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System identification of point-process neural systems using probability based Volterra kernels.

Roman A Sandler1, Samuel A Deadwyler2, Robert E Hampson2

  • 1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.

Journal of Neuroscience Methods
|December 6, 2014
PubMed
Summary
This summary is machine-generated.

A new Probability Based Volterra (PBV) kernel method accurately quantifies nonlinear neural transformations. This novel approach offers robust, intuitive characterization of point-process systems for neuroscience and prosthetics.

Keywords:
Nonlinear modelingRodent hippocampusSchaffer collateralShort-term potentiationSystem identificationVolterra modeling

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neural information processing relies on nonlinear transformations of neuronal spike trains.
  • Quantifying these transformations is crucial for understanding neural physiology and developing neural prosthetics.

Purpose of the Study:

  • To develop a novel method for estimating Volterra kernels in point-process systems.
  • To characterize nonlinear input-output transformations in neural systems.

Main Methods:

  • Developed Probability Based Volterra (PBV) kernels using elementary probability theory.
  • PBV kernels describe output spike probability based on input spike history.
  • Compared PBV kernels with Least Squares Estimation (LSE) and Laguerre Expansion Technique (LET).

Main Results:

  • PBV kernels provided excellent predictive results for synthetic and rodent hippocampus data (CA3/CA1 regions).
  • PBV kernels demonstrated robustness to noise and good convergence/overfitting properties.
  • PBV kernels effectively handled correlated point-process inputs and outperformed LSE.

Conclusions:

  • PBV kernels offer a novel and intuitive method for characterizing point-process input-output nonlinear systems.
  • The method provides accurate quantification of neural transformations.