Abstract:
Our lab studies neural computation and coding in the retina.
Using a multi-electrode array, we can record simultaneously from up to
50 ganglion cells, the output neurons of the retina, while stimulating
with varied visual images generated on a computer monitor. We find that
nearby ganglion cells have spatial receptive fields that overlap
significantly, leading to correlated firing and redundancy in the visual
information that the cells encode. Although the strength of
correlations among pairs of cells is weak (~10%), the effect in larger
populations is dramatic: patterns of spiking and silence in groups of
just 10 cells can occur with a proability ~100,000-fold different from
that predicted from statistical independence. We show that these strong
network correlations can be explained by a model that includes all
pairwise correlations, but no higher-order statistics. This model is
identical to the Ising model from statistical physics, and predicts that
larger populations may exhibit a form of freezing transition that allows
for robust error correction. We have begun to explore these
error-correcting properties in simple visual discrimination tasks using
large populations of ganglion cells.