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Estimation of spatiotemporal neural activity using radial basis function networks

R W Anderson1, S Das, E L Keller

  • 1Smith-Kettlewell Eye Research Institute, San Francisco, CA 94115, USA.

Journal of Computational Neuroscience
|January 7, 1999
PubMed
Summary

This study introduces a novel method using radial basis function (RBF) networks to map neural population activity from single-neuron recordings, aiding neuroscience research into brain function.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Estimating population activity in neural structures is crucial for understanding brain function.
  • Single-neuron data is often unevenly sampled and variable, making accurate estimation challenging.
  • Buildup neurons in the superior colliculus present specific difficulties due to their discharge patterns.

Purpose of the Study:

  • To develop a robust method for estimating the time evolution of population activity in topologically organized neural structures.
  • To address the challenges posed by unevenly sampled and variable single-neuron data.
  • To enable accurate mapping of neural activity for systems-level neuroscience insights.

Main Methods:

  • Utilized radial basis function (RBF) networks for surface estimation.

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  • Developed computational-geometry-based algorithms to regularize neural data.
  • Applied the method to estimate spatiotemporal movement fields of buildup neurons in the superior colliculus.

    • Main Results:

    • Successfully estimated dynamic movement fields for buildup neurons in two spatial dimensions.
    • Developed algorithms to handle data regularization for RBF network surface estimation.
    • Extended the method to estimate simultaneous spatiotemporal activity across the superior colliculus during movements.

    Conclusions:

    • The RBF network method provides a generalizable approach for estimating neural population activity.
    • This methodology can be applied to other regularly organized neural structures with sufficient sampling.
    • The findings advance the ability to infer systems-level brain function from neural recordings.