Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device

Abstract

Animals' survival is dependent on their abilities to adapt to the changing environment by adjusting their behaviours, which is related to the ubiquitous learning behaviour, nonassociative learning. Thus mimicking the indispensable learning behaviour in organisms based on electronic devices is vital to better achieve artificial neural networks and neuromorphic computing. Here a three terminal device consisting of an oxide-based memristor and a NMOS transistor is proposed. The memristor with gradual conductance tuning inherently functions as the synapse between sensor neurons and motor neurons and presents adjustable synaptic plasticity, while the NMOS transistor attached to the memristor is utilized to mimic the modulatory effect of the neuromodulator released by inter neurons. Such a memristor-based multi-terminal device allows the practical implementation of significant nonassociative learning based on a single electronic device. In this study, the experience-induced modification behaviour, both habituation and sensitization, was successfully achieved. The dependence of the nonassociative behavioural response on the strength and interval of presented stimuli was also discussed. The implementation of nonassociative learning offers feasible and experimental advantages for further study on neuromorphic systems based on electronic devices.

Memristive Synapses for Brain‐Inspired Computing

“…However, no obvious advantage can be found over synaptic devices based on bilayer and trilayer structures. Due to a simple structure, oxide single‐layers have also been widely used for synaptic devices, including AlO x , FeO x , HfO x , PrCaMnO x , SrTiO 3 , TaO x , TiO x , WO x , ZnHfO x , ZrHfO x , KNbO 3 , BiFeO 3 , SiO x , and NiO x . The working mechanism of PrCaMnO x ‐based memristive devices is widely attributed to field‐driven oxygen migration and redox reaction at a metal/PrCaMnO x interface .…”

Recent progress in memristors for stimulating synaptic plasticity

“…如图 22 [72] 所示高于阈值电压的输入 (0.3 V, 10 µs) 使回路产生了 0.33 V 的输出电压, 而低于阈 值电压的输入 (0.2 V, 10 µs) 则仅仅生了 0.028 V 的输出电压, 模拟阈值转换特点. 而由于两条通路需 [87] Figure 24 (Color online) The implementation of sensitization. (a) The modulatory effect of NMOS transistor, the current measured under every gate voltage was represented by different colours; (b)two forms of measured currents changed against the repetition of two different stimulation trains respectively [87] [88] .…”

“…而由于两条通路需 [87] Figure 24 (Color online) The implementation of sensitization. (a) The modulatory effect of NMOS transistor, the current measured under every gate voltage was represented by different colours; (b)two forms of measured currents changed against the repetition of two different stimulation trains respectively [87] [88] . 近几年, 有研究者提出了 "第二代 迁移型忆阻器"--利用热耗散或者迁移率衰减的机制来模拟 STDP 功能, 其特点是重复度高和简易 性, 但是它们的精确性和可模拟突触功能的多样性受到了限制 [49] .…”

Associative Memory for Image Recovery with a High‐Performance Memristor Array

“…Memristor is a promising candidate in neuromorphic computing owing to its fast switching operation, simple structure, low energy consumption, and compatible manufacturing processes with existing CMOS technology . Recently, some successful neuromorphic applications have been demonstrated with memristor arrays . As one of the vital functions of the human brain, the memorization ability can be emulated by implementing an HNN on a memristor.…”