In recent years, advances in technology have made simultaneous recording of neuronal ensembles possible, leading to many exciting developments in the understanding of how the brain performs neural computations. These breakthroughs have depended on the development of a number of techniques for extracting information from distributed representations. One of the key techniques has been the development of reconstruction methods, by which one attempts to calculate a behavioral or stimulus variable from neural firing patterns. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. Because neuronal representations are highly distributed, cells could be firing in a manner that is generally consistent or generally inconsistent with the reconstructed value. There is no way, using current reconstruction techniques, to determine whether the representation is robust or not. We have developed a measurement (coherency) which provides just such a detection method. This measurement can detect dynamic events (such as the resolution of ambiguity). Experimental aim: To determine whether the coherency of hippocampal ensembles differs between tasks. Computational aim: To improve the coherency measurement by increasing its temporal resolution and by deriving quantitative statistics so that it can be used to directly measure differences in representation. As more and more experiments are done with neural ensembles, novel methods are going to be needed to analyze and understand those ensembles. The coherency measurement enables the determination of how well those neural ensembles represent the values we believe they represent. Refining the coherency measurement will provide the neuroscience community with a useful tool for analysis of those neural ensembles. ? ?
