Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque.

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called 'coinflips', via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based onp-value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a 'bucket' to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks.

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO 2 based memristive device. 1 st and 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼ 4.3 × and ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab's neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

Encoding integers and rationals on neuromorphic computers using virtual neuron.

Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network-based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the µ-recursive functions, which are the building blocks of general-purpose computation.

Heterosynaptic plasticity in biomembrane memristors controlled by pH.

Abstract: In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97-7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds (V*) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3-5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality. Impact statement: We describe how to use pH as a modulatory "interneuron" that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks. Supplementary information: The online version contains supplementary material available at 10.1557/s43577-022-00344-z.

Probabilistic Neural Computing with Stochastic Devices.

The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.

An Updated Model Does Not Reveal Sex Differences in Patellofemoral Joint Stress during Running.

Background: Structure-specific loading may have implications in understanding the mechanisms of running related injury. As females demonstrate a prevalence of patellofemoral pain twice that of males, this may indicate differences in patellofemoral loads between males and females. Previous works investigating differences in patellofemoral joint stress have shown conflicting results, but the models employed have not used estimates of muscle forces or sex specific contact areas. Hypothesis/Purpose: The aim of this study was to examine sex differences in patellofemoral joint stress using an updated model to include estimates of quadriceps muscle force and sex-specific patellofemoral contact area. Study Design: Descriptive Laboratory Study. Methods: Forty-five healthy recreational runners ran at a controlled speed down a 20-meter runway. Kinetic and kinematic data were utilized to estimate muscle forces using static optimization. Quadriceps muscle force was utilized with sex-specific patellofemoral joint contact area in a two-dimensional patellofemoral joint model to estimate patellofemoral joint stress. Multivariate tests were utilized to detect sex differences in patellofemoral loading and hip and knee kinematics. Results: No differences were found between sexes in measures of patellofemoral loading or quadriceps force. Females displayed a reduced knee extension moment and greater hip adduction and internal rotation than males. Conclusion: The inclusion of static optimization to estimate quadriceps muscle force and sex-specific contact area of the patellofemoral joint did not reveal sex differences in patellofemoral joint stress, but differences in non-sagittal plane hip motion were detected. Therefore, two-dimensional patellofemoral models may not fully characterize differences in patellofemoral joint stress between males and females. Three-dimensional patellofemoral models may be necessary to determine if sex differences in patellofemoral joint stress exist. Level of Evidence: 3b.

Opportunities for neuromorphic computing algorithms and applications.

Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design.

In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparameters. A key challenge is to find the optimum set of hyperparameters that might belong to the input/output encoding modules, the neural network itself, the application, or the underlying hardware. In this work, we present a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework (both software and hardware) that not only maximizes the performance of the network, but also minimizes the energy and area requirements of the corresponding neuromorphic hardware. We validate performance of our approach (in terms of accuracy and computation speed) on several control and classification applications on digital and mixed-signal (memristor-based) neural accelerators. We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52-71% for Pole-Balance), while having minimum effect on another (i.e., 50-53% for RoboNav). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes.

The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages. To overcome these limitations, we have developed and characterized a biomolecular memristor that mimics the composition, structure, and switching characteristics of biological synapses. Here, we describe the process of assembling and characterizing biomolecular memristors consisting of a 5 nm-thick lipid bilayer formed between lipid-functionalized water droplets in oil and doped with voltage-activated alamethicin peptides. While similar assembly protocols have been used to investigate biophysical properties of droplet-supported lipid membranes and membrane-bound ion channels, this article focuses on key modifications of the droplet interface bilayer method essential for achieving consistent memristor performance. Specifically, we describe the liposome preparation process and the incorporation of alamethicin peptides in lipid bilayer membranes, and the appropriate concentrations of each constituent as well as their impact on the overall response of the memristors. We also detail the characterization process of biomolecular memristors, including measurement and analysis of memristive current-voltage relationships obtained via cyclic voltammetry, as well as short-term plasticity and learning in response to step-wise voltage pulse trains.

Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current-voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.

Adiabatic Quantum Computation Applied to Deep Learning Networks.

Training deep learning networks is a difficult task due to computational complexity, and this is traditionally handled by simplifying network topology to enable parallel computation on graphical processing units (GPUs). However, the emergence of quantum devices allows reconsideration of complex topologies. We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum computation known as quantum annealing performed by a D-Wave processor. We provide a brief description of the hardware and how it solves Ising models, how we translate our data into the corresponding Ising models, and how we use available expanded topology options to explore potential performance improvements. Although we focus on the application of quantum annealing in this article, the work discussed here is just one of three approaches we explored as part of a larger project that considers alternative means for training deep learning networks. The other approaches involve using a high performance computing (HPC) environment to automatically find network topologies with good performance and using neuromorphic computing to find a low-power solution for training deep learning networks. Our results show that our quantum approach can find good network parameters in a reasonable time despite increased network topology complexity; that HPC can find good parameters for traditional, simplified network topologies; and that neuromorphic computers can use low power memristive hardware to represent complex topologies and parameters derived from other architecture choices.

Competencies for psychology practice in primary care.

