Instrumenting the nervous system at single-cell resolution

 A neural interface is a direct communication pathway between the nervous system and an electronic device. Neural interfaces of the future will be used to help restore lost sensory capabilities (e.g., retina and cochlear implant), or restore lost motor capabilities of people with motor impairments (e.g., due brainstem stroke, or spinal cord injury). They will also make it possible to augment human capabilities, including sensory acuity, control of complex devices, memory, attention and more. However, to realize this futuristic promise requires a major leap forward in how electronic devices interact with the nervous system. Due to power constraints, current devices provide a coarse communication link that does not respect the single-cell specificity of the neural network they are targeting, indiscriminately activating or recording multiple cells at the same time. 

The goal of my research is to design the next generation of neural interfaces that allow single-cell resolution when communicating with the nervous system. To achieve this, I have conceived a new way of reading information from the neural system. By leveraging stronger knowledge about the underlying biology, we can design electronic systems that dynamically adapt to it. In this way, the neural interface will learn the structure of the cells that it is interacting with and will dynamically redistribute its limited resources to the channels that provide most of the desired information.

To demonstrate this new approach, I will design a neural interface for an artificial retina, a device that replaces the function of retinal circuitry lost to disease. In principle, a device that reproduces the natural pattern of retinal neural activation, and transmits this neural signal to the brain, can restore vision in the patient. Because it is relatively well understood and easily accessible, the retina is an ideal system to develop such a device.

Project Details

Funding Type:

Interdisciplinary Scholar Award

Award Year:


Lead Researcher(s):

Team Members:

Boris Murmann (Sponsor, Electrical Engineering)
E.J. Jose Chichilnisky (Sponsor, Neurosurgery)