A-A-A, Day 2: B-B-B, Day 3: C-C-C) versus interleaved (Day 1: A-B-C, Day 2: A-B-C, Day 3: A-B-C). Their phonological short-term memory (PSTM), attention control, and associative memory were used as predictors of fluency changes measured through speed, breakdown, and repair fluency behaviors. Results showed that while the articulation rate change was not explained by any of the examined predictors, breakdown and repair fluency were predicted by different memory components. Specifically, PSTM was associated with mid-clause pause decrease during the training phase, while associative memory was linked to the increase in clause-final pauses in the posttest. Attention control, as well as PSTM, was related to greater repair frequency in the posttest, indicating increased learners’ attention to speech monitoring. Furthermore, PSTM and associative memory contributed to reducing breakdown fluency in the blocked repetition condition only, suggesting that learners can capitalize on their memory for improving oral fluency by engaging in blocked practice.

Intrusive memories of a traumatic event can be reduced by a subsequent interference procedure, seemingly sparing voluntary memory for that event. This selective-interference effect has potential therapeutic benefits (e.g., for emotional disorders) and legal importance (e.g., for witness testimony). However, the measurements of intrusive memory and voluntary memory typically differ in the role of associations between a cue and the emotional memory “hotspots.” To test this, we asked participants to watch a traumatic film followed by either an interference procedure (reminder plus Tetris) or control procedure (reminder only). Measurement of intrusions (using a laboratory task) and voluntary memory (recognition for film stills) were crossed with the presence or absence of associative cues. The reminder-plus-Tetris group exhibited fewer intrusions despite comparable recognition memory, replicating the results of prior studies. Note that this selective interference did not appear to depend on associative cues. This involuntary versus voluntary memory dissociation for emotional material further supports separate-trace memory theories and has applied advantages.

Our study examined age-related differences across the adult lifespan using a recently developed test assessing memory for "who, when, and where" in addition to associations among these elements. Young (ages 18-25), middle-aged (ages 40-55), and older adults (ages 60+) were asked to remember a sequence of pictures of different faces paired with different places and place the pairs in the correct sequence. Young adults remembered significantly mores face-place pairs in the correct sequence than middle-aged (

A complex-valued multistate Hopfield neural network (CMHNN) is a multistate model of a Hopfield neural network and has been applied as a multistate neural associative memory. CMHNNs require many resources for weight parameters. To reduce the number of weight parameters, twin-multistate activation functions were introduced. A quaternion-valued twin-multistate Hopfield neural network (QTMHNN) is the first model to employ a twin-multistate activation function. A bicomplex-valued twin-multistate Hopfield neural network (BTMHNN) was also introduced. Weak noise tolerance is a disadvantage of QTMHNNs and BTMHNNs. To improve the noise tolerance, a BTMHNN can be modified to a bicomplex-valued twin–hyperbolic Hopfield neural network (BTHHNN). A BTMHNN is defined by the decomposition of a bicomplex number to a pair of complex numbers, whereas a BTHHNN is defined by decomposition of a bicomplex number to a pair of hyperbolic numbers. Computer simulations have improved the noise tolerance of BTHHNNs.

Abstract. Traditionally, most event related potential (ERP) studies of memory retrieval have been reported during item-recognition tasks. Those studies lead to two well-known ERP memory components termed FN400 (familiarity) and LPC (recollection). Nevertheless, some critics have raised concerns regarding the actual meaning of that activity since it emerges as the result of contrasting two different memory traces (previously studied vs. seen for the first time), and it is registered after the target presentation. Therefore, they possibly depict operations not related to memory itself but some cognitive processes associated with recognition memory. Based on those critics, we propose an innovative approach to study electrophysiological activity underlying recognition memory. We compared two very similar tasks with only one of them requiring subjects to actively retrieve a “cue-target” pair of visual stimuli from memory, while the other task required subjects to recognize the target stimulus as equal/different to the cue. Because of this experimental manipulation, we assured that active memory retrieval processes take place between the presentation of the cue and the target stimuli for only one of the tasks. As a result, responses upon the targets can give us valuable information regarding ERP components associated with recognition based on memory retrieval. We found three components possibly related to brain computations necessary to achieve correct target recognition. A N200-like component linked to executive functions (inhibition) from frontal cortices, a P300-like component, related to the expectation of the target stimulus, and a P600-like component associated to recognition based on LTM retrieval. These results help us to understand the complexity behind ERP components associated with recognition memory.

Learning to associate a positive or negative experience with an unrelated cue after the presentation of a reward or a punishment defines associative learning. The ability to form associative memories has been reported in animal species as complex as humans and as simple as insects and sea slugs. Associative memory has even been reported in tardigrades [1], species that diverged from other animal phyla 500 million years ago. Understanding the mechanisms of memory formation is a fundamental goal of neuroscience research. In this article, we work on resolving the current contradictions between different Drosophila associative memory circuit models and propose an updated version of the circuit model that predicts known memory behaviors that current models do not. Finally, we propose a model for how dopamine may function as a reward prediction error signal in Drosophila, a dopamine function that is well-established in mammals but not in insects [2, 3].

