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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi.

Srijanie Dey1, Alexander Dimitrov1

  • 1Department of Mathematics, Washington State University, Vancouver, WA, United States.

Frontiers in Neuroinformatics
|February 2, 2023
PubMed
Summary

Intel

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Neuromorphic hardware emulates brain structure for advanced computation.
  • Spiking Neural Networks (SNNs) mimic biological neurons.
  • Validating neuromorphic models against conventional systems is crucial.

Purpose of the Study:

  • Establish numerical foundations for comparing neuromorphic and conventional computing platforms.
  • Validate Intel's Loihi chip performance using Leaky Integrate and Fire (LIF) models.
  • Assess the scalability and performance of neuromorphic hardware.

Main Methods:

  • Utilized Intel's Loihi neuromorphic chip, based on Spiking Neural Networks (SNNs).
  • Modeled Leaky Integrate and Fire (LIF) neurons from the mouse primary visual cortex.
Keywords:
LIF modelsneural simulationsneuromorphic computingperformance analysisvalidation

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  • Validated neuromorphic simulations against classical hardware implementations.

    • Main Results:

    • Loihi demonstrated efficient and precise replication of classical simulations.
    • Neuromorphic hardware showed excellent scalability with increasing network size.
    • Runtime performance scaled favorably as simulated networks grew larger.

    Conclusions:

    • Neuromorphic platforms like Loihi offer efficient and precise computational acceleration.
    • Loihi exhibits strong scalability, making it suitable for large-scale neuroscience and AI research.
    • This work provides a framework for comparing neuromorphic and conventional computing.