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An implementation of associative memory based on a Hopfield network is described. In the proposed approach, memory addresses are regarded as training vectors of the artificial neural network. The efficiency of memory search is directly associated with solving the overfitting problem. A method for dividing the training and input network vectors into parts, the processing of which requires a smaller number of neurons, is proposed. Results of a series of experiments conducted on Hopfield network models with different numbers of neurons trained with different numbers of vectors and operated under different noise conditions are presented.

A unique feature of fear memory is its persistence that is highly relevant to fear and anxiety-related mental disorders. Recurrent reactivation of neural representations acquired from a traumatic event is thought to contribute to the indelibility of fear memory. Given a well-established role of hippocampus for memory reactivation, hippocampus is likely involved in consolidation process of fear memory. However, evidence suggests that formation of fear memory to a discrete sensory cue is hippocampus-independent. Here, using a pharmacological reversible inactivation of dorsal hippocampus in auditory cued fear conditioning by local infusion of muscimol, we demonstrate in mice that hippocampus is critical for remote memory formation of learned fear to the discrete sensory cue. Muscimol infusion before conditioning did not affect formation of recent auditory fear memory as previously reported. Same muscimol infusion, however, impaired remote auditory fear memory. Muscimol infusion before remote test of auditory fear memory did not affect memory retrieval, indicating hippocampus is not a brain site for storage of remote cued fear memory. Moreover, memory reactivation enforced by re-exposure to the conditioned tone could compensate for hippocampal inactivation, as memory-reactivated mice showed normal remote auditory fear memory despite hippocampal inactivation. Our findings support that hippocampus may have a general role for consolidation of remote associative memory through reactivation of memory trace, giving an insight into how learned fear persists over time.

In this paper, we first established the fractional order memristive multidirectional associative memory neural networks (FMMAMNNs) model, and then considered its fixed-time synchronization control problem. On the basis of sliding model control and Lyapunov stability theorem, a fractional order sliding mode controller is constructed. By adding this controller to the response system, the error of the driver-response systems gradually converges to 0 in a fixed time. Compared with the previous researches, this paper considers a more complex model, and the proposed control theories can ensure that the setting time is only related to the model and controller, but not to the initial states of the system. Besides, the control theories are also applicable to the integer order models. Finally, two numerical simulations are given, the results show the validity of the theories.

Ternary Content-Addressable Memory (TCAM) chips are used to store Forwarding Information Bases (FIB) in modern routers. TCAM provides next-hop lookup for IP packets at the line-rate. However, TCAM is expensive and energy-consuming; in addition, the constant FIB growth may lead to TCAM overflow problem. Yet only a small portion of FIB entries carries the most of network traffic. Thus, FIB caching, namely, installing the most popular entries in a fast memory, e.g., TCAM, may significantly minimize TCAM usage. To date, FIB caching architecture has not been widely deployed in backbone routers due to high cache-miss latency and the lack of an efficient cache replacement strategy. In this work, we leverage the concept of the programmable data plane to design a Programmable FIB Caching Architecture (PFCA) with two levels of cache. We present a pipeline-based algorithm to detect the least popular prefixes in a cache for a victim selection. We tested the prototype of PFCA using real traffic traces and an FIB with more than 599K entries, and showed that PFCA can be implemented using P4 programmable data plane language. Our results show that PFCA achieves 99.8% hit ratio for Level-1 cache with 20K entries and nearly 99.9% hit ratio for Level-2 cache with 40K entries. We also demonstrate that PFCA significantly reduces the number of BGP updates in the cache and thus makes the cache more stable.

Studies show that emotion enhances memory for individual items but weakens memory for associations between items (Bisby & Burgess, 2014). One explanation for this associative memory impairment is that emotional stimuli capture attention, causing enhanced encoding of the emotional item but reduced encoding of the surrounding environment (Schupp, Junghöfer, Weike, & Hamm, 2003). This explanation generates the prediction that emotional information always impairs associative memory. Alternatively, it may be that emotion orients attention towards threats in the environment, suggesting that emotions’ effects on associative memory may differ depending on where they indicate a threat may be coming from (Öhman, Flykt, & Esteves, 2001). For example, seeing an angry face constitutes a direct threat. The angry face itself potentially captures attention and thereby reduces memory for its associated information. In contrast, seeing a fearful face indicates a threat elsewhere in the environment. Therefore, the fearful face may redirect attention towards the surroundings and thus enhance encoding of the associated information. To adjudicate between these hypotheses, subjects studied sets of three images, consisting of two objects and a face with either a neutral, angry, or fearful expression. Subjects were later tested on their memory for the associations between the three items. Supporting the first hypothesis, memory for both angry and fearful associations was worse than memory for neutral associations. Contrary to the second hypothesis, there were no differences in memory for angry versus fearful associations. Thus, emotional information itself seems to capture attention, weakening memory for related information.

