Improved Volterra Kernel Methods with Applications to the Visual System

TL;DR

This paper enhances Volterra kernel analysis methods for complex systems, especially the visual system, by introducing improved input coding techniques that increase interpretability and accurately quantify multi-input interactions.

Contribution

It proposes a novel input coding approach and kernel grouping strategy that improve interpretability of Volterra kernels in multi-input non-linear systems.

Findings

Improved kernel interpretability with non-standard binary analysis.

Effective quantification of intra- and inter-input interactions.

Application demonstrated on Visual Evoked Potential data.

Abstract

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.