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From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Martin Do Pham1, Amedeo D'Angiulli2, Maryam Mehri Dehnavi1

  • 1Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada.

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|September 28, 2023
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Summary

This study explores the integration of computational efficiency and biological realism in spiking neural networks (SNNs) for AI and robotics. Neuromorphic computing offers a promising path for embodied cognition, bridging neuroscience and artificial intelligence.

Keywords:
embodied cognitionneuromorphicsneuroroboticsspiking neural networks

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Area of Science:

  • Intersection of Neuroscience, Artificial Intelligence, and Robotics
  • Computational Neuroscience
  • Embodied Cognition

Background:

  • Spiking Neural Networks (SNNs) are crucial for autonomous intelligent behaviors in biological systems.
  • Bridging computational efficiency and biological plausibility in SNNs is a key challenge.
  • Existing research reviews neuromorphic computing in robotic control, but less on closing the cognition loop.

Purpose of the Study:

  • To review the interplay between computational efficiency and biological plausibility in SNNs.
  • To critically compare hardware and software for SNN emulation.
  • To explore the role of neuromorphics in embodied cognition for robotics.

Main Methods:

  • Transdisciplinary review of historical and recent influences from neuroscience, AI, and robotics.
  • Analysis of Spiking Neural Network (SNN) models.
  • Comparison of neuromorphic hardware and software for SNN emulation.
  • Dissection and contextualization of SNN concepts.

Main Results:

  • Neuromorphic computing is identified as a promising tool for embodying SNNs in physical systems.
  • Biologically viable spiking neuronal models are suitable for explainability studies.
  • Robotic modules like perception, localization, and cognition can benefit from neuromorphic hardware.

Conclusions:

  • Neuromorphics effectively addresses the trade-off between computational power and biological plausibility in hardware.
  • Neuromorphic systems in neurorobotics provide a testbed for synthetic and natural embodied cognition.
  • Future multidisciplinary efforts should focus on converging theoretical and empirical work in this area.