The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.

Ambipolar transistors represent a class of transistors where positive (holes) and negative (electrons) charge carriers both can transport concurrently within the semiconducting channel. The basic switching states of ambipolar transistors are comprised of common off-state and separated on-state mainly impelled by holes or electrons. During the past years, diverse materials are synthesized and utilized for implementing ambipolar charge transport and their further emerging applications comprising ambipolar memory, synaptic, logic, and light-emitting transistors on account of their special bidirectional carrier-transporting characteristic. Within this review, recent developments of ambipolar transistor field involving fundamental principles, interface modifications, selected semiconducting material systems, device structures, ambipolar characteristics, and promising applications are highlighted. The existed challenges and prospective for researching ambipolar transistors in electronics and optoelectronics are also discussed. It is expected that the review and outlook are well timed and instrumental for the rapid progress of academic sector of ambipolar transistors in lighting, display, memory, as well as neuromorphic computing for artificial intelligence.

Photonic computing and neuromorphic computing could address the inherent limitations of traditional von Neumann architecture and gradually invalidate Moore's law. As photonics applications are capable of storing and processing data in an optical manner with unprecedented bandwidth and high speed, two-terminal photonic memristors with a remote optical control of resistive switching behaviors at defined wavelengths ensure the benefit of on-chip integration, low power consumption, multilevel data storage, and a large variation margin, suggesting promising advantages for both photonic and neuromorphic computing. Herein, the development of photonic memristors is reviewed, as well as their application in photonic computing and emulation on optogenetics-modulated artificial synapses. Different photoactive materials acting as both photosensing and storage media are discussed in terms of their optical-tunable memory behaviors and underlying resistive switching mechanism with consideration of photogating and photovoltaic effects. Moreover, light-involved logic operations, system-level integration and light-controlled artificial synaptic memristors along with improved learning tasks performance are presented. Furthermore, the challenges in the field are discussed, such as the lack of a comprehensive understanding of microscopic mechanisms under light illumination and a general constraint of inferior near-infrared (NIR) sensitivity.

Motivated by the biological neuromorphic system with high degree of connectivity to process huge amounts of information, transistor‐based artificial synapses are expected to pave a way to overcome the von Neumann bottleneck for neuromorphic computing paradigm. Here, artificial flexible organic synaptic transistors capable of concurrently exhibiting signal transmission and learning functions are verified using C60/poly(methyl methacrylate) (PMMA) hybrid layer for the first time. C60 trapping sites are doped in PMMA by facile solution process to form the hybrid structure. The flexible synaptic transistor exhibits a memory window of 2.95 V, a currenton/currentoff ratio greater than 103, program/erase endurance cycle over 500 times. In addition, comprehensive synaptic functions of biosynapse including the excitatory postsynaptic current with different duration time, pulse amplitudes and temperatures, paired‐pulse facilitation/depression, potentiation and depression of the channel conductance modulation, transition from short‐term potentiation to long‐term potentiation, and repetitive learning processes are successfully emulated in this synaptic three‐terminal device. The realization of synaptic devices based on C60 with low operation voltage and controlled polarity of charge trapping is an important step toward future neuromorphic computing using organic electronics.

The traditional Von Neumann architecture‐based computers are considered to be inadequate in the coming artificial intelligence era due to increasing computation complexity and rising power consumption. Neuromorphic computing may be the key role to emulate the human brain functions and eliminate the Von Neumann bottleneck. As a basic unit in the nervous system, a synapse is responsible for transmitting information between neurons. Resistive random access memory (RRAM) is able to imitate the synaptic functions because of its tunable resistive switching behavior. Here, an artificial synapse based on solution processed polyvinylpyrrolidone (PVPy)–Au nanoparticle (NP) hybrid is fabricated, various synaptic functions including paired‐pulse facilitation (PPF), posttetanic potentiation (PTP), transformation from short‐term plasticity (STP) to long‐term plasticity (LTP) and learning‐forgetting‐relearning process are emulated, making the polymer–metal NPs hybrid system valuable candidates for the design of novel artificial neural architectures.