A bioinspired flexible organic artificial afferent nerve

By Yeongin Kim, Alex Chortos, Wentao Xu, Yuxin Liu, J. Oh, Donghee Son, Jiheong Kang, Amir Foudeh, Chenxin Zhu, Yeongjun Lee and 5 more. Science, 2018

1,260 citations recorded by the source corpus.

I've got a feeling Sensory (or afferent) nerves bring sensations of touch, pain, or temperature variation to the central nervous system and brain. Using the tools and materials of organic electronics, Kim et al. combined a pressure sensor, a ring oscillator, and an ion gel–gated transistor to form an artificial mechanoreceptor (see the Perspective by Bartolozzi). The combination allows for the sensing of multiple pressure inputs, which can be converted into a sensor signal and used to drive the motion of a cockroach leg in an oscillatory pattern. Science, this issue p. 998; see also p. 966 Organic flexible electronics mimic the functions of a biological afferent nerve and actuate muscles. The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/72dae6ea3dabfddc0cad14838b6b35c3b162cb93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Haixia Zhang
2. George G. Malliaras
3. Mengdi Han
4. Alberto Salleo
5. Haobin Wang
6. Yu Song
7. Zhong Lin Wang

In Cellular and Molecular Neuroscience

1. George G. Malliaras
2. Alberto Salleo
3. Armantas Melianas
4. Scott T. Keene
5. Y. van de Burgt

In Electrical and Electronic Engineering

1. George G. Malliaras
2. Mengdi Han
3. Alberto Salleo
4. Armantas Melianas
5. Scott T. Keene
6. Y. van de Burgt

Citations in the corpus

• Organic electronics for neuromorphic computing (2018, methodology): “We speculate that developments in organic neuromorphic computing could lead to and be exploited for enhanced hybrid biological/organic functionality such as trainable and adaptable brain–machine interfaces 106 , biosensor networks, robotic skin 113 and adaptable local control of prosthetics 109 .”