Improved Volterra Kernel Methods with Applications to the Visual System

Details Improved Volterra Kernel Methods with Applications to the Visual System Improved Volterra Kernel Methods with Applications to the Visual System

2008-12-05

arxiv.org/abs/0812.1062v1

Biology

Quantitative Biology

Quantitative Methods

Scientific paper

Volterra analysis and its variants have long been prominent among methods for modeling multi-input non-linear systems. The product of Volterra analysis, the Volterra kernels, are particularly suited to quantifying intra- and inter-input interactions. They are also readily interpretable, which means that they can be related directly to physical behaviors, and more distantly, to the underlying processing mechanisms of the system being tested. However, accurate estimation of a sufficient set of classical kernels is often not possible for complex systems because the number of kernels that need to be determined, and hence experiment time, increases radically with system memory, response frequency bandwidth, and non-linear interaction order. Practical approaches to kernel estimation often involve forced reductions of the generality of the analysis that in turn compromise interpretability. Here we illustrate the effects on kernel interpretability of two common reductions, slow-stimulation and the use of binary inputs, using both numerical simulations and data from a Visual Evoked Potential experiment. We show how a non-standard version of binary analysis, involving a different coding of the inputs and the use of particular groupings of kernel slices, improves kernel interpretability. We bring together, in a comprehensive fashion, all of the mathematical considerations needed to apply and interpret the results of Volterra kernel analyses. The input-coding method we describe allows one to correctly quantify multi-input interactive effects that occur both within the separate input channels and across them.

Affiliated with

Also associated with

No associations

Rating

Improved Volterra Kernel Methods with Applications to the Visual System does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Improved Volterra Kernel Methods with Applications to the Visual System, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Improved Volterra Kernel Methods with Applications to the Visual System will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-364977