Abstract
The human sensory system has a fascinating stimulus-detection capability attributed to the fact that the feature (pattern) of an input stimulus can be extracted through perceptual learning. Therefore, sensory information can be organized and identified efficiently based on iterative experiences, whereby the sensing ability is improved. Specifically, the distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. Herein, we demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network composed of a single device: a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. Furthermore, the developed circuit model of the system provides quantitative analytical and product-level feasibility. This work is a step toward the design and use of a neuromorphic sensory system with a learning capability for potential applications in robotics and prosthetics.
Highlights
Over the past half-century, device electronics have been successfully advancing the Information Age thanks to consistent performance improvements based on the downscaling of digital devices that can provide reliable logic-gate operations, benefiting from their robustness to high levels of noise
Some analog devices exhibit volatile behaviors for input stimuli; i.e., they yield a temporal state enhancement that quickly decays to its initial state, which constitutes one of the essential characteristics of a neuromorphic system. Research on this volatile behavior focused on the emulation of short-term plasticity (STP) in biological synapses, such as paired-pulse facilitation (PPF)[9,10]
We demonstrate a biorealistic tactile sensor system wherein both the sensory neurons and perceptual synaptic network are implemented by a semivolatile carbon nanotube (CNT) transistor
Summary
Over the past half-century, device electronics have been successfully advancing the Information Age thanks to consistent performance improvements based on the downscaling of digital devices that can provide reliable logic-gate operations, benefiting from their robustness to high levels of noise. Research paradigms have shifted the focus to conventional devices with analog characteristics, which were previously considered a drawback. Some analog devices exhibit volatile behaviors for input stimuli; i.e., they yield a temporal state enhancement that quickly decays to its initial state, which constitutes one of the essential characteristics of a neuromorphic system. Research on this volatile behavior focused on the emulation of short-term plasticity (STP) in biological synapses, such as paired-pulse facilitation (PPF)[9,10]. Recent studies have emulated biological neurons, including their capability to integrate temporal input stimuli[11,12] based on the leaky integrate-and-fire (I&F) neuron
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