Nonlinear reverse correlation with synthesized naturalistic noise

Reverse-correlation is the most widely used method for mapping receptive fields of early visual neurons. Wiener kernels of the neurons are calculated by cross-correlating the neuronal responses with a Gaussian white noise stimulus. However, Gaussian white noise is an inefficient stimulus for driving higher-level visual neurons. We show that if the stimulus is synthesized by a linear generative model such that its statistics approximate that of natural images, a simple solution for the kernels can be derived.

[1]  A. Hyvärinen,et al.  A multi-layer sparse coding network learns contour coding from natural images , 2002, Vision Research.

[2]  J. Hegdé,et al.  Selectivity for Complex Shapes in Primate Visual Area V2 , 2000, The Journal of Neuroscience.

[3]  D. C. Essen,et al.  Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.

[4]  O. L. Zangwill,et al.  Current problems in animal behaviour , 1962 .

[5]  J. DiCarlo,et al.  Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey , 1998, The Journal of Neuroscience.

[6]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[7]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[8]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[9]  Y. W. Lee,et al.  Measurement of the Wiener Kernels of a Non-linear System by Cross-correlation† , 1965 .

[10]  L. Palmer,et al.  The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells , 1990, Vision Research.

[11]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[12]  Bruno A. Olshausen,et al.  Sparse Coding Of Time-Varying Natural Images , 2010 .

[13]  Robert Shapley,et al.  Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. , 2002, Journal of vision.

[14]  Vasilis Z. Marmarelis,et al.  Advanced Methods of Physiological System Modeling , 1989 .

[15]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[16]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[17]  K. Naka,et al.  Identification of multi-input biological systems. , 1974, IEEE transactions on bio-medical engineering.

[18]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[19]  K. Sen,et al.  Spectral-temporal Receptive Fields of Nonlinear Auditory Neurons Obtained Using Natural Sounds , 2022 .

[20]  Stanley A. Klein,et al.  Nonlinear systems analysis with non-Gaussian white stimuli; General basis functionals and kernels (Corresp.) , 1979, IEEE Trans. Inf. Theory.

[21]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[22]  J. Touryan,et al.  Isolation of Relevant Visual Features from Random Stimuli for Cortical Complex Cells , 2002, The Journal of Neuroscience.