Neurocomputing 44–46 (2002) 515–520 www.elsevier.com/locate/neucom Quantitative analysis of kernel properties in Kohonen’s self-organizing map algorithm: Gaussian and dierence of Gaussians neighborhoods  Mimi Liljeholm, Andy Lin, Piotr Ozdzynski, Jackson Beatty ∗ Department of Psychology and Brain Research Institute, University of California Los Angeles, Los Angeles, CA 90095-1563, USA Abstract Recent experimental evidence suggests that processes of plastic reconguration of the primate cerebral cortex may involve an inhibitory component. Here, we modify Kohonen’s self-organizing map model of the cortex to include surround inhibition in its adaptation kernel. This addition not only improves the accuracy of the cortical representation, as measured by quantization error, it also tends to produce pinwheel patterns, similar to those observed in primary visual cortex. c  2002 Elsevier Science B.V. All rights reserved. Keywords: Neuronal empiricism hypothesis; Cerebral cortex; Self-organizing map 1. Introduction In decades following the proliferation of cortical microelectrode recording studies in the 1960s, neuroscience has accumulated a detailed catalog of cellular properties and organization for dierent cortical regions [7]. For example, primary visual cortex is characterized by a preponderance of neurons with linear receptive eld properties [4], that are organized locally into orientation-specic pinwheels [3], and globally assembled into a single retinotopic map [10]. Thus, it was of major importance to learn that all  This project is supported by the Human Brain Project under NIMH Grant 1K07MH01953, NIDOCD Grant DC=8424559, and NSF Grant MH=8429337. * Corresponding author. E-mail address: beatty@ucla.edu (J. Beatty). 0925-2312/02/$-see front matter c  2002 Elsevier Science B.V. All rights reserved. PII:S0925-2312(02)00410-1