Literature DB >> 25879966

Memristive Hebbian plasticity model: device requirements for the emulation of Hebbian plasticity based on memristive devices.

Martin Ziegler, Christoph Riggert, Mirko Hansen, Thorsten Bartsch, Hermann Kohlstedt.   

Abstract

In this work we present a phenomenological model for synaptic plasticity suitable to describe common plasticity measurements of memristive devices. We show evidence that the presented model is basically compatible with advanced biophysical plasticity models, which account for a large body of experimental data on spike-timing-depending plasticity (STDP) as an asymmetric form of Hebbian learning. The basic characteristics of our model are a saturation of the synaptic weight growth and a weight dependent learning rate. Moreover, it accounts for common resistive switching behaviors of memristive devices under voltage pulse application and allows to study essential requirements of individual memristive devices for the emulation of Hebbian plasticity in neuromorphic circuits. In this respect, memristive devices based on mixed ionic/electronic and one exclusively electronic mechanism are explored. The ionic/electronic devices consist of the layer sequence metal/isolator/metal and represent today's most popular devices. The electronic device is a MemFlash-cell which is based on a conventional floating gate transistor in a diode configuration wiring scheme exhibiting a memristive (pinched) I-V characteristic.

Mesh:

Year:  2015        PMID: 25879966     DOI: 10.1109/TBCAS.2015.2410811

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  10 in total

1.  Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

Authors:  Mirko Hansen; Finn Zahari; Martin Ziegler; Hermann Kohlstedt
Journal:  Front Neurosci       Date:  2017-02-28       Impact factor: 4.677

2.  A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

Authors:  Nick Diederich; Thorsten Bartsch; Hermann Kohlstedt; Martin Ziegler
Journal:  Sci Rep       Date:  2018-06-19       Impact factor: 4.379

3.  Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron.

Authors:  Dong Won Kim; Woo Seok Yi; Jin Young Choi; Kei Ashiba; Jong Ung Baek; Han Sol Jun; Jae Joon Kim; Jea Gun Park
Journal:  Front Neurosci       Date:  2020-04-30       Impact factor: 4.677

Review 4.  Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing.

Authors:  Ki Chang Kwon; Ji Hyun Baek; Kootak Hong; Soo Young Kim; Ho Won Jang
Journal:  Nanomicro Lett       Date:  2022-02-05

5.  A memristive spiking neuron with firing rate coding.

Authors:  Marina Ignatov; Martin Ziegler; Mirko Hansen; Adrian Petraru; Hermann Kohlstedt
Journal:  Front Neurosci       Date:  2015-10-20       Impact factor: 4.677

6.  Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion.

Authors:  Marina Ignatov; Martin Ziegler; Mirko Hansen; Hermann Kohlstedt
Journal:  Sci Adv       Date:  2017-10-25       Impact factor: 14.136

7.  Unsupervised Hebbian learning experimentally realized with analogue memristive crossbar arrays.

Authors:  Mirko Hansen; Finn Zahari; Hermann Kohlstedt; Martin Ziegler
Journal:  Sci Rep       Date:  2018-06-11       Impact factor: 4.379

8.  Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing.

Authors:  Jacopo Frascaroli; Stefano Brivio; Erika Covi; Sabina Spiga
Journal:  Sci Rep       Date:  2018-05-08       Impact factor: 4.379

9.  Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices.

Authors:  Finn Zahari; Eduardo Pérez; Mamathamba Kalishettyhalli Mahadevaiah; Hermann Kohlstedt; Christian Wenger; Martin Ziegler
Journal:  Sci Rep       Date:  2020-09-02       Impact factor: 4.379

10.  Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.

Authors:  Ruiyi Li; Peng Huang; Yulin Feng; Zheng Zhou; Yizhou Zhang; Xiangxiang Ding; Lifeng Liu; Jinfeng Kang
Journal:  Micromachines (Basel)       Date:  2022-03-11       Impact factor: 2.891

  10 in total

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