Dante Muratore

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Dante Muratore.jpg

BS/MS, EE, Politecnico di Torino, Italy

Ph.D., EE, University of Pavia, Italy

Email: dantemur AT stanford DOT edu

Linkedinhttps://www.linkedin.com/in/dantegmuratore

HIGH-BANDWIDTH BI-DIRECTIONAL NEURAL INTERFACES FOR EPI-RETINAL PROSTHESES

A retinal prosthesis is a device that replaces the function of retinal circuitry lost to disease. In principle, it captures the image with a camera and, through some signal processing that aims to replicate the natural code of the retina, it stimulates the remaining neurons to transmit artificial visual signals to the brain. Current devices use large electrodes (above 50 um) that activate multiple neurons at the same time, without any sensitivity to the diverse, cell-type specific circuitry in which they are embedded, [1]. 

In order to obtain high-quality visual restoration, a hypothetical prosthesis should achieve single cell activation accuracy over a significant area in the central retina, [2]. Such a device calls for 1-10k electrodes with a pixel pitch close to 10 um. Integrating all the functionalities required for the implant to work (optical sensors, micro electrode array, readout and stimulation circuitry, signal processing, wireless power) in a highly area constrained ASIC is a great system-level challenge. On the other side, achieving the required noise levels for successful spike sorting [3], while maintaining power consumption within the safe limits for implantability asks for novel circuit solutions.

My research, within the NeuroTechnology Initiative, focuses on two main aspects: designing a custom readout and stimulation ASIC that interacts with the micro electrode array, and guide the system-level integration of the entire prosthesis. 


[1] G. A. Goetz, D. V. Palanker, "Electronic approaches to restoration of sight", Reports on Progress in Physics, 2016.

[2] C. Sekirnjak, P. Hottowy, A. Sher, W. Dabrowski, A. M. Litke, E. J. Chichilnisky, "High-resolution electrical stimulation of primate retina for epiretinal implant design", Journal of Neuroscience, 2008.

[3] H. G. Rey, C. Pedreira, R. Quian Quiroga,"Past, present and future of spike sorting techniques" Brain Research Bulletin, 2015.

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