The berkeley wavelet transform: a biologically inspired orthogonal wavelet transform

Neural Computation
Ben WillmoreJack L Gallant

Abstract

We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.

References

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Citations

Aug 8, 2012·Emotion·Timothy D SweenyKen A Paller
Feb 12, 2010·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Ben D B WillmoreJack L Gallant
Jun 28, 2016·PLoS Computational Biology·Ján AntolíkThomas D Mrsic-Flogel
Jan 19, 2018·Journal of Digital Imaging·Nilesh Bhaskarrao BahadureHar Pal Thethi
Apr 24, 2019·PLoS Computational Biology·Santiago A CadenaAlexander S Ecker
Apr 27, 2019·Journal of Vision·William F KindelJoel Zylberberg
Apr 4, 2017·International Journal of Biomedical Imaging·Nilesh Bhaskarrao BahadureHar Pal Thethi
Jun 8, 2021·PLoS Computational Biology·Max F BurgAlexander S Ecker

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