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About 10,000 papers for “Memory errors”

Associative recall of memory without errors

Ido Kanter, Haim Sompolinsky · Physical review. A, General physics · 1987

A neural network which is capable of recalling without errors any set of linearly independent patterns is studied. The network is based on a Hamiltonian version of the model of Personnaz et al. The energy of a state of N (\ifmmode\pm\else\textpm\fi{}1) neurons is the square of the Euclidean distance---in phase space---between the state and the linear subspace spanned by the patterns. This energy corresponds to nonlocal updatings of the synapses in the learning mode. Results of the mean-field theory (MFT) of the system as well as computer simulations are presented. The stable and metastable states of the network are studied as a function of ``temperature'' T and \ensuremath{\alpha}=p/N, where p is the number of embedded patterns. The maximum capacity of the network is \ensuremath{\alpha}=1. For all \ensuremath{\alpha} (0\ensuremath{\le}\ensuremath{\alpha}<1) the embedded patterns are not only locally stable but are global minima of the energy. The patterns appear, as metastable states, below a temperature T=${T}_{M}$(\ensuremath{\alpha}). The temperature ${T}_{M}$(\ensuremath{\alpha}) decreases to zero as \ensuremath{\alpha}\ensuremath{\rightarrow}1. The spurious states of the network are studied in detail in the case of random uncorrelated patterns. At finite p, they are identical to the mixture states of Hopfield's model. At finite \ensuremath{\alpha}, a spin-glass phase exists as a metastable state. According to the replica symmetric MFT the spin-glass state becomes degenerate with the patterns at \ensuremath{\alpha}=${\ensuremath{\alpha}}_{g}$=1-2/\ensuremath{\pi} and disappears above it. Possible interpretations of this unusual result are discussed. The average radius of attraction R of the patterns has been determined by computer simulations, for sizes up to N=400. The value of R for 0<\ensuremath{\alpha}<1 depends on the details of the dynamics. Results for both parallel and serial dynamics are presented. In both cases R is unity (the largest distance in phase space by definition) at \ensuremath{\alpha}\ensuremath{\rightarrow}0 and decreases monotonically to zero as \ensuremath{\alpha}\ensuremath{\rightarrow}1. Contrary to the MFT, simulations have not revealed, so far, any singularity in the properties of the spurious states at an intermediate value of \ensuremath{\alpha}.

Cited by 376

Password Entry Errors: Memory or Motor?

Kristen Greene, Franklin P. Tamborello · 2015

As we increasingly rely upon our computer information systems to store and operate on sensitive information, the methods we use to authenticate user identity also become more important. One of the most important such methods is the password. However, passwords that provide better security also tend to be more difficult to remember. They also tend to be difficult to type, and typing errors can have negative consequences such as being locked out of a critical information system. We present a computational cognitive model of password rehearsal and a typing extension to the ACT-R cognitive architecture intended to study humancomputer interaction issues in the usable security domain.

Cited by 0

Detecting Memory Errors via Static Pointer Analysis

Nurit Dor · 1999

We study the applicability of pointer analysis algorithms (originally aimed at optimizing compilers) to identify potential errors such as dereferencing NULL pointers in C programs, by statically analyzing the behavior of programs on all their input data. The algorithms are conservative, i.e., they never miss an error but may also create "false alarms". Our goal is to identify the "core program analysis techniques" that are needed to develop a realistic tool that does not generate too many false alarms. Our experience indicates that the following techniques are necessary: (i) finding aliases between pointers, (ii) flow sensitive techniques that account for the program control flow constructs, (iii) partial interpretation of conditional statements, (iv) analysis of relationships between pointers, and sometimes (v) analysis of the underlying data structures manipulated by the C program. Our experimental work shows that the combination of these techniques yields b...

Cited by 0

Amphetamine Increases Errors During Episodic Memory Retrieval

Michael E. Ballard, David A. Gallo, Harriet de Wit · Journal of Clinical Psychopharmacology · 2013

Moderate doses of stimulant drugs are known to enhance memory encoding and consolidation, but their effects on memory retrieval have not been explored in depth. In laboratory animals, stimulants seem to improve retrieval of emotional memories, but comparable studies have not been carried out in humans. In the present study, we examined the effects of dextroamphetamine (AMP) on retrieval of emotional and unemotional stimuli in healthy young adults, using doses that enhanced memory formation when administered before encoding in our previous study. During 3 sessions, healthy volunteers (n = 31) received 2 doses of AMP (10 and 20 mg) and placebo in counterbalanced order under double-blind conditions. During each session, they first viewed emotional and unemotional pictures and words in a drug-free state, and then 2 days later their memory was tested, 1 hour after AMP or placebo administration. Dextroamphetamine did not affect the number of emotional or unemotional stimuli remembered, but both doses increased recall intrusions and false recognition. Dextroamphetamine (20 mg) also increased the number of positively rated picture descriptions and words generated during free recall. These data provide the first evidence that therapeutic range doses of stimulant drugs can increase memory retrieval errors. The ability of AMP to positively bias recollection of prior events could contribute to its potential for abuse.