An associative memory is a special type of artificial neural network that has the purpose of store input patterns with their corresponding output patterns and efficiently recall a pattern from a noise-distorted version. Presented in this article is a new framework for constructing a binary associative memory model based on two new autoinverse operations called extended XOR/XNOR; these new operations are generated from the XOR/XNOR operations, respectively. Two types of associative memory are generated with this model: the max type (XOR-AM max), which is constructed with the maximum of the extended XOR operation, and the min type (XOR-AM min), which is constructed with the minimum of the extended XNOR operation. The XOR-AM max exhibits tolerance against the presence of patterns distorted by dilative noise, whereas the XOR-AM min exhibits tolerance against the presence of patterns distorted by erosive noise; both types of memory converge in a single step, use the same extended XOR/XNOR operator for learning and recalling phases, operate in heteroassociative and autoassociative modes, and show infinite storage capacity for the autoassociative mode. Finally, computer simulation results are presented for the new memories based on the extended XOR/XNOR (XOR-AM), which have better or equal performance compared to other associative memories. For the experiments with mixed noise, the conditions established by the kernel method proposed by Ritter for Morphological Associative Memories were conserved, and the solution algorithm proposed by Hattori for the construction of the kernel patterns of these memories was modified.

Hardware search engines (HSEs) have been drawing significant attention in replacing software search algorithms in order to speed up location access and data association in modern systems. Content addressable memory (CAM) is one of the promising HSEs due to its parallel search accessibility. However, it is subjected to considerable dissipation which becomes severe while accessing many components including cells and associated matchlines (MLs) during every search. Ternary CAM (TCAM) based routing tables, especially employed in network systems for packet classification, has put a challenge to design energy-efficient architectures with high-performance and reliable look-up operation. Precharge-free CAM schemes are preferred solutions over precharge types to accomplish high-speed as well as low-power goals of associative memory design. In order to overcome the drawbacks of precharge based designs and also to improve performance during the search, we introduce a precharge-free ternary content addressable memory (PF-TCAM). The proposed searching approach enhances the rate of search by reducing half of the ML evaluation time as it eliminates precharge phase prior to every search by performing search in HALF clock cycle. A $32\times 16$ -bit proposed macro is designed using 45-nm CMOS technology and post-layout simulations at 1 V supply shows 56% and 63% energy efficiency improvements compared to conventional TCAM and compact TCAM respectively over 25 different search keys despite increasing evaluation speed by 50% with an area overhead of 1 transistor/cell over compact TCAM.

In this study, we use a novel, implicit memory paradigm to test forhyper-binding, or older adults’ tendency to form non-target associations. Participants viewed pictures of objects superimposed wit...

Despite large individual differences in memory performance, people remember certain stimuli with overwhelming consistency. This phenomenon is referred to as the memorability of an individual item. However, it remains unknown whether memorability also affects our ability to retrieve associations between items. Here, using a paired-associates verbal memory task, we combine behavioural data, computational modelling and direct recordings from the human brain to examine how memorability influences associative memory retrieval. We find that certain words are correctly retrieved across participants irrespective of the cues used to initiate memory retrieval. These words, which share greater semantic similarity with other words, are more readily available during retrieval and lead to more intrusions when retrieval fails. Successful retrieval of these memorable items, relative to less memorable ones, results in faster reinstatement of neural activity in the anterior temporal lobe. Collectively, our data reveal how the brain prioritizes certain information to facilitate memory retrieval. Xie et al. combine evidence from behavioural data, computational modelling and intracranial electroencephalogram methods to show that the human brain prioritizes certain information to facilitate associative memory retrieval.

This study was designed to assess the effects of acute exercise on performance of a paired associate learning (PAL) test, an operationalization of hippocampal-dependent associative memory. Participants performed a PAL test and then ran on a treadmill (exercise group, n = 52) or solved Sudoku puzzles (control group, n = 54). Participants returned 2, 5, or 8 hr later to perform a second, different, PAL test. PAL scores for the control group did not change over time. Similarly, scores on tests taken 2 and 5 hr after exercise were not different from baseline or control data. Scores on tests taken 8 hr after exercise, however, fell significantly below baseline (by 8.6%) and control (by 9.8%) scores. These data demonstrate that acute exercise can negatively affect the encoding and retrieval of new information even hours after the exercise bout, which should be a consideration when designing exercise programs to enhance, and not hinder, learning.

Due to the fully-connected complex structure of Artificial Neural Networks (ANNs), systems based on ANN may consume much computational time, energy and space. Therefore, intense research has been recently centered on changing the topology and design of ANNs to obtain high performance. To explore the influence of network structure on ANNs complex systems topologies have been applied in these networks to have more efficient and less complex structures while they are more similar to biological systems at the same time. In this paper, the methodology and results of some recent papers are summarized and discussed in which the authors investigated the efficacy of random complex networks on the performance of Hopfield associative memory and multi-layer ANNs compared with ANNs with small-world, scale-free and regular structures.

