Inside Salk - October 2009 - page 12

I understand you first studied the properties of electrons.
What influenced you to turn your attention to neurons?
The theoretical ideas are similar even though the physical systems are
very different. And the closest application between statistical physics
and information theory is neuroscience. When I was a little girl, I wanted
to be a doctor. But I also greatly admiredmy grandfather, who was a
mathematician/physicist. Working in theoretical and computational
biology, in particular neuroscience, provides a way to be connected to
the two fields. After completing a thesis in theoretical physics, I joined
the Sloan-Swartz program duringmy postdoctoral studies that provides
biology training to physicists.
What fascinates you about the brain?
It’s remarkably efficient. For example, my desktop computer uses 60
watts of power and some estimate that the brain uses just 12watts. Yet
the brain is very good at doing analog computations that are very hard
mathematical problems such as forming amap of surrounding events
that come in through our visual system. So for example, your eyesmove
three times per second and each time you have a pinhole presentation
and yet that’s not what we perceive. We perceive a whole continuous
field. The algorithms for doing this do not exist, except in the brain and
we have yet to find out what they are.
You developed amethod for analyzing the brain cells’
response to natural visual stimuli. What type of stimuli
do you use in your experiments and how is it different from
standard practice?
We use a collection of scenes that I took while walking through the
woods with a video camera. The reason for using such natural stimuli is
that they elicit good responses from high-level neurons whose job is to
integrate incoming visual information. When such neurons are presented
with simplified stimuli devoid of objects, they respond poorly, sometimes
not at all. Our hope is that, althoughwe do not know before the experi-
ments which combination of visual features will drive a particular neuron,
by taking scenes from the visual world there will be some features of
interest to any visual neuron. A collaborative project with Salk professor
' laboratory was recently funded by NEI to both develop
new statistical methods and use them to analyze responses of high-level
visual neurons.
So if you only takemeasurements from just one neuron at
a time, how do you knowwhat part of the scene caused it
to fire?How do you know it wasn’t a shape, or a color that
trigged the response?
That’s why people were shying away from using natural scenes: they are
so complex that when you get a spike, you didn’t know what actually
triggered it. Although you can’t make a determination from just one
Inside SalkOctober 2009
Disorders of the nervous system are devastating.
Understanding details of communicationwith
spikes is a pre-requisite for developing better cures.
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