Analyzing neural responses to more natural stimuli
William Bialek
Most experiments on neurons are done with simplifed stimuli that are chosen
from low dimensional parameter spaces. This approach has the
attraction, that one can plot some measure of neural
response against the few parameters, and of course much has been
learned this way. If we leave low dimensional stimuli behind in
favor of natural signals, what can we measure?
One answer is that we can measure information transmission.
I will summarize methods that allow us to measure mutual information
between spike trains and stimuli for aribtrarily complex stimuli,
provided only that we can repeat the stimuli many times.
We can also measure the information carried by single spikes,
bursts, coincident firing of cell pairs, ... , all in a model
independent manner. These results provide a benchmark against
which our understanding of the neural code can be tested.
We want to know not just how much the spike train is telling us, but
also WHAT it is telling us. Our intuition is that single
cells are selective for features, and I will discuss a method
for identifying candidate 'feature dimensions;' if there are just a few of
these interesting dimensions then we can map out a fully
nonlinear input/output relation in this low dimensional space.
This is work in progress, so I don't quite know
how much I'll be able to say, but I will certainly show how these methods
have been used to reveal some striking adaptation to the statistics
of velocity signals in the motion sensitive cells of the fly.
Back to the Natural Scenes Meeting Agenda.