Next
Previous
Up
Ruling out and ruling in neural codes
Sheila Nirenberg
Weill Medical College of Cornell University
The subject of neural coding has generated much debate. A key issue is
whether the nervous system uses coarse or fine coding. Each has
different strengths and weaknesses and, therefore, different
implications for how the brain computes. For example, the strength
of coarse coding is that it's robust to fluctuations in spike arrival
times. Downstream neurons don't have to keep track of spike train
structure. The weakness, though, is that individual cells can't
carry much information, so downstream neurons have to be able to pool
signals across cells and/or time to obtain sufficient information.
With fine coding, individual cells can carry a great deal of
information, but downstream neurons have to be able to resolve spike
train details to obtain it. Here we set up a strategy to determine
what the neural code can and can't be and used it at the level of the
retina. We recorded from essentially all the retinal output cells an
animal uses to solve a task, evaluated the cells' spike trains for as
long as the animal evaluates them, and used optimal, i.e., Bayesian,
decoding. This makes it possible to obtain an upper bound on the
performance of codes and thus eliminate those that aren't viable. Our
results show that standard coarse coding is insufficient; finer,
more information-rich codes are necessary.