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Complex Oxides for Brain-Inspired Computing: A Review.

Tae Joon Park1, Sunbin Deng1, Sukriti Manna2

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Summary
This summary is machine-generated.

Complex oxides offer new pathways for artificial intelligence (AI) and neuromorphic computing by mimicking natural intelligence. These materials enable the development of artificial neurons and synapses for advanced learning machines.

Keywords:
complex oxidesneural networksneuromorphic computingquantum materialssynapses

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Artificial intelligence (AI) and neuromorphic computing aim to replicate natural intelligence in machines.
  • Complex oxides are tunable ceramic materials with potential for brain-inspired electronics.
  • Natural intelligence spans from neural networks to collective animal behavior.

Purpose of the Study:

  • To review the opportunities complex oxides present for AI and neuromorphic computing.
  • To connect natural intelligence principles with material properties for artificial systems.
  • To highlight recent advancements in artificial neurons, synapses, and circuits.

Main Methods:

  • Discussion of natural intelligence at neural and colony levels.
  • Analysis of collective phenomena in complex oxides (e.g., MITs, ferroelectricity).
  • Review of theoretical (first-principles) and experimental (synchrotron spectroscopy) techniques.

Main Results:

  • Demonstrations of artificial neurons, synapses, and circuits using complex oxides.
  • Insights into implementing experimental material characteristics into neural networks and algorithms.
  • Identification of key material challenges for advancing neuromorphic computing.

Conclusions:

  • Complex oxides are promising for developing sophisticated AI and neuromorphic computing systems.
  • Understanding microscopic physical mechanisms in these materials is crucial for future progress.
  • Bridging materials science with AI design is essential for next-generation intelligent machines.