Spiking neuromorphic networks with metal-oxide memristors

Prezioso, M.; Zhong, Yi; Gavrilov, Dmitri; Merrikh-Bayat, Farshad; Hoskins, Brian D.; Adam, Gina C.; Likharev, K. K.; Strukov, Dmitri B.

doi:10.1109/iscas.2016.7527199

Cited by 23 publications

(9 citation statements)

References 20 publications

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“…The time constants are functions of the device conductance and converges to the same value at the boundary points such that the conductance change gradually becomes zero. Such state dependent behavior is akin to the saturating conductance responses observed in many of the gradual conductance and STDP demonstrations in the memristive devices 26,35,36,[40][41][42][43] . The STDP response from the phenomenological model is shown in Fig.3c, where the model conductance is initialized to 1 µS and is programmed with a sequence of random ∆ts.…”

Section: Phenomenological Model For State-dependent Conductance Updatementioning

confidence: 78%

Bio-mimetic synaptic plasticity and learning in a sub-500 mV Cu/SiO2/W memristor

Nandakumar

Rajendran

2020

Microelectronic Engineering

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The computational efficiency of the human brain is believed to stem from the parallel information processing capability of neurons with integrated storage in synaptic interconnections programmed by local spike triggered learning rules such as spike timing dependent plasticity (STDP). The extremely low operating voltages (approximately 100 mV) used to trigger neuronal signaling and synaptic adaptation is believed to be a critical reason for the brain's power efficiency. We demonstrate the feasibility of spike triggered STDP behavior in a two-terminal Cu/SiO 2 /W memristive device capable of operating below 500 mV. We analyze the state-dependent nature of conductance updates in the device to develop a phenomenological model. Using the model, we evaluate the potential of such devices to generate precise spike times under supervised learning conditions and classify handwritten digits from the MNIST dataset in an unsupervised learning setting. The results form a promising step towards creating a low power synaptic device capable of on-chip learning.

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Section: Phenomenological Model For State-dependent Conductance Updatementioning

confidence: 78%

Bio-mimetic synaptic plasticity and learning in a sub-500 mV Cu/SiO2/W memristor

Nandakumar

Rajendran

2020

Microelectronic Engineering

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“…Repeatable observations of the spiking behaviour in I-V of the fungi is very important because this opens new pathways for the implementation of neuromorphic computing with fungi. A fruitful theoretical foundation of this field is already well developed [30,18,28,27,24,19].…”

Section: Discussionmentioning

confidence: 99%

Mem-fractive Properties of Mushrooms

Beasley,

Abdelouahab,

Lozi

et al. 2020

Preprint

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Memristors close the loop for I-V characteristics of the traditional, passive, semi-conductor devices. Originally proposed in 1971,the hunt for the memristor has been going ever since. The key feature of a memristor is that its current resitance is a function of its previous resistance. As such, the behaviour of the device is influenced by changing the way in which potential is applied across it. Ultimately, information can be encoded on memristors. Biological substrates have already been shown to exhibit some memristive propertiesHowever, many memristive devices are yet to be found. Here we show that the fruit bodies of grey oyster fungi Pleurotus ostreatus exhibit memristive behaviours. This paper presents the I-V characteristics of the mushrooms. By examination of the conducted current for a given voltage applied as a function of the previous voltage, it is shown that the mushroom is a memristor. Our results demonstrate that nature continues to provide specimens that hold these unique and valuable electrical characteristics and which have the potential to advance the field of hybrid electronic systems.

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“…The cyclic voltammetry of the subject matter illustrates that periods of 'spiking' (oscillations) occur in their I-V characteristics. The spiking behaviour is important as it is a classic component of devices that exhibit memristive properties [12,26,27]. By way of example, MATLAB was used to detect the occurrence of the spikes in I-V traces from some of the samples that were tested and the results are shown below.…”

Section: Spikingmentioning

confidence: 99%

On memfractance of plants and fungi

Beasley¹,

Abdelouahab²,

Lozi³

et al. 2020

Preprint

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The key feature of a memristor is that the resistance is a function of its previous resistance, thereby the behaviour of the device is influenced by changing the way in which potential is applied across it. Ultimately, information can be encoded on memristors, which can then be used to implement a number of circuit topologies.Biological substrates have already been shown to exhibit some memristive properties.It is, therefore, logical that all biological media will follow this trend to some degree. In this paper we demonstrate that a range of yet untested specimens exhibit memristive properties, including mediums such as water and dampened wood shavings on which we can cultivate biological specimens. We propose that memristance is not a binary property {0, 1}, but rather a continuum on the scale [0,1]. The results imply that there is great potential for hybrid electronic systems that combine traditional electronic typologies with naturally occurring specimens.

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Spiking neuromorphic networks with metal-oxide memristors

Cited by 23 publications

References 20 publications

Bio-mimetic synaptic plasticity and learning in a sub-500 mV Cu/SiO2/W memristor

Bio-mimetic synaptic plasticity and learning in a sub-500 mV Cu/SiO2/W memristor

Mem-fractive Properties of Mushrooms

On memfractance of plants and fungi

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