Olfactory memory is one of the most ancient cognitive systems in the mammal brain and has its own neural structures and mechanisms. Most behavioral models of olfactory memory in laboratory animals use several training sessions, which significantly hinders the detection and study of the neural networks of the brain involved in the acquisition of this type of memory and which undergo derangement in pathology. We have developed a mouse model of the acquisition of conditioned reflex freezing in response to the odor of amyl acetate in which learning time was 40–80 sec and the associative memory, was long-term and persisted for at least two weeks. Our model allows for precise identification of the moment of formation of olfactory memory to provide for subsequent studies of the neural networks involved in this process in health and disease.

Statistical learning, the process of tracking regularities in the environment, has been systematically investigated across development and in younger adulthood over the past two decades. Research investigating statistical learning abilities in older adults has been more limited, though existing studies suggest there are age-related declines. Here, we aim to further understand older adults’ ability to track statistical regularities using a cross-situational statistical learning paradigm in which learners map novel words to novel objects by tracking associations over time. In Experiment 1, we manipulated task difficulty by increasing the number of competitors and found an overall age deficit but no interaction between age and difficulty, indicating that difficulty similarly impacts older and younger adults. In Experiment 2, we provided extended practice with a first set of object-word mappings before switching to a second learning phase in which learners could remap a subset of previously learned words to novel objects. Based on hyper-binding, we posited that older adults might be more willing to remap previously learned words to novel objects. However, despite overall poorer learning of the primary association, older adults were actually less likely to remap previously learned words to novel objects relative to young adults. Thus, we demonstrate that even though older adults may have an associative memory deficit, learned associations are not more weakly bound for older relative to younger adults.

The traditional Pavlov associative memory contains the process of learning and forgetting, which correspond to the reinforcement and extinction in classical conditional reflex respectively. In fact, besides reinforcement and extinction, classical conditional reflex also contains generalization and differentiation. In this paper, a memristor-based circuit is designed to implement generalization and differentiation based on Pavlov associative memory. This circuit can respond to one kind of conditional stimulus after the initial learning, then when another similar stimulus is applied to the circuit, the circuit responds similarily, this is the phenomenon of generalization. To make the circuit be sufficiently cognizant of these two similar stimuli and ultimately distinguish them, a special training method, which selectively reinforces one stimulus while inhibits another similar stimulus, is used to train the whole circuit. As a result, when these two similar stimuli are applied to the circuit separately again, it can respond to them differently. This is the phenomenon of differentiation. Finally, the correctness of implementing these functions described above are demonstrated by simulation results in PSPICE.

Background Despite its potential to revolutionize the treatment of memory dysfunction, the efficacy of direct electrical hippocampal stimulation for memory performance has not yet been well characterized. One of the main challenges to cross-study comparison in this area of research is the diversity of the cognitive tasks used to measure memory performance. Objective We hypothesized that the tasks that differentially engage the hippocampus may be differentially influenced by hippocampal stimulation and the behavioral effects would be related to the underlying hippocampal activity. Methods To investigate this issue, we recorded intracranial EEG from and directly applied stimulation to the hippocampus of 10 epilepsy patients while they performed two different verbal memory tasks – a word pair associative memory task and a single item memory task. Results Hippocampal stimulation modulated memory performance in a task-dependent manner, improving associative memory performance, while impairing item memory performance. In addition, subjects with poorer baseline cognitive function improved much more with stimulation. iEEG recordings from the hippocampus during non-stimulation encoding blocks revealed that the associative memory task elicited stronger theta oscillations than did item memory and that stronger theta power was related to memory performance. Conclusions We show here for the first time that stimulation-induced associative memory enhancement was linked to increased theta power during retrieval. These results suggest that hippocampal stimulation enhances associative memory but not item memory because it engages more hippocampal theta activity and that, in general, increasing hippocampal theta may provide a neural mechanism for successful memory enhancement.

Memory-based reconfigurable computing rose to outperform conventional FPGAs in logic function density, as well as being less impacted by the programmable interconnection. However, the major drawback is that the area and power overheads are significant, and this gets worse in high-density, high-performance Memory-based Computing (MBC) devices. In this brief, we investigate the usage of analog memristive CAMs to improve the area density of MBCs, thus enabling denser memories and smaller power consumption. Simulation results indicate significant area and energy savings, up to $2.2\times $ and $2.6\times $ , respectively, compared to the non-volatile counterparts, at the cost of increasing latency by up to 20%.

In order to accelerate the convergence speed of iterative learning control (ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is pre-corrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.

