Speaker
Gasper Tkacik
(IST, Austria)
Description
In most areas of the brain, information is encoded in the
correlated activity of large populations of neurons. Here we
build probabilistic models of such population codes using
maximum entropy principle from new recordings of more
than 100 retinal ganglion cells from a dense patch on the
salamander retina. We illustrate how the pairwise
maximum entropy (Ising-like) models can be extended to
capture better the experimental data. We analyze the
qualitative features of these codes and report on their
emerging critical behavior. These results can be put into
context by a theoretical examination of information-
maximizing codes for noisy spiking neurons.
