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, 11 (1), 268

A Bioinspired Analogous Nerve Towards Artificial Intelligence


A Bioinspired Analogous Nerve Towards Artificial Intelligence

Xinqin Liao et al. Nat Commun.


A bionic artificial device commonly integrates various distributed functional units to mimic the functions of biological sensory neural system, bringing intricate interconnections, complicated structure, and interference in signal transmission. Here we show an all-in-one bionic artificial nerve based on a separate electrical double-layers structure that integrates the functions of perception, recognition, and transmission. The bionic artificial nerve features flexibility, rapid response (<21 ms), high robustness, excellent durability (>10,000 tests), personalized cutability, and no energy consumption when no mechanical stimulation is being applied. The response signals are highly regionally differentiated for the mechanical stimulations, which enables the bionic artificial nerve to mimic the spatiotemporally dynamic logic of a biological neural network. Multifunctional touch interactions demonstrate the enormous potential of the bionic artificial nerve for human-machine hybrid perceptual enhancement. By incorporating the spatiotemporal resolution function and algorithmic analysis, we hope that bionic artificial nerves will promote further development of sophisticated neuroprosthetics and intelligent robotics.

Conflict of interest statement

The authors declare no competing interests.


Fig. 1
Fig. 1. Biological sensory neurons and an artificial perception and transmission nerve.
a Schematic of the function of biological sensory neurons. Mechanical stimulations are converted into receptor potentials by mechanoreceptors. The receptor potentials induce postsynaptic potentials by synapses. Postsynaptic potentials are transmitted to the cortex for information processing through the interneurons. b Architecture and working mechanism of an artificial perception and transmission nerve (APT nerve). Mechanical stimulation applied on the APT nerve is directly converted into mechanosensitive signals for information processing. Top left: Working mechanism of the APT nerve. Top right: Equivalent circuit. Bottom right: Structure of the APT nerve in right section view.
Fig. 2
Fig. 2. Performance and characteristic of the APT nerve.
a Influence of the thickness of spacer on the response resistance of the APT nerve with the active width of 5mm. Relationship between the location of mechanical stimulation and the response resistance of the APT nerve with different active b width and c length when the spacer was 0.12 mm. d Diagram of one type of mechanical stimulation applying on the APT nerve. Top left: Schematic of a single synapse. Δt is the interval time between adjacent mechanical stimulations. e Change in the response resistance of the APT nerve triggered by the mechanical stimulation at different intervals (5.6 s and 2.7 s). The mechanical stimulation was applied at the location of 5 cm of the APT nerve. f Diagram of mechanical stimulations applying at different locations (3 cm and 6 cm) of the APT nerve. Top left: Schematic of spatiotemporally dynamical stimulations of two synapses. g Spatiotemporally dynamical response of the APT nerve to the mechanical stimulations. The interval was 2.7 s.
Fig. 3
Fig. 3. Multifunctional touch interaction of the APT nerve.
a Circuit schematic to drive the APT nerve for applications. b A linear APT nerve used for playing music. The tonic solfa of Do, Re, Mi, Fa, Sol, La, and Si would be produced by touching corresponding segments of the APT nerve. c Response resistance of the linear APT nerve when touching different segments. d Change in the voltage producing corresponding tonic solfa. e An L-shaped APT nerve used for controlling the position of a chess piece in a two-dimensional plane. The horizontal branch controlled the horizontal movement of the chess piece and the other branch controlled the movement of the chess piece on the vertical axis. Bottom: Change in the voltage when touching different segments. f Demonstration of handling the rotation of an earth model by using a flexible APT nerve with a square shape. Each perception area was 10 × 10 mm2. Bottom: Touching different perception areas resulting in the changes in the voltage.
Fig. 4
Fig. 4. The APT nerve for perceptual learning.
a Schematic diagram of the machine learning based on the APT nerve, including recognition of mechanical stimulation, signal acquisition, feature extraction, decision of neural network. b Mechanosensitive signals from the APT nerve corresponding to different input information. The touching characteristic parameters included the touching location (L), holding time (H), latency interval (I) to characterize different mechanical stimulation within a set time (10 s). c Three-factor feature map based on the touching characteristic parameters extracted from b.

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