Oscillatory neural networks based on insulator to metal transition of VO2 switches are implemented for image recognition. The VO2 oscillators are fabricated on silicon in a CMOS compatible process. A fully-connected network of coupled oscillators is investigated using programmable resistors as coupling elements. In this approach, input of the image information and data processing is performed in the time domain. In particular, tuning the coupling resistors allows to control the phase-relation between the oscillators. This is used to memorize and recognize patterns in an analog circuit. The concept is demonstrated experimentally on a three-VO2 oscillator network, whereas simulations are performed on a larger 9-oscillators circuit.

Static random access memory (SRAM)-based ternary content-addressable memory (TCAM) on field-programmable gate arrays (FPGAs) is used for packet classification in software-defined networking (SDN) and OpenFlow applications. SRAMs implementing TCAM contents constitute the major part of a TCAM design on FPGAs, which are vulnerable to soft errors. The protection of SRAM-based TCAMs against soft errors is challenging without compromising critical path delay and maintaining a high search performance. This brief presents a low-cost and low-response-time technique for the protection of SRAM-based TCAMs. This technique uses simple, single-bit parity for fault detection which has a minimal critical path overhead. This technique exploits the binary-encoded TCAM table maintained in SRAM-based TCAMs for update purposes to implement a low-response-time error-correction mechanism at low cost. The error-correction process is carried out in the background, allowing lookup operations to be performed simultaneously, thus maintaining a high search performance. The proposed technique provides protection against soft errors with a response time of 293 ns, whereas maintaining a search rate of 222 million searches per second on a $1024\times40$ size TCAM on Artix-7 FPGA.

In this paper, a full-regulated neural network (NN) with a double hidden layer recurrent neural network (DHLRNN) structure is designed, and an adaptive global sliding-mode controller based on the DHLRNN is proposed for a class of dynamic systems. Theoretical guidance and adaptive adjustment mechanism are established to set up the base width and central vector of the Gaussian function in the DHLRNN structure, where six sets of parameters can be adaptively stabilized to their best values according to different inputs. The new DHLRNN can improve the accuracy and generalization ability of the network, reduce the number of network weights, and accelerate the network training speed due to the strong fitting and presentation ability of two-layer activation functions compared with a general NN with a single hidden layer. Since the neurons of input layer can receive signals which come back from the neurons of output layer in the output feedback neural structure, it can possess associative memory and rapid system convergence, achieving better approximation and superior dynamic capability. Simulation and experiment on an active power filter are carried out to indicate the excellent static and dynamic performances of the proposed DHLRNN-based adaptive global sliding-mode controller, verifying its best approximation performance and the most stable internal state compared with other schemes.

Performance of a memory depends on the storage stability, yield and sensing speed. Differential input and the latching time of sense amplifiers are considered as primary performance factors in static random access memory. In a content addressable memory (CAM), the sensing is carried out through the matchline (ML) and the time for evaluation is the key to decide the search speed. The density of CAM is on a rise to accommodate a higher amount of information which increases the power dissipation associated with it. Issues such as the logical threshold variation and lower noise margin between match and mismatch are critical in the operation of a CAM. A good ML sensing technique can reduce the ML power with enhanced evaluation speed. This work provides an analogy of various ML sensing techniques based on their pre-charging, evaluation and performance improvement strategies. Estimation on the power dissipation and evaluation time are made and in-depth analysis on their power-speed-overhead trade-off are carried on 64-bit CAM macros.

Hamming Distance (HD) is a popular similarity measure that is used widely in pattern matching applications, DNA sequencing, and binary error-correcting codes. In this article, we extend our previous work to prove that our HD circuit is scalable, tolerant to memristor model variability, and tolerant to device-to-device variation. We showed that the operation of our circuit under non-ideal fabrication conditions changes slightly, decreasing the correct classification rates for the MNIST handwritten digits dataset by n × faster than other HD circuits, where n is the number of HDs to be computed, and it consumes ≈100× − 1,000× less power compared to other memristive and CMOS HD circuits. Used in a full HD Associative Content Addressable Memory (ACAM), the proposed HD circuit consumes only 2.2% of the total system power. Our state-of-the-art, low-power, and fast HD circuit is relevant for a wide range of applications.

In this paper, a self-regulated double hidden layer output feedback neural network (DHLFNN) is presented to control an active power filter (APF) system as a current controller, which is conducive to the improvement of the response characteristic and power quality. First, a global sliding mode controller is introduced because it is effective in achieving overall robustness during the system response. A new output feedback neural structure that has two hidden layers is proposed to make the parameters adaptively adjust themselves and stabilize to their best values. A higher accuracy and stronger generalization ability can be also obtained by reducing the number of network weights and accelerating the network training speed owing to the strong fitting and presentation ability of two-layer activation functions. Furthermore, the designed feedback loops of the neural network play a significant role in possessing associative memory and rapid system convergence. This proposed double hidden layer output feedback neural based global sliding mode controller is simulated on the model of APF and the results show the excellent static and dynamic properties. Experimental results under three cases and comparisons are provided using a fully digital control system to validate the superior performance of the proposed DHLFNN controller.

