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Using Dynamical Systems to
Model Human Heading
Perception
Oliver Layton, PhD
How do humans autonomously navigate about the ever changing world? During
navigation, the brain must continuously adapt to varying environmental conditions. For
example, an independently moving object may require the human observer to adjust
the intended path of travel to prevent collision. A successful model that describes how
humans perceive where they are heading should then fundamentally address how the
temporal unfolding of new visual information impacts perception. Despite the
abundance of approaches to mathematically model the primate brain, few
simultaneously incorporate temporal dynamics, make mechanistic predictions about
how populations of neurons interact to give rise to behavior, link the activity of neurons
to our perceptual experience, and quantatively simulate cell recordings from
neurophysiology. I will discuss a dynamical systems approach to neural modeling that
considers navigation, and other behavioral and perceptual phenomeona, as emergent
outcomes of dynamical interactions between populations of neurons in different areas
of the primate brain. I will first descibe how dynamical systems can provide mechanistic
insight on a well-known visual illusion, and then show how I have used this approach to
model human heading perception in the presence of independently moving objects.
The model proposes the neural mechanisms that underlie why humans systematically
mispercieve their heading direction when a moving object crosses their path.
3PM, WEDNESDAY, OCTOBER 22, 2014
Carlson Auditorium, Center for Imaging Science (CAR)