The neural system is a multifunctional perceptual learning system. Our brain can perceive different kinds of information to form senses, including touch, sight, hearing, and so on. Mimicking such perceptual learning systems is critical for neuromorphic platform applications. Here, an artificial tactile perceptual neuron is realized by utilizing electronic skins (E-skin) with oxide neuromorphic transistors, and this artificial tactile perceptual neuron successfully simulates biological tactile afferent nerves. First, the E-skin device is constructed using microstructured polydimethylsiloxane membranes coated with Ag/indium tin oxide (ITO) layers, exhibiting good sensitivities of ∼2.1 kPa–1 and fast response time of tens of milliseconds. Then, the chitosan-based electrolyte-gated ITO neuromorphic transistor is fabricated and exhibits high performance and synaptic responses. Finally, the integrated artificial tactile perceptual neuron demonstrates pressure excitatory postsynaptic current and paired-pulse facilitation. The artificial tactile perceptual neuron is featured with low energy consumption as low as ∼0.7 nJ. Moreover, it can mimic acute and chronic pain and nociceptive characteristics of allodynia and hyperalgesia in biological nociceptors. Interestingly, the artificial tactile perceptual neuron can employ “Morse code” pressure-interpreting scheme. This simple and low-cost approach has excellent potential for applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.

Neuromorphic devices and systems with ultralow power consumption are important in building artificial intelligent systems. Here, indium tin oxide (ITO)-based oxide neuromorphic transistors are fabricated using poly(vinyl alcohol) (PVA)-based proton-conducting electrolytes as gate dielectrics. The electrical performances of the transistors can be modulated with the ITO channel thickness. Fundamental synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, and multistore memory, are successfully emulated. Most importantly, the PVA-gated neuromorphic devices demonstrate ultralow energy consumption of ∼1.16 fJ with ultrahigh sensitivity of ∼5.4 dB, as is very important for neuromorphic engineering applications. Because of the inherent environmental-friendly characteristics of PVA, the devices possess security biocompatibility. Thus, the proposed PVA-gated oxide neuromorphic transistors may find potential applications in "green" ultrasensitive neuromorphic systems and efficient electronic biological interfaces.

our brain would solve this dilemma, that is, the von Neumann bottleneck. Thus, neuromorphic engineering comes into being. Basically, data-driven neuromorphic engineering needs to complete a large number of data processing tasks. Presently, one of the main tasks in the state-ofthe-art neuromorphic computing system is to optimize neuromorphic algorithm. Due to the use of von Neumann architecture, such neuromorphic systems always consume extremely high energy. In fact, our brain nervous system is consisted of ≈10 11 neurons connected by ≈10 15 synapses. [3] Neurons and synapses are basic units in brain information processing. Brain cognitive behaviors occur through synaptic responses and neural functions. And the synaptic plasticity is the most important characteristics of synapse. Such architecture makes our brain operate in extremely high energy efficiency with certain fault tolerance to respond to our surroundings. Thus, brain-inspired neuromorphic devices have been proposed for mimicking biological synaptic responses and neural functions. [4][5][6] It is getting a new branch for neuromorphic engineering.Recently, with the developments of microelectronics, optoelectronics, and material technologies, solid-state neuromorphic devices have been proposed to mimic biological synaptic functions. Such devices include two-terminal resistance change memory devices [7][8][9][10] and field-effect transistor based neuromorphic transistors. [11][12][13][14][15] Due to the simple sandwich structure, two-terminal memristors have the inherent priority in 3D integrity. They have been widely investigated for high density storage applications. [16,17] With nonlinear electrical characteristics and nonvolatile resistance modulation effects, two-terminal memristors are very suitable for neuromorphic device applications. Such devices include ferroelectric random access memory (FeRAM), phase change random access memory (PCRAM), resistive random access memory (RRAM), etc. [18][19][20][21][22] In fact, most of the recent reported neuromorphic devices are based on two-terminal memristors. Especially with the deep understanding of the operation mechanisms of memristors, neural operation and typical Modified National Institute of Standards and Technology (MNIST) pattern recognition have also been demonstrated by using memristor arrays. [23][24][25] In addition, energy consumption is another important index in neuromorphic device applications. Recently, several works have been reported on neuromorphic device with low energy consumption. [26][27][28][29][30] Xu et al. [26] obtained organic nanowire synaptic transistors with energy consumption of ≈1.23 fJ per Recently, neuromorphic devices have attracted great attention due to their potential to overcome the von Neumann bottleneck. Due to their nonlinear electrical characteristics and nonvolatile resistance, memristors have been proposed for use in neuromorphic device applications. Bilayered HfO 2 /TiO x -based cognitive memristors are proposed. They demonstrate conductance-modulation capabilit...