Cited by 38Open access

Hardening Software Against Memory Errors and Attacks

Novark, Albert Eugene · Scholarworks (University of Massachusetts Amherst) · 2022

Programs written in C and C++ are susceptible to a number of memory errors, including buffer overflows and dangling pointers. At best, these errors cause crashes or performance degradation. At worst, they enable security vulnerabilities, allowing denial-of-service or remote code execution. Existing runtime systems provide little protection against these errors. They allow minor errors to cause crashes and allow attackers to consistently exploit vulnerabilities. In this thesis, we introduce a series of runtime systems that protect deployed applications from memory errors. To guide the design of our systems, we analyze how errors interact with memory allocators to allow consistent exploitation of vulnerabilities. Our systems improve software in two ways: first, they tolerate memory errors, allowing programs to continue proper execution. Second, they decrease the probability of successfully exploiting security vulnerabilities caused by memory errors. Our first system, Archipelago, protects exceptionally sensitive server applications against severe errors using an object-per-page randomized allocator. It provides near-100% protection against most buffer overflows. Our second system, DieHarder, combines ideas from Archipelago, DieHard, and other systems to enable maximal protection against attacks while incurring minimal runtime and memory overhead. Our final system, Exterminator, automatically corrects heap-based buffer overflows and dangling pointers without requiring programmer intervention. Exterminator relies on both a low-overhead randomized allocator and statistical inference techniques to automatically isolate and correct errors in deployed applications.

Cited by 0Open access

Verbal Memory Errors and Symptoms in Schizophrenia

R. Walter Heinrichs, Stephanie McDermid Vaz · Cognitive and Behavioral Neurology · 2004

Impaired recall memory predicts levels of nonspecific psychopathology in schizophrenia. Negative symptoms associate with low intrusion error rates, but there is no evidence of an association between elevated errors and positive symptoms.

Cited by 26

A memory of errors in sensorimotor learning

David J. Herzfeld, Pavan A. Vaswani, Mollie K. Marko et al. · Science · 2014

The current view of motor learning suggests that when we revisit a task, the brain recalls the motor commands it previously learned. In this view, motor memory is a memory of motor commands, acquired through trial-and-error and reinforcement. Here we show that the brain controls how much it is willing to learn from the current error through a principled mechanism that depends on the history of past errors. This suggests that the brain stores a previously unknown form of memory, a memory of errors. A mathematical formulation of this idea provides insights into a host of puzzling experimental data, including savings and meta-learning, demonstrating that when we are better at a motor task, it is partly because the brain recognizes the errors it experienced before.

Cited by 381Open access

Working Memory Load Strengthens Reward Prediction Errors

Anne Collins, Brittany Ciullo, Michael J. Frank et al. · Journal of Neuroscience · 2017

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning.<b>SIGNIFICANCE STATEMENT</b> Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors.

Cited by 153Open access

A direct realist understanding of memory errors

Li Mei Chew · Figshare · 2016

Memory errors are part and parcel of everyday remembering. While memory and errors in general, present as some of strongest evidence in support for representationalism, memory and error are also two of bigger challenges for direct realism. In fact, critics regard both memory and errors as insurmountable logical problems for direct realism, which makes memory error a unique challenge for direct realism. With regards to memory, firstly, the problem is if the direct realist claims that remembering is a direct relation to a past event, how does the subject who is engaged in the present act of remembering have direct relation to a past event that no long exists? With regard to error, the problem is that if there is a direct relation to the object of cognition as direct realism claims then what is the object of cognition when we are in error? In the case of memory errors then, the challenge is that not only is the act of remembering taking place separate from the initial time the event has occurred but the direct realist needs also to account for how the subject, who has a direct independent relation to the event, can be mistaken about the past event he or she is remembering. The present thesis aim is to take steps to show how direct realist will take the first steps to address the problems of memory error. It will be shown that a variety of direct realism known as situational realism that defines psychology as the study of organism environmental relations and the interactions involving cognition, emotion, and motivation will be able to address the challenges. It will be argued that it is only by taking into consideration both memory and error together that a greater sense of error can be understood from a direct realist perspective.