With the rapid growth of the Internet traffic and the intensity of online transactions taking place, it is expected that the current routing needs careful modifications and smart innovations to ensure effective and reliable end-to-end packet delivery. This involves new feature developments for handling traffic with reduced latency to tackle routing scalability issues in a more secure way and to offer new services at cheaper cost. Considering the fact that prices of DRAM (Dynamic Random Access Memory) or TCAM (Ternary Content-Addressable Memory) in legacy routers are not necessarily decreasing at a desired pace, cloud computing can be a great solution to manage the increasing computation and memory complexity of routing functions in a centralized manner with optimized expenses. Such cloud integration to routing is becoming plausible as cloud providers now offer various pricing schemes and provide large-scale computing infrastructure to meet the users’ choice. Focusing on the attributes associated with existing routing cost models and by exploring a hybrid approach to SDN, we compare recent trends in cloud pricing (for both storage and service) to evaluate whether it would be economically beneficial to integrate cloud services with legacy routing for improved cost-efficiency.

Chaotic neural networks is widely used in associative memory because of its abundant chaotic behavior. The bridge synaptic circuit of the memristor has been mostly used in artificial neural networks, because of its synapse-like and non-volatile properties, but the weight addition circuit has a complicated structure, the high power consumption and the high complexity of the network, so the associative memory neural network circuit is still less implemented. In this paper, the memory characteristics of the threshold memristor is used to build the synaptic circuit, on the one hand, when the continuous voltage is applied to the memristor to alter its memristance, it can realize continuous synaptic weights from - 1 to 1. Synaptic weight circuit has simple structure and low energy consumption, due to the configurability of the threshold memristor, and different weights can be obtained in the same circuits to achieve the function of associative memory. On the other hand, we can realize self-associative memory, hetero-associative memory, the separation of superimposed patterns, many-to-many associative memory and application in the three-view drawing, through simulation experiments. Because of the nanoscale characteristics of memristor, the hardware implementation of large-scale chaotic neural network will has simplified structure and be integrated easily.

Theta frequency (4-8 Hz) fluctuations of the local field potential have long been implicated in learning and memory. Human studies of episodic memory, however, have provided mixed evidence for theta's role in successful learning and remembering. Re-evaluating these conflicting findings leads us to conclude that: (i) successful memory is associated both with increased narrow-band theta oscillations and a broad-band tilt of the power spectrum; (ii) theta oscillations specifically support associative memory, whereas the spectral tilt reflects a general index of activation; and (iii) different cognitive contrasts (generalized versus specific to memory), recording techniques (invasive versus noninvasive), and referencing schemes (local versus global) alter the balance between the two phenomena to make one or the other more easily detectable.

The ability to remember associations among components of an event, which is central to episodic memory, declines with normal aging. In accord with the specificity principle of memory (Surprenant & Neath, 2009), these declines may occur because associative memory requires retrieval of specific information. Guided by this principle, we endeavored to determine whether ubiquitous age-related deficits in associative memory (e.g., Naveh-Benjamin, 2000) are restricted to specific representations or extend to the gist of associations. Young and old adults (30 each in Experiment 1, 40 each in Experiment 2) studied face-scene pairs and were administered associative recognition tests following variable delays. Whereas both young and older adults could retrieve the gist of associations, older adults were impaired in their ability to retrieve more specific representations. Our results also show that associations can be retrieved from multiple levels of specificity, suggesting that episodic memory might be accessed on a continuum of specificity.

Recently, various magnetic non-volatile ternary content-addressable memory (MNV-TCAM) cells that use magnetic tunnel junctions (MTJs) as storage units have been proposed to realize zero standby power. However, they still suffer from low reliability and high power consumption for search operation. In addition, the restrictions on the ordering of the TCAM array required by the using of the priority encoder to resolve the multiple match problem due to the ‘X’ results in large delay and high energy consumption for update operation. To address these issues, the novel MNV-TCAM cell is first proposed, aiming to achieve high-speed, low-power, and high-reliable search operation. In addition to the search data operation, it can realize a search length operation. By exploiting this search length operation, we then present circuit-level design of the MNV-TCAM with a priority-decision in memory technology, which can utilize the length information of matched words sorted in an arbitrary order to decide the highest-priority matched word by performing a three-phase search operation. It implies that the restrictions on the ordering can be eliminated, thus, improving the update speed and energy-efficiency. Finally, hybrid CMOS/MTJ simulations are performed to demonstrate its functionality and evaluate its performance in terms of search reliability, search speed, and search energy consumption.