According to the associative deficit hypothesis, older adults experience greater difficulty in remembering associations between pieces of information than young adults, despite their relatively intact memory for individual items. It has been demonstrated that this deficit could be simulated by depleting resources for relational processing. The current study examines the possible mechanisms underlying this simulation. Item and associative memory were assessed using a process dissociation paradigm in which word pairs were encoded under full attention (FA) or relational divided attention (DA) conditions across three groups: FA older adults (

Research has indicated that emotional valence can influence associative memory, but it is less clear whether it still works when the retrieval practice is controlled. The current study combined an associative recognition task with a paradigm of retrieval practice, with negative, neutral, and positive word pairs serving as stimuli. Results revealed that intact pairs possessed higher correct response proportions than rearranged, old+new, and new pairs; the rearranged pairs were more likely to be classified as intact; a negative impairment effect was observed in both learning conditions; the retrieval practice effect was sensitive to the interaction of emotional valence by pair type. We shows that the involvement of the recollection-driven process varies with pair type, providing telling evidence for the dual-process models; the occurrence of negative impairment effect conforms to the account of spontaneous interactive imagery; the contribution of desirable difficulty framework is modulated by the interaction of emotional valence by pair type.

This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales. Some sufficient conditions on the existence, uniqueness and the global exponential stability of almost-periodic solutions are established. An example is presented to illustrate the feasibility and effectiveness of the obtained results.

By virtue of hardware parallelism, ternary content addressable memory (TCAM) is attractive for low-latency search for packet forwarding in routers for network communications in the upcoming fifth-generation (5G) era. However, the demand for high-density TCAM encounters remarkable costs in silicon area and power consumption. In this work, a 16-kb nonvolatile ternary content addressable memory (nvTCAM) test chip based on resistive memory (ReRAM) is demonstrated in 28-nm process with two techniques to deal with the aforementioned issues. First, the crossbar array with 2-diode-2-ReRAM (2D2R) nvTCAM cell is proposed to realize $>3\times $ improvement in storage density. The back-end-of-line integration of both diode and ReRAM resistor also allows for further 3-D stacking to realize larger storage density. Second, the machine learning concept is exploited to realize intelligent search operation. ${K}$ -means clustering is employed to allocate the entry storage and then the search of destination IP address can be targeted to a specific bank for low power. The evaluation shows >70% reduction in search energy with 2% overhead in silicon area for bank count of four. The test chip also achieves a match delay of 2 ns under nominal operating conditions.

In this article, we propose a novel chaotic neuron circuit with memristive neural synapses, construct an architecture of memristive chaotic neural network (MCNN) and implement associative memory application of bipolar images. The proposed neuron circuit mainly consists of synapse module and neuron module with chaotic dynamics characteristics. The synapse module is composed of memristors which represent synaptic weights. The neuron module employs voltage feedback operational amplifiers to accomplish integral operation and output function. MCNN utilizes a memristor crossbar array to perform matrix operations and can process the information in parallel. In addition, the proposed circuit of MCNN can accomplish continuous recursive operations and meet different applications due to the programmability of the memristor. The ex-situ method is utilized to train the memristor crossbar array. Furthermore, the associative memory applications of bipolar images are carried out based on the constructed circuits of MCNN with three and nine neurons. The simulation results in PSPICE software testify the functions of the MCNN circuit.

Fast and accurate load parameter identification has a large impact on power systems operation and stability analysis. This article proposes a novel Imitation and Transfer Q-learning (ITQ)-based method to identify parameters of composite constant impedance-current-power (ZIP) and induction motor (IM) load models. Firstly, an imitation learning process is introduced to improve the exploitation and exploration processes. Then, a transfer learning method is employed to overcome the challenge of time-consuming optimization when dealing with new identification tasks. An associative memory is designed to realize dimension reduction, knowledge learning and transfer between different identification tasks. Agents can exploit the optimal knowledge from source tasks to accelerate the search rate in new tasks and improve solution accuracy. A greedy action selection rule is adopted for agents to balance the global and local search. The performance of the proposed ITQ approach has been validated on a 68-bus test system. Simulation results in multi-test cases verify that the proposed method is robust and can estimate load parameters accurately. Comparisons with other methods show that the proposed method has superior convergence rate and stability.

