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

A simple closure approximation for slow dynamics of a multiscale system: nonlinear and multiplicative coupling

Multiscale dynamics are ubiquitous in applications of modern science. Because of time scale separation between relatively small set of slowly evolving variables and (typically) much larger set of rapidly changing variables, direct numerical simulations of such systems often require relatively small time discretization step to resolve fast dynamics, which, in turn, increases computational expense. As a result, it became a popular approach in applications to develop a closed approximate model for slow variables alone, which both effectively reduces the dimension of the phase space of dynamics, as well as allows for a longer time discretization step. In this work we develop a new method for approximate reduced model, based on the linear fluctuation-dissipation theorem applied to statistical states of the fast variables. The method is suitable for situations with quadratically nonlinear and multiplicative coupling. We show that, with complex quadratically nonlinear and multiplicative coupling in both slow and fast variables, this method produces comparable statistics to what is exhibited by an original multiscale model. In contrast, it is observed that the results from the simplified closed model with a constant coupling term parameterization are consistently less precise

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

Strong interference effects in the resonant Auger decay of atoms induced by intense X-Ray fields

The theory of resonant Auger decay of atoms in a high intensity coherent X-ray pulse is presented. The theory includes the coupling between the ground state and the resonance due to an intense X-ray pulse, taking into account the decay of the resonance and the direct photoionization of the ground state, both populating the final ionic states coherently. The theory also considers the impact of the direct photoionization of the resonance state itself which typically populates highly-excited ionic states. The combined action of the resonant decay and of the direct ionization of the ground state in the field induces a non-hermitian time-dependent coupling between the ground and the 'dressed' resonance stats. The impact of these competing processes on the total electron yield and on the 2s$^2$2p$^{4}(^1\mathrm{D})$3p $^2$P spectator and 2s$^1$2p$^{6}$ $^2$S participator Auger decay spectra of the Ne 1s$\to$3p resonance is investigated. The role of the direct photoionization of the ground state and of the resonance increases dramatically with the field intensity. This results in strong interference effects with distinct patterns in the electron spectra, different for the participator and spectator final states.Comment: 31 pages, 6 figure

An investigation of volcanic depressions. Part 4: Origin of Hole-in-the-ground, a maar in Central Oregon

Hole-in-the-Ground, a volcanic explosion crater, located in central Oregon is described. The morphology of the soil and rocks and the topography of the crater indicate the sequential happenings during the eruption. Geophysical measurements also indicate a domical intrusion below the crater floor, extending upward as a ring dike around the margins of the crater. The volume of ejecta was determined for four major eruptions. Varied analyses were made of the pyroclastic debris, rocks, cinders, and soil