[home] [Personal Program] [Help]

---

Session: Eye
Session starts: Thursday 26 January, 14:30
Presentation starts: 15:45
Room: Room 558

---

Bakr Abdelgaliel (Department of Microelectronics, Delft University of Technology)
Dante Muratore (Department of Microelectronics, Delft University of Technology)

Abstract:
A retinal prosthesis was recently proposed as a promising method to restore partial vision in degienerative disease patients by stimulating remaining healthy neurons in the retina. [1]. Commercial epiretinal implants are already available and have been implanted in patients. Unfortunately, they currently only provide a low-resolution vision for a variety of reasons, including low channel counts, large electrode sizes, and failure to account for ganglion cell heterogeneity. In order to improve upon this, many more channels - in the order of 104 - are required, so that a large region of the RGC layer can be stimulated [2]. An artificial retina proposed in [2] is under development and aims to restore vision with more precise control of natural neural codes in the retina. This project focuses on an implantable chip that is clinically feasible and has bi-directional capabilities when interfacing with neurons. The conceivable implanted system-on-chip consists of three main building blocks: 1) stimulation channels that can activate neurons with single-cell resolution; 2) recording channels that can capture spike activities over a massively parallel microelectrode array; 3) wireless power and data circuits. However, the specifications for a neural interface that attempts to approach the capability of the retinal neural circuitry pose major challenges in terms of area and power consumption for an implanted device. To overcome these challenges, this work focuses on 1) Developing an optimized algorithm that generates a safe stimulation waveform that reduces the residual artifacts with the lowest computational cost without losing its cell activation capabilities. 2) Designing an Application-Specific Integrated Circuit (ASIC) to record neuron activities and generate specific spiking patterns at the single-cell resolution based on the electrode impedance. The first step of this work is to explore novel optimization schemes for finding the waveform shaping parameters to minimize computational complexity and memory requirements, refactoring for implementation on-chip. In vitro validation of the computational results is required. To facilitate in vitro studies with novel waveform shapes, a flexible hardware platform was developed. Furthermore, the generated waveforms were tested on a neuron model to check its efficiency in activating the neuron. The results of the hardware platform and waveform testing will be presented during the conference. A fully integrated design of the bidirectional neural interface of the retina implant system will be further explored in future work.