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Published on: March 25, 2014
Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses
Dong Song1, Rosa H M Chan, Vasilis Z Marmarelis
1Department of Biomedical Engineering, Program in Neuroscience, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089, USA. dsong@usc.edu
IEEE Transactions on Bio-Medical Engineering
|June 8, 2007
Summary
Developing a neural prosthesis for the hippocampus requires modeling its nonlinear information processing. This study models hippocampal activity as a nonlinear multiple-input, multiple-output system, improving predictions of neural activity for memory restoration.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Biophysics
Background:
- The hippocampus is crucial for declarative memory formation.
- Hippocampal damage causes anterograde amnesia due to disrupted spatio-temporal activity patterns.
- Neural prosthetics require understanding nonlinear transformations of neural activity.
Purpose of the Study:
- To model the nonlinear transformation of spatio-temporal spike activity between hippocampal CA3 and CA1 regions.
- To develop a physiologically plausible model for predicting neural output based on input.
- To inform the design of neural prosthetics for memory restoration.
Main Methods:
- Formulated hippocampal CA3-CA1 signal propagation as a nonlinear multiple-input, multiple-output (MIMO) system, decomposed into multiple-input, single-output (MISO) systems.
- Modeled MISO systems using Volterra kernels (feedforward and feedback), a spike generation threshold, somatic integration, and a noise term.
- Estimated model kernels using maximum-likelihood on spike train data from rats performing a memory task, progressively increasing model complexity.
Main Results:
- Increased feedforward kernel complexity improved prediction accuracy of CA1 output spike trains from CA3 input spike trains.
- Second- and third-order nonlinear models accurately predicted output spike distribution, unlike first-order linear models.
- Self-kernels captured input nonlinearities, while cross-kernels revealed nonlinear interactions between inputs.
Conclusions:
- Nonlinear models are essential for accurately capturing hippocampal information processing between CA3 and CA1.
- The proposed MISO model provides a physiologically plausible framework for understanding and potentially restoring hippocampal function.
- This work advances the development of neural prosthetics for memory disorders by elucidating complex neural dynamics.
