Extracting wave structure from biological data with application to responses in turtle visual cortex.

Abstract

Waves have long been thought to be a fundamental mechanism for communicating information within a medium and are widely observed in biological systems. However, a quantitative analysis of biological waves is confounded by the variability and complexity of the response. This paper proposes a robust technique for extracting wave structure from experimental data by calculating "wave subspaces" from the KL decomposition of the data set. If a wave subspace contains a substantial portion of the data set energy during a particular time interval, one can deduce the structure of the wave and potentially isolate its information content. This paper uses the wave subspace technique to extract and compare wave structure in data from three different preparations of the turtle visual cortex. The paper demonstrates that wave subspace caricatures from the three cortical preparations have qualitative similarities. In the numerical model, where information about the underlying dynamics is available, wave subspace landmarks are related to activation and changes in behavior of other dynamic variables besides membrane potential.