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Alexander Russell1, Kevin Mazurek, Stefan Mihalaş

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Automating silicon neuron parameter configuration is crucial for tasks like neuroprosthetics. A distance-based method offers faster optimization than Maximum Likelihood for leaky integrate-and-fire silicon neurons.

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Area of Science:

  • Computational neuroscience
  • Neuromorphic engineering

Background:

  • Spiking neuron models are essential for understanding neural behavior and developing neuroprosthetics.
  • Parameter estimation for single neuron models is critical but challenging, especially for silicon implementations due to fabrication imperfections.

Purpose of the Study:

  • To develop and compare automated methods for configuring silicon neuron parameters.
  • To address the complexities of parameter tuning in hardware neuron models.

Main Methods:

  • Application of a Maximum Likelihood method to a leaky integrate-and-fire silicon neuron with spike-induced currents.
  • Utilization of a distance-based method approximating the negative log-likelihood of the lognormal distribution for parameter tuning.

Main Results:

  • Both Maximum Likelihood and distance-based methods were applied to automate silicon neuron parameter configuration.
  • The distance-based method demonstrated superior optimization speed compared to the Maximum Likelihood method.

Conclusions:

  • Automated parameter configuration is vital for silicon neuron models.
  • The distance-based method is more suitable for parameter configuration of silicon neurons due to its faster optimization speed.