Topic: pattern variation

Understanding pattern variation and establishing equivalence between patterns is important to many areas of neuroscience and is made significantly more complex when the full extent of the pattern space is unknown or where it is infinite. Current studies of such patterns therefore often rely on the comparison of low dimensional pattern features to simplify comparison. We present a more comprehensive approach to pattern comparison based on statistical ensembles of circular pattern patches. We apply this approach to orientation preference maps for which previous feature-based comparisons supporte