Implementation of adaptive neuron based on memristor and memcapacitor emulators

Feali, Mohammad Saeed; Ahmadi, Majid; Hayati, Mohsen

doi:10.1016/j.neucom.2018.05.006

Cited by 48 publications

(23 citation statements)

References 51 publications

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“…However, where the density of neurons is not as high and the stimulus tends to be slowly varying, a frequency code is a more robust alternative [865] , [866] , [867] , [652] . Feali et al [868] also emulated neuristor adaptive behavior (spike-frequency adaptation) in SPICE environment. Parihar et al [869] experimentally demonstrated spontaneous stochastic spiking along with electrically controllable firing probabilities using VO 2 -based metal-insulation-transitions neurons owing two noise sources -thermal noise and threshold fluctuations, which act as precursors of bifurcation.…”

Section: Neural Codesmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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New device concepts and new computing principles are needed to balance our ever-growing appetite for data and information with the realization of the goals of increased energy efficiency, reduction in CO 2 emissions and the circular economy. Neuromorphic or synaptic electronics is an emerging field of research aiming to overcome the current computer's Von-Neumann bottleneck by building artificial neuronal systems to mimic the extremely energy efficient biological synapses. The introduction of photovoltaic and/or photonic aspects into these neuromorphic architectures will produce self-powered adaptive electronics but may also open up new possibilities in artificial neuroscience, artificial neural communications, sensing and machine learning which would enable, in turn, a new era for computational systems owing to the possibility of attaining high bandwidths with much reduced power consumption. This perspective is focused on recent progress in the implementation of functional oxide thinfilms into photovoltaic and neuromorphic applications towards the envisioned goal of selfpowered photovoltaic neuromorphic systems or a solar brain.

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Section: Neural Codesmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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“…Electron. It is noteworthy that some LIF neurons with tunable integration time have been realized using memcapacitors [223,224] or pseudo-memcapacitors (a diffusive memristive device with a series capacitor) [225] to replace normal capacitors. 2019, 5,1900287 its threshold, and soon, the capacitor performs discharging through the memristive device (DL loop in Figure 18b) and induces the plunge of the voltage across the memristive device resulting in it recovering to high resistance.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

“…Thus, the reported artificial LIF neurons mostly belong to this type of LIF neurons so far. It is noteworthy that some LIF neurons with tunable integration time have been realized using memcapacitors or pseudo‐memcapacitors (a diffusive memristive device with a series capacitor) to replace normal capacitors.…”

Section: Memristive Neuronsmentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

174

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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“…In 2018, Babacan et.al, proposed OTA based memcapacitor and meminductor [23]. [24][25] includes applications based on memcapacitor. Yu et.al, recommended a memcapacitor based relaxation oscillator in 2016 [24].…”

Section: Introductionmentioning

confidence: 99%

“…Yu et.al, recommended a memcapacitor based relaxation oscillator in 2016 [24]. In 2018, Feali et.al, implemented adaptive neuron based on memristor and memcapacitor [25]. Vista and Ranjan proposed MOSFET based floating memristor [26].…”

Section: Introductionmentioning

confidence: 99%

Charged Controlled Mem-Element Emulator and Its Application in a Chaotic System

et al. 2020

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This paper proposes a charged controlled emulator model for memristor and memcapacitor using second-generation Current Conveyor (CCIIs) and Analog Multiplier (AM). The grounded and floating memelement circuits have been designed using two CCIIs and one multiplier in addition to some passive components. The grounded type of design requires three resistors and three capacitors while floating design necessitates only two resistors and two capacitors. With the help of a switch, the proposed design can be easily switched into a memristor or memcapacitor. The proposed emulator model has been theoretically analyzed and simulated in PSpice to substantiate the effectiveness and accuracy. The practicability of the circuit has been established using commercially available ICs AD844AN and AD633JN. Non-linear characteristics of the proposed memristor emulator have been used to design a chaotic system.

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Implementation of adaptive neuron based on memristor and memcapacitor emulators

Cited by 48 publications

References 51 publications

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Memristive Synapses and Neurons for Bioinspired Computing

Charged Controlled Mem-Element Emulator and Its Application in a Chaotic System

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