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From Solid to Fluid: Novel Approaches in Neuromorphic Engineering.

Daniil Nikitin1, Hynek Biederman1, Andrei Choukourov1

  • 1Department of Macromolecular Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2 180 00, Prague, Czech Republic.

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Summary

Neuromorphic engineering uses memristors to mimic brain processes, overcoming slow, energy-inefficient computing. Emerging liquid-state and nanofluid systems offer advanced, multi-dimensional computing capabilities.

Keywords:
Neuromorphic engineeringconductive filamentmemristornanofluid.nanoparticlenanowireresistive switching

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Science

Background:

  • Neuromorphic engineering mimics brain functions using artificial memristors, which exhibit resistive switching.
  • Memristor-based systems offer a solution to the limitations of conventional computing, such as slow speed and high energy consumption.
  • Traditional memristor arrays struggle to replicate the multi-dimensionality of biological neural networks.

Purpose of the Study:

  • To review the principles, history, and applications of memristor-based neuromorphic systems.
  • To explore advanced neuromorphic architectures, including multi-dimensional nanowire/nanoparticle systems and liquid-state devices.
  • To highlight the potential of nanofluids in creating reconfigurable memristive networks.

Main Methods:

  • Review of fundamental research on resistive switching and memristor operation.
  • Analysis of unconventional neuromorphic systems like nanowire, nanoparticle, and liquid-based devices.
  • Exploration of nanofluid applications for memristive nanoparticle networks.

Main Results:

  • Memristors demonstrate resistive switching, enabling brain-like computing.
  • Nanowire and nanoparticle systems show potential for reservoir computing with neuron-like spiking behavior.
  • Liquid-state memristors and nanofluids offer novel pathways for mimicking biological information transmittance and creating reconfigurable networks.

Conclusions:

  • Neuromorphic engineering with memristors promises more efficient and advanced computing.
  • Multi-dimensional and liquid-state approaches are crucial for replicating biological neural network complexity.
  • Nanofluids represent a promising frontier for next-generation reconfigurable neuromorphic hardware.