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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Effects of non-invasive brain stimulation on associative memory

Associative memory refers to remembering the association between two items, such as a face and a name. It is a crucial part of daily life, but it is also one of the first aspects of memory performance that is impacted by aging and by Alzheimer’s disease. Evidence suggests that transcranial direct current stimulation (tDCS) can improve memory performance, but few tDCS studies have investigated its impact on associative memory. In addition, no prior study of the effects of tDCS on memory performance has systematically evaluated the impact of tDCS on different types of memory assessments, such as recognition and recall tests. In this study, we measured the effects of tDCS on associative memory performance in healthy adults, using both recognition and recall tests. Participants studied face-name pairs while receiving either active (30 minutes, 2 mA) or sham (30 minutes, 0.1 mA) stimulation with the anode placed at F9 and the cathode placed on the contralateral upper arm. Participants in the active stimulation group performed significantly better on the recall test than participants in the sham group, recalling 50% more names, on average, and making fewer recall errors. However, the two groups did not differ significantly in terms of their performance on the recognition memory test. This investigation provides evidence that stimulation at the time of study improves associative memory encoding, but that this memory benefit is evident only under certain retrieval conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking, in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Active oscillatory associative memory

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions.

Chemistry↗
DOE OSTI2018

An FPGA-based Pattern Recognition Associative Memory

Pattern recognition associative memory (PRAM) devices are parallel processing engines which are used to tackle the complex combinatorics of track nding algorithms, particularly for silicon based tracking triggers. PRAM development has been mostly limited to the realm of ASICs, which often leads to lengthy and expensive design cycles. FPGAs allow for quick iterations, making them an ideal hardware platform for designing and evaluating new PRAM features before committing to silicon. In this paper we present our FPGA-based PRAM designs and discuss how logic blocks which were originally developed for ASICs have been redesigned to take advantage of modern FPGA architectures.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Differential Age Effects of Transcranial Direct Current Stimulation on Associative Memory

Abstract Objectives Older adults experience associative memory deficits relative to younger adults (Old & Naveh-Benjamin, 2008). The aim of this study was to test the effect of transcranial direct current stimulation (tDCS) on face-name associative memory in older and younger adults. Method Experimenters applied active (1.5 mA) or sham (0.1 mA) stimulation with the anode placed over the left dorsolateral prefrontal cortex (dlPFC) during a face-name encoding task, and measured both cued recall and recognition performance. Participants completed memory tests immediately after stimulation and after a 24-h delay to examine both immediate and delayed stimulation effects on memory. Results Results showed improved face-name associative memory performance for both recall and recognition measures, but only for younger adults, whereas there was no difference between active and sham stimulation for older adults. For younger adults, stimulation-induced memory improvements persisted after a 24-h delay, suggesting delayed effects of tDCS after a consolidation period. Discussion Although effective in younger adults, these results suggest that older adults may be resistant to this intervention, at least under the stimulation parameters used in the current study. This finding is inconsistent with a commonly seen trend, where tDCS effects on cognition are larger in older than younger adults.

Leach, Ryan C.↗
DOE OSTI2014

Adiabatic quantum optimization for associative memory recall

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Particle track classification using quantum associative memory

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Reachable set bounding for a class of bidirectional associative memory NNSs with Markov jump switching parameters

This paper studies the problem how to estimate the reachable set for a class of delayed bidirectional associative memory neural network systems (NNSs), which have Markov switching parameters and unit-energy or unit-peak bounded disturbance inputs. The feature of the Markov jump bidirectional associative memory NNSs shows in the following twofold: the time delay is time varying; the transition rates is time varying. Moreover, the time-varying transition rates is piecewise constant. Using the Lyapunov functional method, delay-partitioning and linear matrix inequalities techniques, the estimate problem of the reachable set depending on time delay is solved. The effectiveness of the given results is illustrated by the proposed numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Superconducting Hyperdimensional Associative Memory Circuit for Scalable Machine Learning

