Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Nikiruy, K. E.; Surazhevsky, Igor A.; Демин, В. А.; Emelyanov, A. V.

doi:10.1002/pssa.201900938

Cited by 13 publications

(4 citation statements)

References 46 publications

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“…excitatory or inhibitory), as well as to model various SNNs capable of RL. Moreover, obtained results are similar to those demonstrated for inorganic nanocomposite memristors with the different RS mechanism (multifilamentary valence change) [47,48] which supports that the suggested method of STDP-modulation can be used for NNs with a wide range of synaptic weight types. It is also worth noting that such modulation can potentially be implemented in hardware using special hardware neurons with support for independent adjustment of the amplitude and duration of spikes directly during network operation [49].…”

Section: Dopamine-modulated Stdp Characterizationsupporting

confidence: 82%

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Миннеханов¹,

Shvetsov²,

Emelyanov³

et al. 2021

J. Phys. D: Appl. Phys.

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Nowadays there is a growing interest in wearable and biocompatible computing systems that are safe for the human body. Memristive devices are prospective for such tasks owing to a number of their attractive properties, in particular, the multilevel character of resistive switching, or plasticity, which allows them to emulate synapses in hardware neuromorphic networks (NNs). The use of local learning rules for such NNs, for example, bioinspired spike-timing-dependent plasticity (STDP), has firmly established itself in recent years. In biological systems the basic STDP can be modified in the presence of neuromodulators (e.g. dopamine). This effect is believed to be essential for important biological functions such as reinforcement learning (RL), memory and others. The goal of this work was to demonstrate that such dopamine-like modulated STDP can be used in memristors based on a biocompatible polymer, parylene (poly-p-xylylene, or PPX). We have studied memristors both in the form of single Cu/PPX/ITO devices and in the form of crossbar Cu/PPX/Au structures. It was found that, in addition to stable memristive characteristics suitable for NNs, these devices can also change their conductance by means of bioinspired STDP rules, including dopamine-like modulated STDP window realized by introducing the coefficients for neuron spike amplitudes. The amplitude coefficients from −1 (inhibitory mode) to 1 (excitatory mode) of pre-and post-spikes, reflecting the 'dopamine' concentration, in various combinations allow observing the STDP window not only of the usual shape, but also of the anti-STDP, bell and anti-bell shapes. The obtained results demonstrate that the development of memristors based on PPX provides prospects for hardware realization of bio-inspired spiking NNs with RL ability.

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Section: Dopamine-modulated Stdp Characterizationsupporting

confidence: 82%

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Миннеханов¹,

Shvetsov²,

Emelyanov³

et al. 2021

J. Phys. D: Appl. Phys.

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“…The NC films, along with the metal nanogranules of 3-6 nm, contained a large number of dispersed Co (Fe) atoms (up to ~10 21 -10 22 cm −3 ). At some optimal x ≈ 8-15 at.%, the M/NC/M structures manifest stable resistive switching (RS) through a multifilamentary mechanism [32]; demonstrate high endurance, long retention, and multilevel RS [33,34]; and can be successfully used in NCSs [27,35].…”

Section: Resultsmentioning

confidence: 99%

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100−x Nanocomposite Memristors

et al. 2022

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Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.

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“…Recently, memristor-based RL has been widely studied. [21][22][23][24][25][26][27][28] However, in previous studies, the training process of RL task was mainly accelerated by using the matrix-vector-multiplication (MVM) inherent to memristor. [22,23] Hardware implementations of Sarsa (λ) have rarely been verified either, which requires stable λ decay to enhance the convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning

Dai

Guo

Feng

et al. 2023

Advanced Intelligent Systems

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A memristor‐based reinforcement learning (RL) system has shown outstanding performance in achieving efficient autonomous decision‐making and edge computing. Sarsa (λ) is a classical multistep RL algorithm that records state with λ decay and guides policy updates, significantly improving the algorithm convergence speed. However, λ decay implementation of traditional computing hardware is confined by the extensive computation of power exponential decay. Herein, the value update equation for Sarsa (λ) is implemented by using the topological structure of the memristor array, without complex circuits. Where, most importantly, the critical λ decay function is realized by a TiOx‐based memristor with conductance decay property. The energy required for floating‐point operations can be significantly reduced while accelerating the convergence speed. Then, a path planning task is demonstrated based on intrinsic conductance decay property and shows outstanding performance. Finally, the information of rounds used for the task is obtained, which is based on the intrinsic decay property of the TiOx‐based memristor, maps into a 32 × 32 memristor array in parallel to calculate the value of each round. The results indicate that the experimental data have similar results to the simulations. Herein, thus, it provides a hardware‐enabled scheme for the memristor‐based RL algorithm implementation.

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Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Cited by 13 publications

References 46 publications

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100−x Nanocomposite Memristors

Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning

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Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Cited by 13 publications

References 46 publications

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100−x Nanocomposite Memristors

Intrinsic Decay Property of Ti/TiOx/Pt Memristor for Reinforcement Learning

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Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning