Complementary oxide memristor technology facilitating both inhibitory and excitatory synapses for potential neuromorphic computing applications

Doolittle, W. Alan; Calley, W. Laws; Henderson, Walter

doi:10.1109/isdrs.2009.5378162

Cited by 14 publications

(9 citation statements)

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“…As has been shown elsewhere, our memristors [17,18] and other memristive devices [19,20,21] present a 'transient' current spike in response to a d.c. voltage (which we have also referred to as the 'short-term memory of the memristor'). In this paper, we demonstrate that these spikes interact within small memristor networks and give rise to emergent brain-like dynamics.…”

Section: Introductionsupporting

confidence: 58%

Emergent spiking in non-ideal memristor networks

Gale

Costello

Adamatzky

2014

Microelectronics Journal

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Memristors have uses as artificial synapses and perform well in this role in simulations with artificial spiking neurons. Our experiments show that memristor networks natively spike and can exhibit emergent oscillations and bursting spikes. Networks of near-ideal memristors exhibit behaviour similar to a single memristor and combine in circuits like resistors do. Spiking is more likely when filamentary memristors are used or the circuits have a higher degree of compositional complexity (i.e. a larger number of anti-series or anti-parallel interactions). 3-memristor circuits with the same memristor polarity (low compositional complexity) are stabilised and do not show spiking behaviour. 3-memristor circuits with anti-series and/or anti-parallel compositions show richer and more complex dynamics than 2-memristor spiking circuits. We show that the complexity of these dynamics can be quantified by calculating (using partial auto-correlation functions) the minimum order auto-regression function that could fit it. We propose that these oscillations and spikes may be similar phenomena to brainwaves and neural spike trains and suggest that these behaviours can be used to perform neuromorphic computation. arXiv:1210.8024v2 [cond-mat.mtrl-sci]

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Section: Introductionsupporting

confidence: 58%

Emergent spiking in non-ideal memristor networks

Gale

Costello

Adamatzky

2014

Microelectronics Journal

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“…The fabrication of memristor at Hewlett-Packard labs [1], proved mathematical proposition for memristive behaviour given by Chua [2], and kickstarted an active area of research in modern material science and device research. The potential applications for memristive devices are ultimate high density, efficient and durable memory devices [3][4][5], neuromorphic application [6,7], and analogue computing [8].…”

Section: Introductionmentioning

confidence: 99%

Fabrication of graphene-nanoflake/poly(4-vinylphenol) polymer nanocomposite thin film by electrohydrodynamic atomization and its application as flexible resistive switching device

Choi¹,

Ali²,

Na³

2015

Physica B: Condensed Matter

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“…Memristive systems have recently enjoyed a resurgence of interest from the research community after being manufactured by HP labs [4]. This has spawned a number of research avenues in terms of applications of memristive systems [29] [30], and synthesis of various other memristors [31] [32].…”

Section: Introductionmentioning

confidence: 99%

Evolution of Plastic Learning in Spiking Networks via Memristive Connections

Howard

Gale

Bull

et al. 2012

IEEE Trans. Evol. Computat.

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This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of (i) linear resistors (ii) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks.

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Complementary oxide memristor technology facilitating both inhibitory and excitatory synapses for potential neuromorphic computing applications

Cited by 14 publications

References 0 publications

Emergent spiking in non-ideal memristor networks

Emergent spiking in non-ideal memristor networks

Fabrication of graphene-nanoflake/poly(4-vinylphenol) polymer nanocomposite thin film by electrohydrodynamic atomization and its application as flexible resistive switching device

Evolution of Plastic Learning in Spiking Networks via Memristive Connections

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