A Kalman-based encoder for electrical stimulation modulation in a thalamic network model

Abstract

Restoring vision is no longer impossible as a result of recent advances in neural interfaces. Successful demonstrations of retinal implants motivate the development of more effective visual prostheses. The thalamic Lateral Geniculate Nucleus (LGN) is one potential deep-brain interfacing site for visual prostheses. A main challenge in developing thalamic as well as other visual prostheses is optimizing the parameters of electrical stimulation. This paper introduces a Kalman-based optimal encoder whose function is to determine the optimal electrical stimulation parameters required to induce a certain visual sensation. The performance of the proposed approach is demonstrated using a probabilistic model of LGN neurons. Results demonstrate a significant similarity between neuronal responses obtained using electrical stimulation and the responses obtained using the corresponding visual stimuli with a mean correlation of 0.62 (P <; 0.01, n = 54). These results indicate the efficacy of the proposed optimal encoder in driving LGN neurons to induce visual sensations.