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Stability of point process spiking neuron models.

Yu Chen1, Qi Xin2, Valérie Ventura3

  • 1Carnegie Mellon University, Pittsburgh, PA, 15213, USA. yuc2@andrew.cmu.edu.

Journal of Computational Neuroscience
|September 16, 2018
PubMed
Summary
This summary is machine-generated.

Point process regression models can exhibit instability, generating unrealistic spike trains. Our analysis improves instability diagnostics and shows that models with multiple filters generally avoid this issue, though careful data fitting is essential.

Keywords:
Generalized linear modelOutlier trialsPoint process regressionSpike train

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Area of Science:

  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Point process regression models, utilizing generalized linear model (GLM) technology, are standard for spike train analysis.
  • A recent study identified instability issues in these models, leading to simulated spike trains with explosive firing rates.

Purpose of the Study:

  • To analyze and address the instability problem in point process regression models for spike train analysis.
  • To improve existing instability diagnostics and extend them to a broader range of models.
  • To investigate the impact of filter structures (single vs. multiple filters) on model stability.

Main Methods:

  • Extending the instability analysis methods proposed by Gerhard et al.
  • Developing an improved instability diagnostic applicable to a wider class of models.
  • Comparing single-filter models (Pillow et al., 2008) with multiple-filter models (Kass and Ventura, 2001).
  • Re-analyzing existing datasets and introducing new datasets exhibiting bursting behavior.
  • Simulating spike trains using the Izhikevich model for various ground truth scenarios.

Main Results:

  • Instability in point process models can often be linked to model lack of fit.
  • Models employing multiple filters to represent prior spike effects demonstrate a tendency to avoid instability compared to single-filter models.
  • No universal rules guarantee stability; careful data fitting and model assessment are crucial.

Conclusions:

  • While multiple-filter models offer improved stability in spike train analysis, they do not eliminate the need for rigorous data fitting and validation.
  • Model assessment must be tailored to the specific dataset and research question.
  • Understanding and mitigating instability are critical for reliable spike train modeling.