The design of our artificial retina system is based on knowledge acquired in our unique laboratory setting. The Chichilnisky lab has spent many years studying how light stimuli are encoded by neural circuitry in the healthy retina, specifically, the non-human primate retina, which resembles the human retina much more closely than other animal models. Our goal is to use the information we and others have gathered to build an advanced retinal implant that can produce a naturalistic visual signal. Our main focus is on ganglion cells, the output neurons of the retina, which largely survive the retinal degeneration process. Ganglion cells generate visual representations of light inputs and transmit these encoded representations towards specific target areas in the brain. The human retina contains around 1 million ganglion cells, and all visual experiences ultimately arise from the signals transmitted by these cells. By first learning how patterns of activity in many ganglion cells represent a visual scene, we can mimic the neural retinal code using our artificial implant. If we accurately reproduce the retinal code, the brain can accurately perceive the visual image.


Ganglion cells come in many types

Importantly, ganglion cells are not a single cell population. The human retina contains about 20 distinct types of ganglion cells and each type responds differently to light stimuli. For example, some cells respond to the presence of light, while other cells respond to the absence of light. Some cells convey color information, while others are more sensitive to flickering light. Some are characterized by small and smooth receptive fields while others show large and irregular receptive fields. Some show very fast responses to light, others are much slower to respond. Some types are very numerous, others are much less abundant. Recognizing this heterogeneity forms the basis of our artificial retina and sets our approach apart from all other approaches.

Each type of ganglion cell forms a uniform lattice, or mosaic, across the retina, thereby sampling the entire visual scene, and carries a specific aspect of the visual stimulus to its specific targets the brain. The different ganglion cell types have been likened to the various instruments in an orchestra, each contributing uniquely to the musical score. A faithful reproduction of music is only possible by having each instrument play its part with correct timing and order. Similarly, to reproduce a meaningful neural retinal code, it is crucial to respect the specificity and selectivity of the diverse retinal ganglion cell types.

The response to light stimuli in 7 identified ganglion cell types, recorded in primate retina using a 512-electrode array. Each oval represents the area of visual space where light changes the firing rate of a single ganglion cell (receptive field). These differ greatly in size, shape, and other response parameters.


Key questions we are asking

We use large-scale multi-electrode recording from the non-human primate retina to characterize normal light-evoked activity in hundreds of retinal ganglion cells of multiple types simultaneously. We then evoke similar patterns of activity by fine-grained, patterned electrical stimulation. Finally, we evaluate how effectively we have mimicked the neural code by using machine learning methods to estimate the visual image that would be perceived by the subject in response to this electrical stimulation. This approach constitutes our laboratory prototype for an artificial retina.

We focus on several questions:

  1. What distinct aspects of vision are encoded by the diverse types of ganglion cells?
  2. How can computational models accurately mimic these retinal signals?
  3. How can we precisely target individual ganglion cells using an implant?
  4. How can the distinct cell types be recognized and separately targeted by the implant?
  5. How can we engineer an implant that faithfully reproduces normal vision in blind patients?

We anticipate that in addition to restoring vision, the artificial retina will allow us to transmit visual information to the brain in ways that are not possible with light stimulation. This will open the door to visual augmentation – creating visual sensations that were never before possible. Our understanding of the retinal circuitry and how to interface to it effectively will be relevant for developing other interfaces to the brain – both for treating disease and for augmenting human capabilities.

Explore our approach on a more in-depth level by selecting the Details tab on this page.