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Michael Krumin1, Avner Shimron1, Shy Shoham2

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Area of Science:

  • Computational Neuroscience
  • Neural Encoding Models
  • Spike Train Analysis

Background:

  • Linear-Nonlinear-Poisson (LNP) models are widely used for single neuron stimulus-response characterization.
  • Growing interest exists in understanding higher-order spike train correlations and their relation to neural encoding.
  • Traditional methods like reverse-correlation and maximum-likelihood require simultaneous stimulus-response pairs.

Purpose of the Study:

  • To propose and develop a computational method for identifying LNP encoding models based on correlation transformations.
  • To identify single-neuron temporal kernels from second-order statistics of neural activity.
  • To enable neural model estimation without direct observation of stimulus-response pairs.

Main Methods:

  • Developed a correlation-distortion based identification method.
  • Applied the method to identify minimum-phase single-neuron temporal kernels under Gaussian excitation (white and colored).
  • Utilized second-order statistics of stimulus and response, rather than direct pairs.

Main Results:

  • The proposed method successfully identifies neural temporal kernels from correlation transformations.
  • This approach provides excellent kernel estimates for various parametric neural models in practice.
  • The method does not require simultaneous stimulus-response observations, relying only on second-order statistics.

Conclusions:

  • LNP encoding models can be identified from the correlation transformations they induce.
  • This correlation-distortion approach offers a viable alternative to traditional identification methods.
  • The method broadens the applicability of neural model estimation across diverse experimental conditions and systems.