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Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.

Daniel Brüderle1, Eric Müller, Andrew Davison

  • 1Kirchhoff Institute for Physics, University of Heidelberg Heidelberg, Germany.

Frontiers in Neuroinformatics
|June 30, 2009
PubMed
Summary
This summary is machine-generated.

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Neuromorphic hardware offers scalable neuroscience modeling. A new software concept enables unified experiment descriptions for both hardware and software simulators, bridging a gap in the research community.

Area of Science:

  • Computational Neuroscience
  • Neuromorphic Engineering

Background:

  • Neuromorphic hardware systems offer scalable, parallel processing for neural computation.
  • A disconnect exists between neuromorphic engineering and neuroscience software simulation communities.

Purpose of the Study:

  • To present a software concept for integrating neuromorphic hardware into neuroscience modeling workflows.
  • To enable unified experiment descriptions for cross-platform compatibility and result comparison.

Main Methods:

  • Developed a simulator-independent language integrating hardware interfaces.
  • Implemented the concept on an accelerated neuromorphic hardware device.
  • Demonstrated a comparative setup using both hardware and software simulators.

Main Results:

Keywords:
PyNNPythonVLSIcomputationalhardwaremodelingneuromorphicneurosciencesoftware

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  • Achieved experiment portability across different simulation platforms.
  • Facilitated quantitative comparison between neuromorphic hardware and software simulator results.
  • Showcased a practical implementation and example results.

Conclusions:

  • The proposed software concept bridges the gap between neuromorphic hardware and neuroscience simulation.
  • This approach enhances the utility of neuromorphic devices as valuable neuroscience modeling tools.
  • Enables simplified, quantitative comparisons essential for advancing computational neuroscience.