Dr. Sheila Nirenberg

The Technology Review article Now I See You: A surgery-free prosthetic retina restores vision in blind mice and raises the prospects for something similar in people said

Scientists have built a new type of prosthetic retina that could someday restore detailed sight to the millions of people who’ve lost their vision to retinal disease.
 
Neuroscientist Sheila Nirenberg, of Weill Cornell Medical College in New York, and postdoctoral student Chethan Pandarinath have enabled blind mice to see nearly normal images of everything from human and animal faces to complex panoramas of Central Park.
 
Artificial retinas already exist. But they require surgery to implant an array of electrodes deep into the eye. The electrodes stimulate cells that transmit information to the brain, and must be powered by an external battery. They are capable of restoring crude vision, allowing patients to pick up only major contrasts and edges, such as a light object against a dark background. But Nirenberg’s research, which was presented this week at the Society for Neuroscience meeting in San Diego, enables still and moving images to be conveyed more cleanly and rapidly than ever before possible. And the method doesn’t require surgery.

Sheila Nirenberg, Ph.D. is Associate Professor of Physiology and Biophysics at Weill Cornell Medical College and Associate Professor of Computational Neuroscience in Computational Biomedicine at Weill Cornell Medical College.
 
Sheila coauthored A novel mechanism for switching a neural system from one state to another, Heterogeneous Response Dynamics in Retinal Ganglion Cells: The Interplay of Predictive Coding and Adaptation, Pairwise maximum entropy models for studying large biological systems: when they can work and when they can’t, Ruling out and ruling in neural codes, Indices for testing neural codes, Ganglion cell adaptability: Does the coupling of horizontal cells play a role?, and Analyzing the activity of large populations of neurons: how tractable is the problem? Read the full list of her publications!
 
Her lab works on the general question, “How do networks of neurons process information?”, and she uses a combined theoretical and experimental approach.
 
The projects fall into three main areas. The first focuses on how neural circuits in the visual system carry out computations. Her approach is to dissect the circuits using a method she developed for targeted cell class ablation (a genetic, inducible method). Currently, she is focusing on the retina. She stimulates the retinal input cells, the photoreceptors, with computer-generated images while recording the responses of the retinal output cells, the ganglion cells. She then ablates specific classes of interneurons as a way to perturb the transfer of information from input to output and to test computational models for how the output is generated. Recently, she has expanded this work to higher brain areas, specifically visual cortex.
 
The second focuses on how populations of neurons in the visual system represent information, and also uses the retina as the model system. Her aim is to understand how the retinal output cells collectively encode visual scenes. Can we look at a set of spike trains coming out of the retina and know what the animal is seeing? Her approach involves three general steps: The first is to determine which aspects of the spike trains carry visual information, the second is to build a decoder that translates spike trains into visual scenes, and the third is to test the decoder against the animal’s behavior.
 
The last project focuses on how neural networks recognize and classify patterned inputs. Currently, she is addressing this question in the context of attractor networks, and is using networks of dissociated CNS neurons as her model system. The project involves two parts. The first is to examine the intrinsic behavior of the cultured networks. The second is to examine their behavior when they receive patterned input (i.e, patterned current pulses). She can then determine whether the network can learn to distinguish among the inputs by representing them as different patterns of stable activity, i.e., as attractors.
 
Note that her retinal prosthetic’s technology has applications for machine (robot) vision. Sheila earned her Ph.D. from Harvard University in 1993.