Abstract
The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the visual cortex. Due to the importance of its role, modeling the retina to advance in interpreting its spiking activity output is a well studied problem. In particular, it has been shown that latent variable models can be used to model the joint distribution of Retinal Ganglion Cells (RGCs). In this work, we validate the applicability of Restricted Boltzmann Machines to model the spiking activity responses of a large a population of RGCs recorded with high-resolution electrode arrays. In particular, we show that latent variables can encode modes in the RGC activity distribution that are closely related to the visual stimuli. In contrast to previous work, we further validate our findings by comparing results associated with recordings from retinas under normal and altered encoding conditions obtained by pharmacological manipulation. In these conditions, we observe that the model reflects well-known physiological behaviors of the retina. Finally, we show that we can also discover temporal patterns, associated with distinct dynamics of the stimuli.
Highlights
Retinal Ganglion Cells (RGCs) play a key role in the visual system, being the output of the retina circuit and the connection between the retina and the areas of the brain dedicated to the high-level processing of the visual stimuli
We modeled the joint distribution of the firing rates with mean-covariance Restricted Boltzmann M achines5,6, and—to detect temporal patterns—with conditional Restricted Boltzmann Machines[7]
In Experiment 1, the population activity related to grating stimulation is modeled with mcRBMs5,6, to assess whether this class of models can retrieve modes associated with visual features
Summary
Retinal Ganglion Cells (RGCs) play a key role in the visual system, being the output of the retina circuit and the connection between the retina and the areas of the brain dedicated to the high-level processing of the visual stimuli. The ability of the model to learn regularities in the data decreases after having increased the blockade of GABA receptors This is because pharmacologically induced impairment leads to an unreliable population code. We have chosen to use a series of natural images representing a movement of the visual field over a brick wall Even with such more complex stimuli, the models detected stimuli-associated modes. The possibility of retrieving temporal patterns related to different dynamics of the stimuli is investigated, by modeling the RGC joint distribution We address this task by modeling the RGC population activity with a specific type of RBM (the conditional RBM7), which allows to model the history of the input
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