Plasticity in memristive devices for spiking neural networks

Saïghi, Sylvain; Mayr, Christian; Serrano-Gotarredona, Teresa; Schmidt, Heidemarie; Lecerf, G.; Tomas, Jean; Grollier, Julie; Boyn, Sören; Vincent, Adrien F.; Querlioz, Damien; Barbera, Selina La; Alibart, Fabien; Vuillaume, D.; Bichler, Olivier; Gamrat, Christian; Linares-Barranco, B.

doi:10.3389/fnins.2015.00051

Cited by 212 publications

(156 citation statements)

References 98 publications

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“…It has to be mentioned that a stair-case like algorithm, like the one used here, is not practical to implement in real large-scale system, because requires neurons to keep track of previous activity. On the other hand, the testing procedure reported in Figure 2A is useful for characterizing the device and to clarify the functioning principle of the STDP implementation described below, which has actually been proposed as a learning rule for practical implementation of neuromorphic hardware (Saïghi et al, 2015). …”

Section: Resultsmentioning

confidence: 99%

“…Indeed, dedicated read-out and variable voltage biasing circuits are required. On the other hand, the voltage on the memristor in a system could be modulated through superimposition of long spikes, as proposed several times in literature (Serrano-Gotarredona et al, 2013; Saïghi et al, 2015). This method allows the neuron to always fire the same spike and let the delay times between spike determine the actual voltage on the device.…”

Section: Discussionmentioning

confidence: 99%

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Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

et al. 2016

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Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

et al. 2016

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“…It is to note that even not every initial state of the network led to the convergence. Possible solutions of the issue could be an implementation of different algorithms based on spike timing dependent plasticity (STDP) rules 27 or realization the circuit where conductivity of each element would be measured with a contactless spectrophotometric method. 15 Each step of our learning procedure consisted consecutively of an application of the whole training set of vectors x (k ) (k = 1, 2, 3, 4), actual weight measuring (applying the "reading" pulses) …”

Section: Nonseparable Task Solvingmentioning

confidence: 99%

First steps towards the realization of a double layer perceptron based on organic memristive devices

et al. 2016

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Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

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“…Several STDP learning models are discussed in [80,139] including STDP, double-spike STDP, quadratic STDP which are suitable to the usage of memristors, possibly arranged in arrays, Fig. 13 (c).…”

Section: Device Level the Memristor And Crossbar Realizationsmentioning

confidence: 99%

Neuromorphic microelectronics from devices to hardware systems and applications

Schmid

2016

NOLTA

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Neuromorphic systems aiming at mimicking some characteristics of the nervous systems of living humans or animals have been developed since the late 1980s', taking benefit of intrinsic properties and increasing performances of the successive silicon fabrication technologies. A regain of interest has been observed in the middle of the 2010s', which manifests itself from the emergence of large-scale projects integrating various computational and hardware perspectives, by the increased interest and involvement of industry and the growth of the volume of scientific publications. This paper reviews research directions and methods of neuromorphic microelectronics hardware, the developed hardware and its performance, and discusses current issues and potential future developments.

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Plasticity in memristive devices for spiking neural networks

Cited by 212 publications

References 98 publications

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

First steps towards the realization of a double layer perceptron based on organic memristive devices

Neuromorphic microelectronics from devices to hardware systems and applications

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