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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Neuromorphic Engineering Needs Closed-Loop Benchmarks.

Moritz B Milde1, Saeed Afshar1, Ying Xu1

  • 1International Centre for Neuromorphic Systems, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Penrith, NSW, Australia.

Frontiers in Neuroscience
|March 3, 2022
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Summary
This summary is machine-generated.

Neuromorphic engineering seeks to mimic biological systems for more efficient AI. Current benchmarks focus on accuracy, but real-world, closed-loop tasks are vital for developing resilient neuromorphic intelligence.

Keywords:
ATISDAVISDVSaudiobenchmarksevent-based systemsneuromorphic engineeringolfaction

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Biological systems demonstrate superior sensing, reaction times, and energy efficiency compared to current machines.
  • Neuromorphic engineering aims to replicate these biological advantages in artificial systems.
  • Existing systems often fall short in real-time operation, power consumption, and resilience to real-world conditions.

Purpose of the Study:

  • Advocate for a shift in neuromorphic system evaluation.
  • Promote the use of closed-loop, real-world task benchmarks.
  • Drive the development of more robust and intelligent neuromorphic systems.

Main Methods:

  • Critique of current neuromorphic benchmarking practices, which rely heavily on static datasets.
  • Highlighting the limitations of sensing accuracy as a primary performance metric.
  • Proposing the adoption of dynamic, real-world tasks for evaluation.

Main Results:

  • Sensing accuracy is an insufficient proxy for true system performance in decision-making.
  • Static datasets impede the study of crucial closed-loop sensing and control strategies.
  • Current benchmarks do not adequately prepare systems for real-world complexities.

Conclusions:

  • A renewed focus on closed-loop benchmarks using real-world tasks is essential for advancing neuromorphic intelligence.
  • Dynamic, real-world benchmarking will foster the creation of more resilient and robust AI.
  • This paradigm shift is critical for developing truly intelligent autonomous systems.