A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network's throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two classification tasks.Comment: 18 Pages main text, 9 pages of supplementary text, 19 figures. Patente

On the Applicability of Low-Dimensional Models for Convective Flow Reversals at Extreme Prandtl Numbers

Constructing simpler models, either stochastic or deterministic, for exploring the phenomenon of flow reversals in fluid systems is in vogue across disciplines. Using direct numerical simulations and nonlinear time series analysis, we illustrate that the basic nature of flow reversals in convecting fluids can depend on the dimensionless parameters describing the system. Specifically, we find evidence of low-dimensional determinism in flow reversals occurring at zero Prandtl number, whereas we fail to find such signatures for reversals at infinite Prandtl number. Thus, even in a single system, as one varies the system parameters, one can encounter reversals that are fundamentally different in nature. Consequently, we conclude that a single general low-dimensional deterministic model cannot faithfully characterize flow reversals for every set of parameter values.Comment: 9 pages, 4 figure

Beyond sustainable buildings: eco-efficiency to eco-effectiveness through cradle-to-cradle design

Sustainable building development focuses on achieving buildings that meet performance and functionality requirements with minimum adverse impact on the environment. Such eco-efficiency strategies are however not feasible for achieving long-term economic and environmental objectives as they only result in damage reduction without addressing design flaws of contemporary industry. The cradle-to-cradle (C2C) design philosophy which has been described as a paradigm changing innovative platform for achieving ecologically intelligent and environmentally restorative buildings appears to offer an alternative vision which, if embraced, could lead to eco-effectiveness and the achievement of long-term environmental objectives. Adoption of C2C principles in the built environment has however been hindered by several factors especially in a sector where change has always been a very slow process. From a review of extant literature, it is argued that the promotion of current sustainable and/or gree n building strategies - which in themselves are not coherent enough due to their pluralistic meanings and sometimes differing solutions - are a major barrier to the promotion of C2C principles in the built environment. To overcome this barrier to C2C implementation, it is recommended that research should focus on developing clearly defined and measurable C2C targets that can be incorporated into project briefs from the inception of development projects. These targets could enable control, monitoring and comparison of C2C design outcomes with eco-efficient measures as well as serve as a guide for project stakeholders to achieve eco-effective “nutrient” management from the project conceptualization phase to the end of life of the building

Design and Implementation Strategies for Peer-Led Team Learning in Organic Chemistry

Peer-Led Team Learning (PLTL), which initially began as an effort to improve undergraduate chemistry education, has shown the potential to improve learning among college students in all disciplines. The model applies the desired qualities of teamwork to learning. This project is aimed at designing and setting up a PLTL Workshop at Southern Adventist University (SAU). Nine students currently taking organic chemistry at SAU volunteered to participate in the project. In order to determine the effectiveness of the project, test scores were collected and analyzed for any apparent trends, and an end-of-semester survey was conducted. The results indicate that the program was a success

A Case Report and Overview of Familial Cerebral Cavernous Malformation Pathogenesis in an Adult Patient

OBJECTIVE We present a case of a 39 year-old woman who presented with a solitary cavernous malformation hemorrhage without any other lesions, and subsequently presented several months later with a new hemorrhage from a de novo lesion. We discuss mechanisms of paradominant inheritance and haploinsufficiency to describe phenotype expression of familial cavernous malformations. CASE DESCRIPTION The patient presented with unremitting headaches, who had a known history of a solitary cerebral cavernous malformation (CCM) for which she underwent resection several months prior with no evidence of any other CCM lesions seen on post-operative MRI. She has no history of whole brain radiation, family history of cavernous malformations, or prior head trauma. During this hospital visit, she was found to have develop two new lesions in the left fronto-parietal lobe and cerebellum. She was treated with surgical resection of the left frontoparietal lesion, and recovered fully. It is of interest that a patient approaching her fourth decade of life would start to develop formation of multiple de novo cavernous malformations, especially with an absent family history. Paradominant Inheritance and haploinsufficiency are two proposed models of inheritance that can be related to this patient’s disease progression. CONCLUSION The case illustrates an atypical clinical course of a patient with familia

A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation

Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state-dependence, non-linear conductance changes, and intrinsic variability in both their switching threshold and conductance values, that make them ideal devices for emulating the bio-physics of real synapses. In this paper we present a spiking neural network architecture that supports the use of memristive devices as synaptic elements, and propose mixed-signal analog-digital interfacing circuits which mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold, for implementing stochastic learning. The effect of device variability is mitigated by using pairs of memristive devices configured in a complementary push-pull mechanism and interfaced to a current-mode normalizer circuit. The stochastic learning mechanism is obtained by mapping the desired change in synaptic weight into a corresponding switching probability that is derived from the intrinsic stochastic behavior of memristive devices. We demonstrate the features of the CMOS circuits and apply the architecture proposed to a standard neural network hand-written digit classification benchmark based on the MNIST data-set. We evaluate the performance of the approach proposed on this benchmark using behavioral-level spiking neural network simulation, showing both the effect of the reduction in conductance variability produced by the current-mode normalizer circuit, and the increase in performance as a function of the number of memristive devices used in each synapse.Comment: 13 pages, 12 figures, accepted for Faraday Discussion