  The retina is a complex nervous system which encodes visual stimuli before
higher order processing occurs in the visual cortex. In this study we evaluated
whether information about the stimuli received by the retina can be retrieved
from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting
High-Density 64x64 MEA technology. To this end, we modeled the RGC population
activity using mean-covariance Restricted Boltzmann Machines, latent variable
models capable of learning the joint distribution of a set of continuous
observed random variables and a set of binary unobserved random units. The idea
was to figure out if binary latent states encode the regularities associated to
different visual stimuli, as modes in the joint distribution. We measured the
goodness of mcRBM encoding by calculating the Mutual Information between the
latent states and the stimuli shown to the retina. Results show that binary
states can encode the regularities associated to different stimuli, using both
gratings and natural scenes as stimuli. We also discovered that hidden
variables encode interesting properties of retinal activity, interpreted as
population receptive fields. We further investigated the ability of the model
to learn different modes in population activity by comparing results associated
to a retina in normal conditions and after pharmacologically blocking GABA
receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual
Information tends to decrease if we pharmacologically block receptors. We
finally stress that the computational method described in this work could
potentially be applied to any kind of neural data obtained through MEA
technology, though different techniques should be applied to interpret the
results.