Few studies have compared interference-based forgetting between item versus associative memory. The memory-system dependent forgetting hypothesis (Hardt, Nader, & Nadel, 2013) predicts that effects of interference on associative memory should be minimal because its hippocampal representation allows pattern separation even of highly similar information. In contrast, there should be strong interference effects on extra-hippocampally represented item memory. We tested this prediction in behavioral data from 3 experiments using continuous recognition paradigms. Given older adults' greater deficits in associative than item memory, we also compared younger and older adults to test whether this associative deficit extends to greater interference susceptibility in older adults' associative memory. Experiment 1 examined item-item associative memory with participants studying unrelated word pairs continuously intermixed with item (single words) and associative (intact vs. recombined pairs) recognition tests across interference-filled lags. Experiments 2 and 3 examined item-context (i.e., source) associative memory with participants studying words in different spatial positions continuously intermixed with source-monitoring tests (presented on top vs. on bottom vs. new?) across interference-filled lags (Experiment 3 controlling for delay/decay-based effects). In all experiments, item memory declined from the first lag on. In contrast, associative memory initially remained stable, with strong evidence for null effects of interference even in older adults, but showed some declines at later lags. The data supports Hardt et al.'s proposal of differential interference-based forgetting in item versus associative memory. The results further show that the age-related associative memory deficit does not extend to greater interference-based forgetting in older adults' associative memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Packet classification is the base of various network functions such as firewall filtering, network intrusion detection and quality of services, etc. Ternary content addressable memory (TCAM) is widely employed in performing efficient packet classification. However, TCAM has some drawbacks, including limited capacity, high energy consumption, and incapability to store arbitrary ranges. Moreover, TCAM is only suitable for single-match packet classification natively, which is associated with one rule-set and reports one rule, for it only reports the first matching entry. However, except for single-match packet classification, another type of packet classification, multi-category packet classification, which is associated with multiple rule-sets and reports one matching rule for each rule-set, is also required in some scenarios, such as in the consolidation of multiple single-match network functions. The naive scheme performing multi-category packet classification with TCAM is to search a packet in multiple rule-sets one by one. Its performance decreases linearly as the number of rule-sets increases. To efficiently perform multi-category packet classification using TCAM, a novel scheme named REM is proposed in this paper. REM is based on the idea of reducing TCAM accesses per classification by merging rule-entry sets converted from rule-sets. The experiments show that compared with the naive scheme, REM can achieve 3x to 5x improvement on packet classification throughput, and reduce the energy consumption by 50% to 75%.

In network routers and lookup tables for fast searching, ternary content addressable memories (TCAMs) have found great use. For future generation networks, architecture is essential for efficient TCAM in terms of power consumption and speed of service. A TCAM cell with an optimized CNTFET is proposed in this paper. The TCAM cell proposed is designed for improved performance using optimized CNTFET with shorted gate (SG) CNTFET and independent gate CNTFET (IG-CNTFET). As access transistors, IG-CNTFETs are used in the SRAM cell design, and SG-CNTFETs are used in the comparison circuit. The 4 × 4 TCAM array was also planned using the TCAM cells proposed. The suggested CNTFET TCAM cell and array showed better performance than that of TCAM cells and arrays based on CMOS and FinFET. The suggested CNTFET-based TCAM with SG shows a substantial improvement in search delay and average power and peak power compared to CMOS and FinFET-based cell technology. Compared to CMOS and FinFET technologies, the average capacity of the proposed TCAM cell is substantially enhanced. The proposed technique's search delay is enhanced by 17.2 percent over TCAM cell based on FinFET. Compared to the FinFET and CMOS-based TCAM cell, the peak power of the proposed technique is also enhanced. All the simulations are carried out at 32 nm with HSPICE technology.

The ability to generate associative representations and to retrieve them from long-term episodic memory generally declines in healthy aging. However, it is unclear whether healthy aging has differential effects on associative memory for identity, spatial configuration, and temporal order relationships. In the current study, we assessed how healthy aging impacts on associative memory for identity, spatial, or temporal relationships between pairs of visual objects via discrimination of intact and rearranged pairs. Accuracy and response time performance of healthy older adults (aged 65-80) were compared with young adults (ages 19-30). Age-related declines in associative memory were observed equally for all types of associations, but these declines differed by associative status: aging most strongly affected ability to discriminate rearranged pairs. These results suggest that associative memory for identity, spatial, and temporal relationships are equally affected by healthy aging, and may all depend on a shared set of basic associative mechanisms.

Memorizing the locations of environmental cues is crucial for survival and depends on the hippocampus. We recorded local field potentials (LFPs) from the hippocampus of freely moving mice during an object location task. The power of beta-band (23-30 Hz) oscillations increased immediately before approaching objects in a memory-encoding phase. The exploration-induced beta oscillations gradually decreased during the memory-encoding session. Mice that exhibited stronger beta oscillation power exhibited better performance in the subsequent memory-retrieval test. These results suggest that beta oscillations in the hippocampal CA1 region are involved in the memory encoding of object-location associations.

The ternary content addressable memory (TCAM) is widely used in the routing table due to its high lookup performance. However, a large number of transistors would cause the power consumption of TCAM to be considerable. In this brief, a new state-preserved technique, named data retention based TCAM (DR-TCAM), is proposed to reduce the leakage power dissipated in the TCAM memory. According to the continuous feature of mask data, the DR-TCAM can dynamically adapt the power source of mask cells so as to reduce the TCAM leakage power. Particularly, the mask data wouldn’t be destroyed in the DR-TCAM. Based on TSMC 40nm technology, the simulation results show that the DR-TCAM performs better than the state-of-the-art works. Compared to the traditional TCAM design, the DR-TCAM can deliver a TCAM leakage power reduction of 41% for a collection of real routing tables. Besides, the total power reduction is about 12%.