Article Synopsis

  • The research focuses on creating an artificial sensory nervous system for robots, modeled after biological systems, that can efficiently respond to external stimuli without needing bulky circuitry.
  • A new memristor device is introduced, capable of mimicking biological functions like habituation and sensitization, which allows it to adaptively ignore familiar stimuli while responding to important or threatening ones.
  • Experimental results show that the robotic system using this memristor-based setup effectively discards 71% of non-threatening stimuli, suggesting potential for creating more energy-efficient and intelligent robots.

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Article Abstract

The sensory nervous system in animals enables the perception of external stimuli. Developing an artificial sensory nervous system has been widely conducted to realize neuro-inspired robots capable of effectively responding to external stimuli. However, it remains challenging to develop artificial sensory nervous systems that possess sophisticated biological functions, such as habituation and sensitization, enabling efficient responses without bulky peripheral circuitry. Here, we introduce a memristor device with third-order switching complexity, emulating an artificial synapse that inherently possesses habituation and sensitization properties. Incorporating an additional resistive switching TiO layer into the HfO memristor exhibits third-order switching complexity and non-volatile habituation characteristics. Based on the third-order memristor, we propose a robotic system equipped with a memristor-based artificial sensory nervous system for optimizing the robot arm's response to external stimuli without the aid of processors. It is experimentally demonstrated that the robot arm with the developed memristor-based artificial sensory nervous system ignores approximately 71% of safe and familiar stimuli while sensitively responding to threatening and significant stimuli, similar to the habituation and sensitization of biological sensory nervous systems. Our findings can be a stepping stone for energy-efficient and intelligent robotic systems with reduced hardware burden.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12215477PMC
http://dx.doi.org/10.1038/s41467-025-60818-xDOI Listing

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