ORDCF Professor of Spatial and Computational Vision, York University
Departments of Biology, Psychology, Mathematics, and Computer Science
1985-2000: Professor, Ophthalmology & Visual Science, Neurobiology & Biopsychology,
University of Chicago
Fellow, Optical Society of America
Research Interests with Selected Publications
of Form Vision (including fMRI) and Motion Perception
My experimental research in these areas is designed to elucidate
the sequence of cortical processing stages involved in the analysis of visual
form and motion. At present a main focus is to understand the various forms
of global, configural orientation pooling occurring in cortical area V4 and
contributing to the visual analysis of faces. Both our psychophysics and fMRI
in this area employ synthetic faces (see above) that are derived from a data
base of measurements on digitized photographs of many subjects. Synthetic faces
can be manipulated in a multi-dimensional metric space to generate stimuli to
test hypotheses about the transformations involved in form vision.
Kim, J. and Wilson, H. R. (1996) Direction repulsion between
components in motion transparency. Vision Res. 36, 1177-1187.
Kim, J. and Wilson, H. R. (1997) Motion integration over space:
Interaction of the center and surround motion. Vision Res. 37,
Wilson, H. R., Wilkinson, F. & Asaad, W. (1997) Concentric
orientation summation in human form vision. Vision Res., 37,
Wilkinson, F., Wilson, H. R. & Ellemberg, D. (1997) Lateral
Interactions in Peripherally-Viewed Texture Arrays. J. Opt. Soc. Am.
A, 14, 2057-2068.
Wilson, H. R. & Kim, J. (1998) Dynamics of a divisive gain
control in human vision. Vision Res. In press.
Wilkinson, F., Wilson, H. R. & Habak, C. (1998) Detection and
recognition of radial frequency patterns. Vision Res. In press.
Wilson, H. R. & Wilkinson, F. (1998) Detection of global
structure in Glass patterns: implications for form vision. Vision
Res. In press.
Wilson, H. R., Wilkinson, F., Lin, L. M., Castillo, M. (1999)
Discrimination of head orientation. Vision Res. In press.
Wilkinson, F., James, T. W., Wilson, H. R., Menon, R., Gati, J. & Goodale,
Wilson, H. R., Wilkinson, F., Lin, L. M. and Castillo, M. (2000) Perception
of head orientation. Vision Res. Vision Res. 40, 459-472.
Wilson, H. R., Loffler, G., Wilkinson, F. and Thistlethwaite, W. A. (2001)
An inverse oblique effect in human vision. Vision Res. 41, 1749-1753.
Loffler, G. and Wilson, H. R. (2001) Detecting shape deformation of moving
patterns. Vision Res. 41, 991-1006.
Wilson, H. R. & Wilkinson, F. (2002) Symmetry Perception: a novel approach
for biological shapes. Vision Res. 42, 589-597.
Wilson, H. R. & Wilkinson, F. (2002) Global visual pattern extraction.
In Handbook of Brain Theory & Neural Networks, ed. by M. A. Arbib, MIT Press:
Cambridge, MA. In press.
Wilson, H. R. & Wilkinson, F. (2002) Spatial channels in vision &
spatial pooling. In The Visual Neurosciences, ed. by L. M. Chalupa & J.
S. Werner, MIT Press: Cambridge, MA. In press.
Dynamic Neural Network
Models of Visual Function
My neural network models are designed to be consistent with the
anatomy and single unit physiology of the visual system while predicting psychophysical
data. Network models thus form the basis for relating physiology to function.
Emphasis has been on modeling the retina and cortical areas MT and V4. Our current
modeling is designed to examine hypotheses on the dynamical role of feedback
between cortical areas V4, V2, and IT and its role in pattern recognition.
Wilson, H. R., Ferrera, V. P., and Yo, C.
(1992) A psychophysically motivated model for two-dimensional motion
perception. Visual Neurosci. Visual Neurosci. 9, 79-97.
Wilson, H. R. (1993) Theories of infant
visual development. In Early Visual Development, Normal and
Abnormal, ed. by K. Simmons, Oxford University Press, New
Wilson, H. R. and Kim, J. (1994) A model
for motion coherence and transparency. Visual Neurosci. 11,
Wilson, H. R. (1994) Models of
two-dimensional motion perception. In Visual Detection of Motion,
ed. by A. T. Smith & R. J. Snowden, Academic Press, London,
Wilson, H. R. (1997) A neural model of
foveal light adaptation and afterimage formation. Visual
Neuroscience, 14, 403-423.
Wilson, H. R. (1998) Non-Fourier cortical
processes in texture, form, and motion perception. In Cerebral
Cortex, vol. 14: Models of Cortical Circuitry, ed. by P. S. Ulinski
& E. G. Jones, Plenum, New York, In press.
Wilson, H. R., Krupa, B. & Wilkinson, F. (1999)
Dynamics of a scintillating visual illusion. Submitted.
Wilson, H. R., Blake, R. & Lee, S.-H. (2001) Dynamics of travelling waves
in visual perception. Nature, 412, 907-910.
of Cortical Neurons & Interactions
My purpose in simulating individual neurons and small neural networks
has been to develop simplified mathematical descriptions that capture key elements
of the underlying nonlinear dynamics. Such simplified descriptions can be very
useful in larger network simulations. Current projects focus on interactions
in primary visual cortex that may produce the visual auras in migraine.
Wilson, H. R. and Cowan, J. D. (1972)
Excitatory and inhibitory interactions in localized populations of
model neurons. Biophysical Journal, 12, 1-24.
Wilson, H. R. and Cowan, J. D. (1973) A
mathematical theory of the functional dynamics of cortical and
thalamic nervous tissue. Kybernetik, 13, 55-80.
Wilson, H. R. (1999) Simplified dynamics of
human and mammalian neocortical neurons. J. Theoretical Biol. In
Nonlinear Dynamics in
is one central reason why all neuroscientists and biopsychologists should
be conversant with nonlinear dynamics. Nonlinear dynamics reveals and elucidates
a range of phenomena that are simply inconceivable in the more mundane world
of linear systems theory. Memory and forgetting, decision making, motor control,
action potentials, and perhaps even free will and determinism can no longer
be intelligently conceptualized without a basic understanding of nonlinear
systems. At the deepest level, therefore, my decision to write this book was
predicated on the belief that an understanding of brain function and behavior
must be grounded in a conceptual framework informed by nonlinear dynamics.
The book is accompanied
by a disk containing about 100 MatLab scripts that are intended to complement
the text MatLab was chosen because the scripts will run on the Macintosh,
UNIX, or Windows platforms.
to the book page
|Go to the CVR page
For more information and to order:
(416) 736-2100 ext. 33140
Hugh R. Wilson
ORDCF Professor of Biological & Computational Vision
Computer Science Building B002F
4700 Keele Street
Canada M3J 1P3