Cited by 0Open access

Avoiding Out-Of-Memory Errors in ThinGC

Ernstedt, Pontus · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2020

A new garbage collector, called ThinGC, is producing out-of-memory errors when applied to certain user applications. ThinGC introduces a classification of objects as either hot or cold, where hot objects are currently in use by the application and cold objects are not. The heap is partitioned into two isolated areas, hot storage and coldstorage, separating the differently classified objects. When cold objects are needed bythe application again, they are first moved back into hot storage - a process called reheating. Reheating requires that space is available in hot storage to accommodate the object. If the reheating fails, ThinGC crashes reporting an out-of-memory error, as ThinGC disallows hot objects to reside in cold storage. As it stands, ThinGC's approach in performing the reheat is flawed, leading to manyfailed reheats when running reheat-prone applications with limited memory space.This thesis sets out to reduce the amount of out-of-memory errors produced byreheats in ThinGC. A strategy which reserves hot storage space specifically forreheats is proposed, and implemented into ThinGC. Evaluation shows that the strategy is successful in avoiding applications crashing. Our implementation comes of short, however, as it puts the burden on the user to configure the amount of memory to reserve for the strategy to work well - a task for which there is no obvious optimal approach.

Cited by 0Open access

DDR memory errors caused by Row Hammer

Barbara Aichinger · 2015

DDR3 memory is at the heart of almost all cloud computing servers today. A recently publicized failure mechanism in DDR3 memory, coined Row Hammer, has been shown to not only be a reliability issue but also a security risk. No industry standards group, government agency or trade association has signed up to address this issue. Data Centers and end users are on their own. This paper will discuss briefly the problem, mitigation strategies and a unique testing tool to determine what applications have the potential to create these types of failures.

Cited by 36

Estimating Human Movements Using Memory of Errors

Daqi Dong, Stan Franklin, Pulin Agrawal · Procedia Computer Science · 2015

Humans estimate their movements based on their knowledge of the dynamics of the environment, and on actual sensory data. Wolpert and colleagues have incorporated this understanding into a model that simulates this estimation using the Kalman filter [1]. Inspired by a recent study in neuroscience [2], we here introduce a new factor—memory of errors—into this simulation of movement estimation. These historical errors help humans determine the stability of the environment, which could be either steady or rapidly changing. This condition controls the rate at which a given error will be learned, so as to affect the estimates of future movements. We here apply our new model, a modified Kalman filter incorporating memory of errors, to the simulation of a hand lifting movement, and compare the simulated estimation process with its human counterpart.

Cited by 3Open access

Effect of Correlated Errors on Quantum Memory

Smita Bagewadi, Avhishek Chatterjee · arXiv (Cornell University) · 2024

Recent results on constant overhead LDPC code-based fault-tolerance against i.i.d. errors naturally lead to the question of fault-tolerance against errors with long-range correlations. Ideally, any correlation can be captured by a joint (system and bath) Hamiltonian. However, an arbitrary joint Hamiltonian is often intractable, and hence, the joint Hamiltonian model with pairwise terms was introduced and developed in a series of foundational works. However, the analysis of the new constant overhead codes in that error model appears to be quite challenging. In this paper, to model correlated errors in quantum memory, we introduce a correlation model which is a generalization of the well-known hidden random fields. This proposed model, which includes stationary and ergodic (non-Markov) error distributions, is shown to capture correlations not captured by the joint Hamiltonian model with pairwise terms. On the other hand, building on non-i.i.d. measure concentration, we show that for a broad class of non-Markov and (possibly) non-stationary error distributions, quantum Tanner codes ensure an exponential retention time (in the number of physical qubits), when the error rate is below a threshold. An implication of these results is that the rate of decay of the correlation with distance does not necessarily differentiate between good and bad correlation.

Cited by 0Open access

Intrusion errors in visuospatial working memory performance

Cesare Cornoldi, Nicola Mammarella · Memory · 2005

This study tested the hypothesis that failure in active visuospatial working memory tasks involves a difficulty in avoiding intrusions due to information that is already activated. Two experiments are described, in which participants were required to process several series of locations on a 4 x 4 matrix and then to produce only the final location of each series. Results revealed a higher number of errors due to already activated locations (intrusions) compared with errors due to new locations (inventions). Moreover, when participants were required to pay extra attention to some irrelevant (non-final) locations by tapping on the table, intrusion errors increased. Results are discussed in terms of current models of working memory functioning.

Cited by 16

Action errors impair active working memory maintenance.

Jan R. Wessel, Jiefeng Jiang, Jeff J Stolley · Journal of Experimental Psychology General · 2022

The ability to detect and correct action errors is paramount to safe and efficient behavior. Its underlying processes are subject of intense scientific debate. The recent adaptive orienting theory of error processing (AOT) proposes that errors trigger a cascade of processes that purportedly begins with a broad suppression of active motoric and-crucially-cognitive processes. While the motoric effects of errors are well established, an empirical test of their purported suppressive effects on active cognitive processes is still missing. Here, we provide data from seven experiments that clearly demonstrate such effects. Participants maintained information in working memory (WM) and performed different response conflict tasks during the delay period. Motor error commission during the delay period consistently reduced accuracy on the WM probe, demonstrating an error-related impairment of WM maintenance. We discuss the broad theoretical and practical implications of this finding, both for the AOT and beyond. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cited by 6Open access