An memristor-based synapse implementation using BCM learning rule

Huang, Yongchuang; Liu, Junxiu; Harkin, Jim; McDaid, Liam; Luo, Yuling

doi:10.1016/j.neucom.2020.10.106

Cited by 24 publications

(14 citation statements)

References 32 publications

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“…This is a common use approximation in neuroscience (28). As reported before, the real BCM curves measured in biological cells show a more complex shape that can be simplistically described with a parabolic model (42,48,49). The crucial point for the model is to properly follow the dynamics of the threshold between potentiation and depression.…”

Section: Bioinspired Second-order Nn Demonstrates Binocular Direction...mentioning

confidence: 90%

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

et al. 2022

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With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing–dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

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Section: Bioinspired Second-order Nn Demonstrates Binocular Direction...mentioning

confidence: 90%

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

et al. 2022

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“…An STDP-based SNN (Zhao et al, 2020 ) was proposed to achieve the mechanism of lateral inhibition and homeostasis by memristor-based inhibitory synapses. A novel memristive synapse model based on the HP memristor was proposed, and a spiking neural network hardware fragment was constructed (Huang et al, 2021 ).…”

Section: Preliminariesmentioning

confidence: 99%

Mapping the BCPNN Learning Rule to a Memristor Model

Wang

Stathis

et al. 2021

Front. Neurosci.

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The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in a way that allows mapping to neural and synaptic processes in the human cortexandhas been used extensively in detailed spiking models of cortical associative memory function and recently also for machine learning applications. In conventional digital implementations of BCPNN, the von Neumann bottleneck is a major challenge with synaptic storage and access to it as the dominant cost. The memristor is a non-volatile device ideal for artificial synapses that fuses computation and storage and thus fundamentally overcomes the von Neumann bottleneck. While the implementation of other neural networks like Spiking Neural Network (SNN) and even Convolutional Neural Network (CNN) on memristor has been studied, the implementation of BCPNN has not. In this paper, the BCPNN learning rule is mapped to a memristor model and implemented with a memristor-based architecture. The implementation of the BCPNN learning rule is a mixed-signal design with the main computation and storage happening in the analog domain. In particular, the nonlinear dopant drift phenomenon of the memristor is exploited to simulate the exponential decay of the synaptic state variables in the BCPNN learning rule. The consistency between the memristor-based solution and the BCPNN learning rule is simulated and verified in Matlab, with a correlation coefficient as high as 0.99. The analog circuit is designed and implemented in the SPICE simulation environment, demonstrating a good emulation effect for the BCPNN learning rule with a correlation coefficient as high as 0.98. This work focuses on demonstrating the feasibility of mapping the BCPNN learning rule to in-circuit computation in memristor. The feasibility of the memristor-based implementation is evaluated and validated in the paper, to pave the way for a more efficient BCPNN implementation, toward a real-time brain emulation engine.

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“…Currently, the vision sensors are emerging to mimic receptive field of ganglion cell in retina for simultaneously sensing and processing. The emulation of experience-dependent modifications of synaptic strength to form binocular, orientation selective receptive field that observed in the striate cortex lags considerably behind that of the retina 36 – 38 . In this work, we first report the bioinspired striate cortex with binocularity and orientation selectivity based on the crossbar array of self-powered memristors where each cross-point contains one CsFAPbI 3 perovskite solar cell and one CsPbBr 2 I perovskite memristor.…”

Section: Introductionmentioning

confidence: 99%

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

Ren

Wang

et al. 2022

Nat Commun

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Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

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An memristor-based synapse implementation using BCM learning rule

Cited by 24 publications

References 32 publications

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

Mapping the BCPNN Learning Rule to a Memristor Model

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

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