In this work, we present an experimental demonstration of a content addressable memory (CAM) cell based on ferroelectric HfO2 field effect transistors (FeFETs). Our proposed ferroelectric CAM (FeCAM) utilizes a CMOS-compatible ferroelectric material, hafnium zirconium oxide (HZO), as the gate dielectric. We discuss operation of the FeCAM cell and propose a suitable architecture to realize in-memory computation as well as single clock cycle content-driven search. In addition, the HZO FeFET is analyzed for its intrinsic memory characteristic, and design considerations are identified for improving device and therefore projected system-level performance. Our results indicate that FeCAM is well-suited to accommodate demanding modern computational needs by sealing the gaps between conventional memory, logic, and continued device scaling.

In this paper, an image encryption algorithm is proposed for the first time, which uses coding deoxyribonucleic acid (DNA) sequences to transcript its complementary messenger ribonucleic acid (mRNA) sequence. A binary associative memory neural network (BAM NN) is trained to produce the required transfer ribonucleic acid (tRNA) sequences to bond with the mRNA sequences to assign the correct amino acids. DNA and mRNA sequences, together with tRNA sequences, provide the permutation and diffusion operations for the pixels in colour digital imaging and communications in medicine images. Then, keys, which are determined by the generated amino acids, are used to encrypt the diffused cipher pixels further. Also key sharing has been carried out using Universal Software Radio Peripheral (USRP), cloud infrastructure and One Time Password (OTP) generation mechanisms.The different sequence formation can also identify any alterations made by the intruder in the amino acid, which results in the wrong set of key generation. Various decryption quality analyses, statistical and differential attack analyses (namely mean square error, unified average changing intensity, peak signal-to-noise ratio, number of pixel changing rate, entropy, mean absolute error, key sensitivity, cropping attacks, and chosen-plaintext attacks), and correlation tests were carried out to confirm the robustness of the proposed algorithm.

The passivity, low power consumption, memory characteristics and nanometer size of memristors make them the best choice to simulate synapses in artificial neural networks. In this paper, based on the proposed associative memory rules, we design a memristor neural network with plasticity synapses, which can perform analog operations similar to its biological behavior. For the memristor neural network circuit, we also construct a relatively simple Pavlov’s dog experiment simulation circuit, which can effectively reduce the complexity and power consumption of the network. Some advanced neural activities including learning, associative memory and three kinds of forgetting are realized based on the spiking-rate-dependent plasticity rule. Finally, the Simulation program with integrated circuit emphasis is used to simulate the circuit. The simulation results not only prove the correctness of the design, but also help to realize more efficient, simpler and more complex analog circuit of memristor neural network and then help to realize more intelligent, smaller and low-power brain chips.

Content Addressable Memory (CAM) is used in high speed searching applications and also in data compression. Recently in the network computing era, fast lookup tables are required for address resolution in network switches and routers such as LAN bridges/switches, ATM switches, and layer-3 switches. High search speed is obtained by parallel comparison. But power consumption is high in case of parallel comparison. In precomputation based CAM (PB-CAM) the comparison operation is divided into two stages to reduce the massive comparison operations in data searches. Precomputation is the way of comparing the extracted parameter from the input data in the first stage. The second stage comparison is done between the matched output of the first stage and the input data. In this paper, a Remainder Function parameter extractor is proposed as a precomputation technique to enhance the performance of low power PB-CAM. The experimental results obtained using 90 nm CMOS technology in the Cadence virtuoso show that the proposed approach achieves an average of 91% in power reduction.

Dissociation between the output of the circadian clock and external environmental cues is a major cause of human cognitive dysfunction. While the effects of ablation of the molecular clock on memory have been studied in many systems, little has been done to test the role of specific clock circuit output signals. To address this gap, we examined the effects of mutations ofPigment-dispersing factor(Pdf) and its receptor,Pdfr, on associative memory in male and femaleDrosophila. Loss of PDF signaling significantly decreases the ability to form associative memory. Appetitive short-term memory (STM), which in wild-type (WT) is time-of-day (TOD) independent, is decreased across the day by mutation ofPdforPdfr, but more substantially in the morning than in the evening. This defect is because of PDFR expression in adult neurons outside the core clock circuit and the mushroom body (MB) Kenyon cells (KCs). The acquisition of a TOD difference in mutants implies the existence of multiple oscillators that act to normalize memory formation across the day for appetitive processes. Interestingly, aversive STM requires PDF but not PDFR, suggesting that there are valence-specific pathways downstream of PDF that regulate memory formation. These data argue that the circadian clock uses circuit-specific and molecularly diverse output pathways to enhance the ability of animals to optimize responses to changing conditions.SIGNIFICANCE STATEMENTFrom humans to invertebrates, cognitive processes are influenced by organisms' internal circadian clocks, the pace of which is linked to the solar cycle. Disruption of this link is increasingly common (e.g., jetlag, social jetlag disorders) and causes cognitive impairments that are costly and long lasting. A detailed understanding of how the internal clock regulates cognition is critical for the development of therapeutic methods. Here, we show for the first time that olfactory associative memory inDrosophilarequires signaling by Pigment-dispersing factor (PDF), a neuromodulatory signaling peptide produced only by circadian clock circuit neurons. We also find a novel role for the clock circuit in stabilizing appetitive sucrose/odor memory across the day.

