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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 Neuroscience
|June 17, 2022
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Summary

Intel

Area of Science:

  • Neuromorphic engineering
  • Computational neuroscience
  • Artificial intelligence

Background:

  • Neuromorphic hardware emulates brain structure for computational acceleration.
  • Spiking Neural Networks (SNNs) mimic biological neurons.
  • Validation of neuromorphic models against conventional platforms is crucial.

Purpose of the Study:

  • To establish numerical foundations for comparing neuromorphic and conventional computing platforms.
  • To validate Intel's Loihi chip against classical simulations using Leaky Integrate and Fire (LIF) models.
  • To assess the scalability and performance of neuromorphic hardware.

Main Methods:

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

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  • Validated neuromorphic simulations against classical hardware simulations.

    • Main Results:

    • Loihi demonstrated efficient and high-precision replication of classical simulations.
    • The neuromorphic platform showed excellent scalability with increasing network size.
    • Runtime performance scaled favorably as simulated networks grew larger.

    Conclusions:

    • Neuromorphic hardware, exemplified by Loihi, offers efficient and precise emulation of neural models.
    • Loihi presents a scalable platform for neuroscience and AI research.
    • This work provides a framework for comparing neuromorphic and conventional computational approaches.