1,721,005 research outputs found

    Computing with Memristor-based Nonlinear Oscillators

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    Among the recent disruptive technologies, volatile/nonvolatile memory-resistor (memristor) has attracted the researchers' attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule for neuromorphic computing systems. The overall goal of this work is to provide an unconventional computing platform exploiting memristor-based nonlinear oscillators described by means of phase deviation equations. Experimental results show that the approach significantly outperforms conventional architectures used for pattern recognition tasks

    Pattern Characterization in Second Order Memristor Networks

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    Second order memristors are two terminal devices which present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems, by exploiting temporal learning techniques. On the other hand spiking networks have been intensively studied in the context of unsupervised, supervised and reinforcement learning. In this manuscript we briefly present a simplified model of second order memristors, that we have derived in a previous paper. Then we focus on two main results. As a first step we show that the model is capable of accurately reproducing a correct synaptic response to complex inputs, like cycles of spike triplets and quadruplets at different frequencies. Then we show that, exploiting such a simple model, complex spatio-temporal patterns can be almost analytically characterized, in memristor networks connecting an arbitrary number of presynaptic and postsynaptic neurons

    A continuous-time learning rule for memristor-based recurrent neural networks

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    Among the recent disruptive technologies, volatile/nonvolatile memory-resistor (memristor) has attracted the researchers' attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule for competitive Hebbian learning. The overall goal of this work is to provide a novel analogue computing platform based on memristor devices and recurrent neural networks that exploit the memristor device physics to implement the backpropagation algorithm. Back propagation for recurrent neural networks requires a side network for the propagation of error derivatives. The use of memristor-based synaptic weights permit to propagate the error signals in the network via the nonlinear dynamics without the need of a digital side network. Experimental results show that the approach significantly outperforms conventional architectures used for pattern reconstruction. Further results will be reported in an extended work

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Variations on the Author

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    “Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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