An area and energy efficient LIF neuron model with spike frequency adaptation mechanism

Zare, Maryam; Zafarkhah, Elnaz; Anzabi-Nezhad, Nima S.

doi:10.1016/j.neucom.2021.09.004

Cited by 18 publications

(14 citation statements)

References 32 publications

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“…The LIF/IF neuron model supports bio-plausible neuronal activity, alongside a biophysical explanation for this electrical activity [42,43,[115][116][117][118]. A simple device or circuit with threshold properties is generally sufficient to implement IF ch as resting potential, depolarization, hyperpolarization, and repolarization [119,120].…”

Section: Artificial Neuron Functional Modelsmentioning

confidence: 85%

Advances in memristor based artificial neuron fabrication-materials, models, and applications

Bian,

Liu,

Tao

et al. 2023

Int. J. Extrem. Manuf.

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Spiking Neural Network (SNN), widely known as the third-generation neural network, has been frequently investigated due to its excellent spatiotemporal information processing capability, high biological plausibility and low energy consumption characteristics. Analogous to the working mechanism of human brain, SNN system transmits information through spiking action of neurons. Therefore, artificial neurons are critical building blocks for constructing SNN in hardware. Memristors are drawing growing attentions due to low consumption, high speed, and nonlinearity characteristics, which are recently introduced to mimic the functions of biological neurons. Researchers have proposed multifarious memristive materials including organic materials, inorganic materials, or even two- dimensional materials. Taking advantage of the unique electrical behavior of these materials, several neuron models are successfully implemented, such as Hodgkin-Huxley (HH) model, leaky integrate-and-fire (LIF) model and integrate-and-fire (IF) model. In this review, the recent reports of artificial neuron based on memristive devices are discussed. In addition, we highlight the functions and applications through combining artificial neuronal device with sensors or the other electronic devices. Finally, the future challenges and outlooks of memristor-based artificial neurons are discussed, and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

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Section: Artificial Neuron Functional Modelsmentioning

confidence: 85%

Advances in memristor based artificial neuron fabrication-materials, models, and applications

Bian,

Liu,

Tao

et al. 2023

Int. J. Extrem. Manuf.

View full text Add to dashboard Cite

show abstract

“…There are several proposals reported on analog implementations of neural model circuits (Abbott, 1999 ; Wijekoon and Dudek, 2008 ; Zamarreño-Ramos et al, 2011 ; Wu et al, 2015 ; Zare et al, 2021 ). Throughout the development of this study, the leaky integrate and fire neuron (IFN) circuit, proposed in Wu et al ( 2015 ) was used, as seen in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Neuromorphic Signal Filter for Robot Sensoring

et al. 2022

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Noise management associated with input signals in sensor devices arises as one of the main problems limiting robot control performance. This article introduces a novel neuromorphic filter model based on a leaky integrate and fire (LIF) neural model cell, which encodes the primary information from a noisy input signal and delivers an output signal with a significant noise reduction in practically real-time with energy-efficient consumption. A new approach for neural decoding based on the neuron-cell spiking frequency is introduced to recover the primary signal information. The simulations conducted on the neuromorphic filter demonstrate an outstanding performance of white noise rejecting while preserving the original noiseless signal with a low information loss. The proposed filter model is compatible with the CMOS technology design methodologies for implementing low consumption smart sensors with applications in various fields such as robotics and the automotive industry demanded by Industry 4.0.

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“…A leaky integrate-and-fire (LIF) neuron [ 10 , 11 , 12 , 13 , 14 , 15 ] represents the simplest spiking neuron model, which is based on a threshold (switching) element and an RC integrator, and has multiple implementation methods, including methods based on CMOS technology [ 14 , 15 ]. A two-terminal element with an S-shaped I–V characteristic (S-IVC) [ 13 ] can be used as a switching element and is widely represented in electronics, for example, in silicon trigger diodes and thin-film structures based on oxides of transition metals (V, Nb, Ti and others) [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch

Борисков

Velichko

Shilovsky

et al. 2022

Entropy

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This paper presents a model and experimental study of a chaotic spike oscillator based on a leaky integrate-and-fire (LIF) neuron, which has a switching element with an S-type current-voltage characteristic (S-switch). The oscillator generates spikes of the S-switch in the form of chaotic pulse position modulation driven by the feedback with rate coding instability of LIF neuron. The oscillator model with piecewise function of the S-switch has resistive feedback using a second order filter. The oscillator circuit is built on four operational amplifiers and two field-effect transistors (MOSFETs) that form an S-switch based on a Schmitt trigger, an active RC filter and a matching amplifier. We investigate the bifurcation diagrams of the model and the circuit and calculate the entropy of oscillations. For the analog circuit, the “regular oscillation-chaos” transition is analysed in a series of tests initiated by a step voltage in the matching amplifier. Entropy values are used to estimate the average time for the transition of oscillations to chaos and the degree of signal correlation of the transition mode of different tests. Study results can be applied in various reservoir computing applications, for example, in choosing and configuring the LogNNet network reservoir circuits.

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An area and energy efficient LIF neuron model with spike frequency adaptation mechanism

Cited by 18 publications

References 32 publications

Advances in memristor based artificial neuron fabrication-materials, models, and applications

Advances in memristor based artificial neuron fabrication-materials, models, and applications

Neuromorphic Signal Filter for Robot Sensoring

Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch

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