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Abstract At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain’s memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model’s unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent’s adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm’s and Kirchhoff’s laws, we validated the model’s efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level.

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain's memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model's unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent's adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm's and Kirchhoff's laws, we validated the model's efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs), along with long short-term memory networks (LSTMs), have been successfully used on a wide range of sequential data problems and have been entitled as extraordinarily powerful tools for learning and processing such data. However, the search for a new or derived architecture that would model very long-term dependencies is still an active area of research. In this paper, a relatively psychologically plausible architecture named event buffering JANET (EB-JANET) is proposed. The architecture is derived from the forgetgate-only version of the LSTM, which is also called just another network (JANET). The new architecture implements a new working memory mechanism that operates on information represented as dynamic events. The event buffer, as a container of events, is a reference to the state of the relevant pre-activation values on the basis of which historical candidate values were generated relative to the current timestep. The buffer is emptied as needed and depending on the context of information. The proposed architecture has achieved world-class results and it outperforms JANET on multiple benchmark datasets. Moreover, the new architecture is applicable to a wider class of problems and showed superior resilience when processing longer sequences, as opposed to JANET which experienced catastrophic failures on certain tasks. [ABSTRACT FROM AUTHOR]

Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations. [ABSTRACT FROM AUTHOR]

The retention of human memory is a process that can be understood from a hazard-function perspective. Hazard is the conditional probability of a state change at time t given that the state change did not yet occur. After reviewing the underlying mathematical results of hazard functions in general, there is an analysis of the hazard properties associated with nine theories of memory that emerged from psychological science. Five theories predict strictly monotonically decreasing hazard whereas the other four theories predict a peaked-shaped hazard function that rises initially to a peak and then decreases for longer time periods. Thus, the behavior of hazard shortly after the initial encoding is the critical difference among the theories. Several theorems provide a basis to explore hazard for the initial time period after encoding in terms of a more practical surrogate function that is linked to the behavior of the hazard function. Evidence for a peak-shaped hazard function is provided and a case is made for one particular psychological theory of memory that posits that memory encoding produces two redundant representations that have different hazard properties. One memory representation has increasing hazard while the other representation has decreasing hazard.

The retention of human memory is a process that can be understood from a hazard-function perspective. Hazard is the conditional probability of a state change at time t given that the state change did not yet occur. After reviewing the underlying mathematical results of hazard functions in general, there is an analysis of the hazard properties associated with nine theories of memory that emerged from psychological science. Five theories predict strictly monotonically decreasing hazard whereas the other four theories predict a peaked-shaped hazard function that rises initially to a peak and then decreases for longer time periods. Thus, the behavior of hazard shortly after the initial encoding is the critical difference among the theories. Several theorems provide a basis to explore hazard for the initial time period after encoding in terms of a more practical surrogate function that is linked to the behavior of the hazard function. Evidence for a peak-shaped hazard function is provided and a case is made for one particular psychological theory of memory that posits that memory encoding produces two redundant representations that have different hazard properties. One memory representation has increasing hazard while the other representation has decreasing hazard. [ABSTRACT FROM AUTHOR]

Memory models typically assume that recall is a two-stageprocess with learning affecting both processes to the samedegree. This equal learning assumption is difficult to reconcilewith studies of the 'testing effect', which reveal differentforgetting rates following learning from test practice versuslearning from restudy. Here we present a new memory model,termed Primary and Convergent Retrieval (PCR) that assumessuccessful recall leads to a selective enhancement for thesecond stage of recall (Convergent Retrieval). We applied thismodel to existing testing effect data. In two new experiments,we confirmed novel predictions of the PCR model for transferbetween retrieval cues and for recall latencies. This is the firstformally specified model of the testing effect and it has broadimplications for the nature of learning and retrieval.

While Resistive Random Access Memories (RRAM) are perceived nowadays as a promising solution for the future of computing, these technologies suffer from intrinsic variability regarding programming voltage, switching speed and achieved resistance values. Write Termination (WT) circuits are a potential solution to solve these issues. However, previously reported WT circuits do not demonstrate sufficient reliability. In this work, we propose an industrially-ready WT circuit that was simulated with a RRAM model calibrated on real measurements. We perform extensive CMOS and RRAM variability simulations to extract the actual performances of the proposed WT circuit. Finally, we simulate the effects of the proposed WT circuit with memory traces extracted from real Edge-level data-intensive applications. Overall, we demonstrate 2× to 40× of energy gains at bit level. Moreover, we show from 1.9× to 16.2× energy gains with real applications running depending on the application memory access pattern thanks to the proposed WT circuit.

