Visual encoding in rat lateral geniculate nucleus: An artificial neural network approach

Visual prostheses have recently shown success in partially restoring vision to the blind. While retinal implants are considered the most successful type of visual prostheses, other techniques are needed for patients with completely degenerated retina or injured optic nerve. Thalamic visual prostheses that target the Lateral Geniculate Nucleus (LGN) represent one promising type. However, one challenge in tuning thalamic visual prostheses is to understand how visual information is encoded in the firing of LGN neurons. In this paper, we introduce an artificial neural network visual encoding model that incorporates visual stimulation history to predict the firing of LGN neurons in response to visual stimulation. To assess the performance of the model, we recorded stimulus-driven activity from the LGN in three anesthetized rats. Multielectrode arrays with 32 channels were used to simultaneously record the extracellular activity of LGN neurons in response to single-pixel flashing stimulation patterns. Visual stimulation information and the corresponding neuronal firing rates were then used to train the proposed visual encoder that was subsequently used to predict LGN firing in a testing dataset. Our results indicate the efficacy of the proposed encoder, where a mean correlation of 0.66 between the actual and the predicted firing rates obtained using the proposed model is achieved. The results also revealed the dependency of the prediction accuracy on the length of the visual stimulation history window incorporated in the model. This approach could help in better identifying electrical stimulation patterns for thalamic visual prostheses.