Research Progress of Neural Synapses Based on Memristors

Abstract

The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and analog memristors. Synapses based on digital memristors have been utilized to construct positive, zero, and negative weights for artificial neural networks, while synapses based on analog memristors have demonstrated their ability to simulate the essential functions of neural synapses, such as short-term memory (STM), long-term memory (LTM), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation (PPF). Furthermore, synapses based on analog memristors have shown potential for performing advanced functions such as experiential learning, associative learning, and nonassociative learning. Finally, we highlight some challenges of building large-scale artificial neural networks using memristors.

Recent progress of organic artificial synapses in biomimetic sensory neural systems

“…STP plasticity is usually closely associated with critical spatiotemporal information processing operations in biological systems, whereas LTP is associated with learning and memory functions. 53 The study of synaptic plasticity is important for understanding the mechanisms of learning, memory, neurodevelopment and neurological disorders. 54 STP is a short-term enhancement of neuronal connectivity, typically lasting a few minutes or less, whereas LTP is a permanent enhancement of neuronal connectivity, typically lasting hours or months.…”

Investigation on Synaptic Adaptation and Fatigue in ZnO/HfZrO-Based Memristors under Continuous Electrical Pulse Stimulation

“…Hong et al developed a memory circuit specifically for non-associative learning [19]. Sun et al developed a memristor circuit that enables non-associative learning in various emotional states [20]. In light of the current situation, where achieving different response behaviors in non-associative learning necessitates artificially altering the voltage polarity in both directions, and the requirement for memory circuits designed for non-associative learning, we suggest employing straightforward bilayer polarized self-regulating devices to accomplish habituation or sensitization monitoring using a unidirectional voltage.…”

Introducing Chiro-optical Activities in Photonic Synapses for Neuromorphic Computing and In-Memory Logic Operations

“…In this direction, a range of materials, such as metal oxides, organic semiconductors, phase change materials, perovskites, ,− 2D materials, − etc., are being considered. As device structures, two and three terminal device geometries in the form of memristors, ,, and field-effect transistors, , respectively, have been studied to successfully emulate the diverse synaptic functions of the human brain. Such functionalities are, in general, classified as electrical synaptic devices.…”