Resistive switching conduction in Ni/HfO2/Si capacitors is studied at temperatures ranging from 77 to 473 K. A model for the low-resistance state (LRS) consistent with the experimental data is proposed. The LRS current–voltage ( I – V ) curves show a maximum resistance, R0 , at zero bias and a minimum value, R\infty , at voltages close to reset, which indicates a departure from linearity. A three-parameter model for the $I$ – $V$ curves is reported and its temperature dependence analyzed. [ABSTRACT FROM AUTHOR]

Using a field-programmable gate array (FPGA) development board, a digital signal processor (DSP) builder, and the phase-to-amplitude conversion principle, a low-cost system for measuring the amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) distortion curves of radio frequency (RF) power amplifiers (PAs) is presented. The state of the art based on the measurements and preliminary studies of AM/AM and AM/PM distortion curves is discussed. A full digital control of the test bed simulated/emulated in Matlab/Simulink is introduced to recalculate the known AM/AM and AM/PM measurements stored as look-up table (LUT). Finally, the low-cost system comprises the memory polynomial model (MPM) that involves the nonlinearity order and memory effects of real PAs. [ABSTRACT FROM AUTHOR]

Serial position functions with marked primacy and recency effects are ubiquitous in episodic memory tasks. The demonstrations reported here explored whether bow-shaped serial position functions would be observed when people ordered exemplars from various categories along a specified dimension. The categories and dimensions were: actors and age; animals and weight; basketball players and height; countries and area; and planets and diameter. In all cases, a serial position function was observed: People were more accurate to order the youngest and oldest actors, the lightest and heaviest animals, the shortest and tallest basketball players, the smallest and largest countries, and the smallest and largest planets, relative to intermediate items. The results support an explanation of serial position functions based on relative distinctiveness, which predicts that serial position functions will be observed whenever a set of items can be sensibly ordered along a particular dimension. The serial position function arises because the first and last items enjoy a benefit of having no competitors on 1 side and therefore have enhanced distinctiveness relative to mid-dimension items, which suffer by having many competitors on both sides. [ABSTRACT FROM AUTHOR]

An emulation tool with the capability of modeling the nonlinearity order and memory effects for real power amplifier’s (PAs) conversion curves, is introduced. The proposed tool comprises special cases of Volterra series as the Memory Polynomial Model and a learning technique like artificial neural networks. The proposed system is a novel integrated one able to model the real behavior of radio frequency (RF)-PAs under different memory models. The developed system starts from a test bed able to acquire AM–AM and AM–PM distortion curves measurements. This procedure can predict the behavior and improve the analysis using it as a design tool. [ABSTRACT FROM AUTHOR]

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Emerging nonvolatile memories based on resistive switching mechanisms pull intense research and development efforts from both academia and industry. Oxide-based resistive random access memories (OxRAM) gather noteworthy performances, such as fast WRITE/READ speed, low power, high endurance, and large integration density that outperform conventional flash memories. To fully explore new design concepts, such as distributed memory in logic or biomimetic architectures, robust OxRAM compact models must be developed and implemented into electrical simulators to assess performances at a circuit level. In this paper, we propose a physics-based compact model used in electrical simulator for bipolar OxRAM memories. After uncovering the theoretical background and the set of relevant physical parameters, this model is confronted to experimental electrical data. The excellent agreement with these data suggests that this model can be confidently implemented into circuit simulators for design purpose. [ABSTRACT FROM PUBLISHER]

Set and reset characteristics are studied for unipolar and bipolar metal-oxide resistive-switching memory devices. We show a universal dependence of set-state resistance and reset current on the compliance current used during set, with negligible impact of metal-oxide composition and switching condition. An analytical Joule-heating model for universal reset is presented, predicting a weak dependence of reset temperature and voltage on diffusion and migration parameters in both unipolar- and bipolar-switching modes. Data for the reset voltage are shown for a wide range of unipolar and bipolar metal oxides, in support of our calculations. [ABSTRACT FROM AUTHOR]

Abstract: We used quantitative modeling of remember–know responses () to investigate the processes underlying recognition memory for faces in the choice-similarity paradigm (). Similarity between recognition test choices produces opposite effects on confidence and accuracy in this paradigm. We extended existing models of this double dissociation to account for remember–know responses, by adding a variable recollection criterion to single-process model and by adding graded recollection strength to dual-process model. Both models provided an accurate and comprehensive account of objective and subjective judgments in an experiment we conducted on memory for faces, and of data from Dobbins et al. on memory for natural scenes. Model selection techniques were used to refine these accounts, providing insight into the psychological processes proposed by each approach and into their implications for the relationship between recollection and confidence in two-alternative forced choice recognition. [Copyright &y& Elsevier]

Many models of immediate memory predict the presence or absence of various effects, but none have been tested to see whether they predict an appropriate distribution of effect sizes. The authors show that the feature model (J. S. Nairne, 1990) produces appropriate distributions of effect sizes for both the phonological confusion effect and the word-length effect. The model produces the appropriate number of reversals, when participants are more accurate with similar items or long items, and also correctly predicts that participants performing less well overall demonstrate smaller and less reliable phonological similarity and word-length effects and are more likely to show reversals. These patterns appear within the model without the need to assume a change in encoding or rehearsal strategy or the deployment of a different storage buffer. The implications of these results and the wider applicability of the distribution-modeling approach are discussed. [ABSTRACT FROM AUTHOR]

The resistive switching memory (RRAM) offers fast switching, low-voltage operation, and scalable device area. However, reliability and variability issues must be understood, particularly in the low-current operation regime. This letter addresses set-state variability and presents a new set failure phenomenon in RRAM, leading to a high-resistance tail in the set-state distribution. The set failure is due to complementary switching of the RRAM, causing an increase of resistance soon after the set transition. The dependence of set failure on the programing current is explained by the increasing voltage stress across the RRAM device causing filament disconnection. [ABSTRACT FROM PUBLISHER]