This article reports on the outcome of a presidential initiative of 2012 American Psychological Association President Suzanne Bennett Johnson to delineate competencies for primary care (PC) psychology in six broad domains: science, systems, professionalism, relationships, application, and education. Essential knowledge, skills, and attitudes are described for each PC psychology competency. Two behavioral examples are provided to illustrate each competency. Clinical vignettes demonstrate the competencies in action. Delineation of these competencies is intended to inform education, practice, and research in PC psychology and efforts to further develop team-based competencies in PC.

Activation, self-management, engagement, and retention in behavioral health care: a randomized clinical trial of the DECIDE intervention.

IMPORTANCE: Given minority patients' unequal access to quality care, patient activation and self-management strategies have been suggested as a promising approach to improving mental health care. OBJECTIVE: To determine whether the DECIDE (Decide the problem; Explore the questions; Closed or open-ended questions; Identify the who, why, or how of the problem; Direct questions to your health care professional; Enjoy a shared solution) intervention, an educational strategy that teaches patients to ask questions and make collaborative decisions with their health care professional, improves patient activation and self-management, as well as engagement and retention in behavioral health care. DESIGN, SETTING, AND PATIENTS: In this multisite randomized clinical trial performed from February 1, 2009, through October 9, 2011 (date of last follow-up interview), we recruited 647 English- or Spanish-speaking patients 18 to 70 years old from 13 outpatient community mental health clinics across 5 states and 1 US territory. A total of 722 patients were included in analyses of secondary outcomes. INTERVENTIONS: Three DECIDE training sessions delivered by a care manager vs giving patients a brochure on management of behavioral health. MAIN OUTCOMES AND MEASURES: Primary outcomes were patient assessment of activation (Patient Activation Scale) and self-management (Perceived Efficacy in Patient-Physician Interactions). Secondary outcomes included patient engagement (proportion of visits attended of those scheduled) and retention (attending at least 4 visits in the 6 months after the baseline research assessment), collected through medical record review or electronic records. RESULTS: Patients assigned to DECIDE reported significant increases in activation (mean ß = 1.74, SD = 0.58; P = .003) and self-management (mean ß = 2.42, SD = 0.90; P = .008) relative to control patients, but there was no evidence of an effect on engagement or retention in care. CONCLUSIONS AND RELEVANCE: The DECIDE intervention appears to help patients learn to effectively ask questions and participate in decisions about their behavioral health care, but a health care professional component might be needed to augment engagement in care. DECIDE appears to have promise as a strategy for changing the role of minority patients in behavioral health care. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01226329

Uptake of depression treatment recommendations among Latino primary care patients.

BACKGROUND: Primary care providers (PCP) are the entry point for public sector depression treatment for many Latino patients. However, many Latino patients do not initiate their PCPs' recommended treatment, which likely contributes to ethnic disparities in depression treatment. This study examined factors related to Latino patients' uptake of their PCPs' recommendations for depression treatment. METHOD: Ninety Latino primary care patients who received a depression treatment recommendation from their PCP participated in a telephone interview. Patients rated their working alliance with their PCP and their PCP's cultural competence. They also reported their treatment preference, the type of recommendation, and their intended and actual uptake of the recommendation. Patients were contacted at two time points (Time 1: M = 14 days after PCP appointment; Time 2: M = 84 days after PCP appointment) to report their uptake status. RESULTS: At Time 1, 23% of patients had initiated uptake of the treatment recommendation, increasing to 53% at Time 2. Patients who received a medication recommendation were more likely to have followed though on the recommendation, compared with patients who received a psychotherapy recommendation. The working alliance was positively associated with intention to follow up on a treatment recommendation, and also mediated the relationship between cultural competence and intention of following up on the recommendation. CONCLUSION: PCP's treatment recommendation and the PCP-patient alliance play a role in Latino primary care patients intention to follow a treatment recommendation for depression. An improved understanding of this role could enhance efforts to improve depression treatment uptake.

Dynamic artificial neural networks with affective systems.

Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

Integrating sleep management into clinical practice.

Although the basic function of sleep remains a mystery, insufficient sleep is associated with mood disturbance, fatigue and daytime lethargy, cognitive impairments, daytime behavior problems, academic problems, use of stimulants, work absenteeism, lost work production and an increase in healthcare utilization. The International Classification of Sleep Disorders distinguishes 90 different disorders, many of which can be effectively treated, but when left untreated can be costly in terms of quality of life, health and healthcare cost. Over the past 50 years we have become more effective in measuring sleep and have honed our treatments to better address the sleep disorders that most impact us. This article will focus on the three sleep disorders for which patients most frequently seek care, including insomnia, obstructive sleep apnea syndrome and restless leg syndrome.

Preparing psychologists in academic health centers for the rapidly changing health care environment.

The Association of Psychologists in Academic Health Centers (APAHC) convened its 5th National APAHC Conference in Boston March 3-5 2011. The conference and its theme, "Preparing Psychologists for a Rapidly Changing Health Care Environment," brought psychologists from academic health centers together to examine how psychology can adapt to and help lead health care efforts in the face of health care reform. This paper reports on the conference and introduces the special issue of JCPMS that is dedicated to the conference. The conference theme is framed in the historical context of the four national conferences that preceded it. In examining the focus and topics of the preceding conferences, recurrent themes are identified and progress in certain areas is highlighted.