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<p>Neurobiological circuits in the brain, operating in Super-Turing mode, process information while simultaneously modifying their synaptic connections through learning, allowing them to dynamically adapt to changes. In contrast, artificial intelligence systems based on computers operate in Turing mode and lack the ability to concurrently infer and learn, making them vulnerable to failure under dynamically changing conditions. Here we show a synaptic resistor circuit that operates in Super-Turing mode, enabling concurrent learning and inference. The circuit controls a morphing wing to reduce its drag-to-lift force ratio and recover from stalls in complex aerodynamic environments. The synaptic resistor circuit demonstrates superior performance, faster learning speeds, enhanced adaptability, and reduced power consumption compared to artificial neural networks and human operators on the same task. By overcoming the fundamental limitations of computers, synaptic resistor circuits offer high-speed concurrent learning and inference, ultra-low power consumption, error correction, and agile adaptability for artificial intelligence systems.</p>

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Super-Turing synaptic resistor circuits for intelligent morphing wing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Atharva Deo,
  • Jungmin Lee,
  • Dawei Gao,
  • Rahul Shenoy,
  • Kevin PT. Haughn,
  • Zixuan Rong,
  • Yong Hei,
  • D. Qiao,
  • Tanay Topac,
  • Fu-Kuo Chang,
  • Daniel J. Inman,
  • Yong Chen

摘要

Neurobiological circuits in the brain, operating in Super-Turing mode, process information while simultaneously modifying their synaptic connections through learning, allowing them to dynamically adapt to changes. In contrast, artificial intelligence systems based on computers operate in Turing mode and lack the ability to concurrently infer and learn, making them vulnerable to failure under dynamically changing conditions. Here we show a synaptic resistor circuit that operates in Super-Turing mode, enabling concurrent learning and inference. The circuit controls a morphing wing to reduce its drag-to-lift force ratio and recover from stalls in complex aerodynamic environments. The synaptic resistor circuit demonstrates superior performance, faster learning speeds, enhanced adaptability, and reduced power consumption compared to artificial neural networks and human operators on the same task. By overcoming the fundamental limitations of computers, synaptic resistor circuits offer high-speed concurrent learning and inference, ultra-low power consumption, error correction, and agile adaptability for artificial intelligence systems.