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A method for constructing data-based models of spiking neurons using a dynamic linear-static nonlinear cascade

M G Paulin1

  • 1Department of Zoology, University of Otago, Dunedin, New Zealand.

Biological Cybernetics
|January 1, 1993
PubMed
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This study introduces a novel nonlinear dynamical model for neural system identification. The developed algorithm accurately estimates neural system parameters from spike train data, even with limited samples.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Dynamical Systems Theory

Background:

  • Accurate neural system identification is crucial for understanding brain function.
  • Existing models often struggle with the complex nonlinear dynamics inherent in neuronal behavior.
  • Developing flexible yet tractable models for neural data analysis remains a challenge.

Purpose of the Study:

  • To present a general nonlinear dynamical model for neural system identification.
  • To introduce an efficient algorithm for fitting this model to spike train data.
  • To evaluate the algorithm's performance in identifying simulated neuronal structures and parameters.

Main Methods:

  • A Wiener-Bose dynamic nonlinearity forms the core of the model, enabling approximation of arbitrary nonlinear dynamical systems.

Related Experiment Videos

  • Spike generation and transmission nonlinearities are handled via cascade connections with pulse frequency modulators/demodulators.
  • The output static nonlinearity is decomposed into a rectifier and multinomial for simplified yet general representation.

    • Main Results:

    • The model effectively approximates arbitrary nonlinear dynamical systems.
    • The identification algorithm accurately estimates the structure and parameters of simulated neuron-like systems.
    • Successful identification was achieved using datasets with as few as a few hundred spikes.

    Conclusions:

    • The proposed model offers a generalized and realistic approach to connectionist "neurons".
    • The model is computationally efficient and amenable to standard dynamical systems analysis.
    • The algorithm demonstrates high accuracy in neural system identification from spike train data.