Ternary content addressable memories (TCAMs) have a lookup complexity of O(1). They require just one memory access to find an entry. For this reason, they are widely used in core routers of the Internet to achieve high packet forwarding capacities in terms of several 100 Gb/s. However, in a TCAM, the routing table needs to be stored in a sorted order according to the prefix length of the entries. Therefore, if a new entry needs to be added or an old one needs to be deleted, some other entries should be moved around to open a place for the new entry or fill the place of the old one. Practical methods to update a routing table require locking the TCAM during the update process. Therefore, this will cause a large number of packets to be dropped. This is because in the core routers of the Internet, the packets arrive at very high rates, and the routing table updates are very frequent. In this paper, we introduce a new, yet simple TCAM architecture in which entries do not need to be sorted. This means that updating the routing table will be done with a complexity of O(1), and therefore, no packets will be dropped.

Denial of Service (DoS) attack is a serious threat to Software Defined Network (SDN). Although many research efforts have been devoted to identify new features for DoS attack detection, the existing approaches are not able to detect various types of DoS attacks. In SDN, DoS attacks against data plane are mainly organized in two ways: 1) DoS attack with multiple flow entries (M-DoS) to exhaust the Ternary Content-Addressable Memory (TCAM) resource of the switch. 2) DoS attack with a single well-designed entry (S-DoS) to overwhelm the target link and further impact the controller. To detect these two attacks, we propose a new approach by extracting six features of flow table, and using the back propagation (BP) neural network to construct the classifier. Test results of test-bed experiments indicate that the accurate detection probability of proposed approach is 98.9%, which can effectively distinguish M-DoS flows and S-DoS flows without being affected by Flash crowd scene.

The development of hardware neural networks, including neuromorphic hardware, has been accelerated over the past few years. However, it is challenging to operate very large-scale neural networks with low-power hardware devices, partly due to signal transmissions through a massive number of interconnections. Our aim is to deal with the issue of communication cost from an algorithmic viewpoint and study learning algorithms for energy-efficient information processing. Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory. In the first approach following classical methods, we focus on sparse modular network structures inspired by biological brain networks and examine their storage capacity under an iterative learning rule. We show that incorporating long-range intermodule connections into purely modular networks can enhance the cost-performance ratio. In the second approach, we formulate for the first time an optimization problem where the network sparsity is maximized under the constraints imposed by a pattern embedding condition. We show that there is a tradeoff between the interconnection cost and the computational performance in the optimized networks. We demonstrate that the optimized networks can achieve a better cost-performance ratio compared with those considered in the first approach. We show the effectiveness of the optimization approach mainly using binary patterns and apply it also to gray-scale image restoration. Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.

A hybrid analog/digital very large-scale integration (VLSI) implementation of a spiking neural network with programmable synaptic weights was designed. The synaptic weight values are stored in an asynchronous module, which is interfaced to a fast current-mode event-driven DAC for producing synaptic currents with the appropriate amplitude values. It acts as a transceiver, receiving asynchronous events for input, performing neural computations with hybrid analog/digital circuits on the input spikes, and eventually producing digital asynchronous events in output. Input, output, and synaptic weight values are transmitted to/from the chip using a common communication protocol based on the address event representation (AER). Using this representation, it is possible to interface the device to a workstation or a microcontroller and explore the effect of different types of spike-timing dependent plasticity (STDP) learning algorithms for updating the synaptic weights values in the CAM module.

We study the stability of an oscillatory associative memory network consisting of $N$ coupled Kuramoto oscillators with applications in binary pattern retrieve. In this model, the coupling function...

A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25 Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10's of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10–100's ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceiver's responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors.

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and nonsparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their subtypes uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory subpopulations, one connected to excitatory assemblies locally and the other connected globally, can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model, while still theoretical, therefore highlights a potentially biologically-plausible and behaviorally-useful function of inhibitory diversity in memory.

