neuroengineering

CS 291I: Bionic Vision

This graduate course will introduce students to the multidisciplinary field of bionic vision, with an emphasis on both the computer science and neuroscience of the field.

Model-based recommendations for optimal surgical placement of epiretinal implants

We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.

How can we design more effective stimulation strategies?

Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?

Data-driven models in human neuroscience and neuroengineering

In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering.

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.

Biophysical model of axonal stimulation in epiretinal visual prostheses

To investigate the effect of axonal stimulation on the retinal response, we developed a computational model of a small population of morphologically and biophysically detailed retinal ganglion cells, and simulated their response to epiretinal electrical stimulation. We found that activation thresholds of ganglion cell somas and axons varied systematically with both stimulus pulse duration and electrode-retina distance. These findings have important implications for the improvement of stimulus encoding methods for epiretinal prostheses.

Commentary: Detailed visual cortical responses generated by retinal sheet transplants in rats with severe retinal degeneration

A Commentary on: Detailed Visual Cortical Responses Generated by Retinal Sheet Transplants in Rats with Severe Retinal Degeneration by Foik, A. T., Lean, G. A., Scholl, L. R., McLelland, B. T., Mathur, A., Aramant, R. B., et al. (2018). J. Neurosci. 38, 10709–10724. doi: 10.1523/JNEUROSCI.1279-18.2018

Learning to see again: Biological constraints on cortical plasticity and the implications for sight restoration technologies

The goal of this review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision.

pulse2percept: A Python-based simulation framework for bionic vision

*pulse2percept* is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.