A memristive spiking neuron with firing rate coding

Ignatov, Marina; Ziegler, Martin; Hansen, Mirko; Petraru, A.; Kohlstedt, H.

doi:10.3389/fnins.2015.00376

Cited by 54 publications

(45 citation statements)

References 43 publications

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“…Recent studies highlighted the interest of VO 2 switch applications in the SNN, ONN, and neural-like circuits [9,12,48,[51][52][53]. The study [9] illustrates the implementation of VO 2 oscillators with various chaotic and burst modes of spike generation based on two switches.…”

Section: Discussionmentioning

confidence: 99%

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

Борисков

Velichko

2019

Electronics

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In this paper, we present circuit solutions based on a switch element with the S-type I–V characteristic implemented using the classic FitzHugh–Nagumo and FitzHugh–Rinzel models. Using the proposed simplified electrical circuits allows the modeling of the integrate-and-fire neuron and burst oscillation modes with the emulation of the mammalian cold receptor patterns. The circuits were studied using the experimental I–V characteristic of an NbO2 switch with a stable section of negative differential resistance (NDR) and a VO2 switch with an unstable NDR, considering the temperature dependences of the threshold characteristics. The results are relevant for modern neuroelectronics and have practical significance for the introduction of the neurodynamic models in circuit design and the brain–machine interface. The proposed systems of differential equations with the piecewise linear approximation of the S-type I–V characteristic may be of scientific interest for further analytical and numerical research and development of neural networks with artificial intelligence.

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Section: Discussionmentioning

confidence: 99%

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

Борисков

Velichko

2019

Electronics

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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%

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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“…Devices based on S-type NDR have been widely used in electronic circuits [1] including oscillators and amplifiers. [5,6] Most of reported S-type NDR result from distinct properties of materials, such as electronic instabilities, [7,8] interband tunneling, [9,10] threshold switching, [11][12][13] insulator-metal transitions in metal oxides. For instance, selector based on S-type NDR can limit the leakage current in memristor cross-bar arrays due to highly nonlinear I-V characteristics.…”

Section: Negative Differential Resistancementioning

confidence: 99%

S‐Type Negative Differential Resistance in Semiconducting Transition‐Metal Dichalcogenides

Wang

et al. 2019

Adv Elect Materials

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Figure 4. Simulation of neuron spikes. a) Circuit layout of simulating neuron spikes. The circuit consists of a self-oscillating circuit (highlighted by blue dashed box) based on G/MoS 2 /G NDR device and an output branch with a diode D (1N4007) in series with two ohmic resistors R 1 and R 2 , which deliver the spikes from the oscillating voltage. Parameters used for the circuit: V C = −2.3 V, I = 8 mA, C = 100 µF, R 1 = 50 kΩ, R 2 = 10 kΩ. b,c) Recorded spike patterns for V 1 and V 2 in (a), respectively. www.advancedsciencenews.com

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A memristive spiking neuron with firing rate coding

Cited by 54 publications

References 43 publications

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

Memristive Synapses and Neurons for Bioinspired Computing

S‐Type Negative Differential Resistance in Semiconducting Transition‐Metal Dichalcogenides

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