Bioinspired artificial haptic neuron system has received much attention in the booming artificial intelligence industry for its broad range of high-impact applications such as personal healthcare monitoring, electronic skins, and human-machine interfaces. An artificial haptic neuron system is designed by integrating a piezoresistive sensor and a Nafion-based memristor for the first time in this paper. The piezoresistive sensor serves as a sensory receptor to transform mechanical stimuli into electric signals, and the Nafion-based memristor serves as the synapse to further process the information. The pyramid-structured sensor exhibits excellent sensitivity (6.7 × 10 7 kPa −1 in 1-5 kPa and 3.8 × 10 5 kPa −1 in 5-50 kPa) and durability (>7000 cycles), while the memristor realizes fundamental synaptic functions under low power consumption (10-200 pJ) and remains stable for over 10 4 consecutive tests. The integrated system can detect tactile stimuli encoded with temporal information, such as the count, frequency, duration and speed of the external force. As a proof-of-concept, English characters recognition with high accuracy can be achieved on the system under a supervised learning method. This work shows promising potential in bioinspired sensing systems owing to the high performance, excellent durability, and simple fabrication procedure.
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.
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.
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