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Related Experiment Video

Updated: May 7, 2026

Synaptic Microcircuit Modeling with 3D Cocultures of Astrocytes and Neurons from Human Pluripotent Stem Cells
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A digital neuromorphic system for working memory based on spiking neuron-astrocyte network.

Roghayeh Aghazadeh1, Nima Salimi-Nezhad2, Fatemeh Arezoomand3

  • 1Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Neural Networks : the Official Journal of the International Neural Network Society
|December 2, 2024
PubMed
Summary

This study introduces a novel neuromorphic system for real-time working memory (WM) emulation using spiking neuron-astrocyte networks on an FPGA. The system demonstrates robust multi-item memory formation and noise resilience, crucial for brain-inspired computing.

Keywords:
AstrocyteDigital designNeuromorphic systemSpiking neuronWorking memory

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Area of Science:

  • Neuroscience
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Working memory (WM) is vital for cognitive functions like reasoning and decision-making.
  • Neuromorphic computing offers a brain-mimicking approach to understanding and emulating WM.

Purpose of the Study:

  • To propose and implement a digital neuromorphic system for real-time WM processes.
  • To utilize a spiking neuron-astrocyte network (SNAN) for brain-like memory emulation.

Main Methods:

  • Hardware implementation of SNAN on a Field Programmable Gate Array (FPGA).
  • Employed optimization techniques: piecewise linear approximation, double buffering, and time multiplexing.
  • Evaluated multi-item memory formation capacity and noise resilience.

Main Results:

  • The time interval between storing and recalling information critically impacts retrieval performance.
  • The SNAN-based neuromorphic system exhibits resilience to noise.
  • Hardware optimizations successfully minimized area and power consumption.

Conclusions:

  • The proposed neuromorphic system effectively emulates WM processes in real-time.
  • The modular design allows for scalability to larger networks and real-world applications.
  • This work advances brain-inspired computing for cognitive functions.