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[A grid field calculation model based on perceived speed and perceived angle].

Naigong Yu1, Hui Feng1, Yishen Liao1

  • 1Department of Informatics, Beijing University of Technology, Beijing 100124, P.R.China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
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PubMed
Summary
This summary is machine-generated.

This study introduces a biomimetic model for robot navigation, enhancing grid cell function using perceived speed and angle. This improves environmental encoding and path integration for cognitive maps.

Keywords:
grid cellgrid field computing modelperceived angleperceived speedspeed cell

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Area of Science:

  • Robotics
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Traditional grid cell models for environmental encoding lack anti-interference and biomimetic properties.
  • Directly using speed and angle information presents limitations in real-world robotic applications.

Purpose of the Study:

  • To propose a novel grid cell calculation model integrating perceived speed and angle for enhanced robot navigation.
  • To improve the biomimetic and anti-interference capabilities of robot cognitive map construction.

Main Methods:

  • Decoding visual streams for visual speed and modeling speed cells for body speed, then integrating them into perceived speed.
  • Simulating head direction cell firing mechanisms using a 1D circularly connected cell model to obtain perceived angle.
  • Combining perceived speed and angle information to drive the grid cell attractors model.

Main Results:

  • The proposed model successfully replicates the periodic hexagonal firing field mode of grid cells.
  • Experimental verification confirmed the model's precise path integration capabilities.
  • The model demonstrates improved biomimetic characteristics compared to previous methods.

Conclusions:

  • The developed model provides a robust method for driving grid cell attractors using integrated perceptual information.
  • This approach offers a foundation for constructing robot cognitive maps based on hippocampal cognition mechanisms.
  • The findings suggest potential advancements in biomimetic robot navigation and spatial cognition research.