A parametric model for synthesis of cortical column patterns

Abstract

Summary form only given. The authors introduce a parametric model for columnar structure which considers the spatial form in an image-processing framework. This method permits easy synthesis of column-like structure from noise images. In particular, bandpass filtering of noise images followed by thresholding yields patterns which strongly resemble the columnar structure that has been observed in the brain. The image-oriented technique is flexible and inexpensive to compute. There are only a few independent parameters, and the role they play in column formation is apparent. The parameters for a particular column system can be readily determined from actual brain data by the use of standard image-processing techniques. The authors have used the model to process data obtained in their computer reconstruction of the pattern of ocular dominance columns in the macaque monkey. This approach avoids the necessity of constructing computationally expensive cellular models which are based on poorly understood details of neural development. The authors provide an efficient, accurate model which can be adjusted to fit a wide variety of column data.<>