Objective:The relevance of the episodic memory in the prediction of brain aging is well known. The Face Name Associative Memory Exam (FNAME) is a valued associative memory measure related to Alzheimer’s disease (AD) biomarkers, such as amyloid-β deposition preclinical AD individuals. Previous validation of the Spanish version of the FNAME test (S-FNAME) provided normative data and psychometric characteristics. The study was limited to subjects attending a memory clinic and included a reduced sample with gender inequality distribution. The purpose of this study was to assess S-FNAME psychometric properties and provide normative data in a larger independent sample of cognitively healthy individuals.Method:S-FNAME was administered to 511 cognitively healthy volunteers (242 women, aged 41–65 years) participating in the Barcelona Brain Health Initiative cohort study.Results:Factor analysis supported construct validity revealing two underlying components: face-name and face-occupation and explaining 95.34% of the total variance, with satisfactory goodness of fit. Correlations between S-FNAME and Rey Auditory-Verbal Learning Test were statistically significant and confirmed its convergent validity. We also found weak correlations with non-memory tests supporting divergent validity. Women showed better scores, and S-FNAME was positively correlated with education and negatively with age. Finally, we generated normative data.Conclusions:The S-FNAME test exhibits good psychometric properties, consistent with previous findings, resulting in a valid and reliable tool to assess episodic memory in cognitively healthy middle-aged adults. It is a promising test for the early detection of subtle memory dysfunction associated with abnormal brain aging.

In recent decades, with the rapid development of artificial intelligence technologies and bionic engineering, the spiking neural network (SNN), inspired by biological neural systems, has become one of the most promising research topics, enjoying numerous applications in various fields. Due to its complex structure, the simplification of SNN circuits requires serious consideration, along with their power consumption and space occupation. In this regard, the use of SSN circuits based on single-electron transistors (SETs) and modified memristor synapses is proposed herein. A prominent feature of SETs is Coulomb oscillation, which has characteristics similar to the pulses produced by spiking neurons. Here, a novel window function is used in the memristor model to improve the linearity of the memristor and solve the boundary and terminal lock problems. In addition, we modify the memristor synapse to achieve better weight control. Finally, to test the SNN constructed with SETs and memristor synapses, an associative memory learning process, including memory construction, loss, reconstruction, and change, is implemented in the circuit using the PSPICE simulator.

Metal–oxide memristive integrated technologies for analogue neuromorphic computing have undergone notable developments in the past decade, but are still not mature enough for very large-scale integration with complementary metal–oxide–semiconductor (CMOS) processes. Although non-volatile floating-gate synapse transistors are a more advanced technology embedded within CMOS processes, their performance as analogue resistive memories remains limited. Here, we report a low-power, two-terminal floating-gate transistor fabricated using standard single-poly technology in a commercial 180 nm CMOS process. Our device, which is integrated with a readout transistor, can operate in an energy-efficient subthreshold memristive mode. At the same time, it is linearized for small-signal changes with a two-orders-of-magnitude resistance dynamic range. Our device can be precisely tuned using optimized switching voltages and times, and can achieve 65 distinct resistive levels and ten-year analogue data retention. We experimentally demonstrate the feasibility of a selector-free integrated memristive array in basic neuromorphic applications, including spike-time-dependent plasticity, vector-matrix multiplication, associative memory and classification training. A floating-gate memristive device fabricated in a commercial 180 nm CMOS process can be integrated into a selector-free memristive array and used to demonstrate basic neuromorphic applications.

Identification of the neural mechanisms of associative memory is one of the fundamental tasks in neuroscience. We report here use of a model of associative learning in mice in which formation of a memory of a neutral context and subsequent association of transient presentation of this context with an unconditioned electrocutaneous stimulus (footshock) were separated in time. This provided for studies of relationship between different stages in the formation of this memory and protein synthesis and allowed us to study genomic activation of various brain structures during association and the memory of the context of the footshock. These studies showed that blockade of protein synthesis both during exploration of the novel context and during application of immediate footshock in an already familiar context impaired conditioned reflex freezing to this context. The association of the memory trace of the context of the footshock retrieved in response to a reminder was accompanied by activation of the expression of transcription factor c-Fos in the retrosplenial cortex. The retrosplenial cortex was also specifically activated by subsequent retrieval of the associative memory of the context. This activation was not seen in mice with impaired memory. No such differences were seen in the hippocampus, where activation of c-Fos expression occurred in relation to exploration of the novel context and not its association with the footshock. These results provide evidence that the retrosplenial cortex may be part of the neocortex supporting formation of associations between context and aversive unconditioned stimuli.

Olfaction allows animals to adapt their behavior in response to different chemical cues in their environment. How does the brain efficiently discriminate different odors to drive appropriate behavior, and how does it flexibly assign value to odors to adjust behavior according to experience? This review traces neuronal mechanisms underlying these processes in adult Drosophila melanogaster from olfactory receptors to higher brain centers. We highlight neural circuit principles such as lateral inhibition, segregation and integration of olfactory channels, temporal accumulation of sensory evidence, and compartmentalized synaptic plasticity underlying associative memory.