Here we propose a generalized architecture for the first rapid-single-flux-quantum (RSFQ) associative memory circuit. The circuit employs hyperdimensional computing (HDC), a machine learning (ML) paradigm utilizing vectors with dimensionality in the thousands to represent information. HDC designs have small memory footprints, simple computations, and simple training algorithms compared to superconducting neural network accelerators (SNNAs), making them a better option for scalable SFQ machine learning (ML) solutions. The proposed superconducting HDC (SHDC) circuit uses entirely on-chip RSFQ memory which is tightly integrated with logic, operates at 33.3 GHz, is applicable to general ML tasks, and is manufacturable at practically useful scales given current SFQ fabrication limits. Tailored to a language recognition task, SHDC consists of ~ 2-20 M Josephson junctions (JJs) and consumes up to three times less power than an analogous CMOS HDC circuit while achieving 78-84% higher throughput. SHDC is capable of outperforming the state of the art RSFQ SNNA, SuperNPU, by 48-99% for all benchmark NN architectures tested while occupying up to 90% less area and consuming up to nine times less power. To the best of the authors' knowledge, SHDC is currently the only superconducting ML approach feasible at practically useful scales for real-world ML tasks and capable of online learning.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Transcranial stimulation over the left inferior frontal gyrus increases false alarms in an associative memory task in older adults

Here, transcranial direct current stimulation (tDCS) is a potent ial tool for alleviating various forms of cognitive decline, including memory loss, in older adults. However, past effects of tDCS on cognitive ability have been mixed. One important potential moderator of tDCS effects is the baseline level of cognitive performance. We tested the effects of tDCS on face-name associative memory in older adults, who suffer from performance deficits in this task relative to younger adults. Stimulation was applied to the left inferior prefrontal cortex during encoding of face-name pairs, and memory was assessed with both a recognition and recall task. As a result, face–name memory performance was decreased with the use of tDCS. This result was driven by increased false alarms when recognizing rearranged face–name pairs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Quantum Associative Memory in Hep Track Pattern Recognition

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease: Brain protein O-GlcNAcylation in Alzheimer's disease

Protein modification by O-linked beta-N-acetylglucosamine (O-GlcNAc) is emerging as an important factor in the pathogenesis of sporadic Alzheimer’s disease. Herein we report the most comprehensive, quantitative proteomics analysis for protein O-GlcNAcylation in post-mortem human brains with and without Alzheimer’s using isobaric tandem mass tags labeling, chemoenzymatic photocleavage enrichment and liquid chromatography coupled to mass spectrometry. A total of 1,850 O-GlcNAc peptides covering 1,094 O-GlcNAcylation sites were identified from 530 proteins in the human brain. 128 O-GlcNAc peptides covering 78 proteins were altered significantly in Alzheimer’s brain as compared to controls (q<0.05). Moreover, alteration of the O-GlcNAc peptide abundance could be attributed more to O-GlcNAcylation level than to protein level changes. The altered O-GlcNAcylated proteins belong to several structural and functional categories, including synaptic proteins, cytoskeleton proteins, and memory-associated proteins. These findings suggest that dysregulation of O-GlcNAcylation of multiple brain proteins may be involved in the development of sporadic Alzheimer’s disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Particle Track Classification Using Quantum Associative Memory (Final Technical Report)

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recall was determined as a function of detector resolution, noise, and efficiency and pattern density, where pattern density is defined as the ratio of the number of reference signal patterns encoded in the library to each pattern’s length. We found that QAMM achieved high classification accuracy when applied to datasets with low pattern density. QCAM achieved high classification accuracy for datasets with high pattern density and was found to be more robust to detector noise. The project methodology and results are described in detail in our arXiv preprint (arXiv:2011.11848) . This project was conducted by scientists at the Johns Hopkins University Applied Physics Laboratory and Oak Ridge National Laboratory from August 2018 to August 2020 and was supported by DOE grant DE-SC0019497.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

Abstract Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16 × 12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5% root mean square deviations from the ideal image require approximately 5 μ s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than 10 −3 , depending on the strength of the feedback.

Prasad, Nitin (ORCID:0000000180880164)↗