Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be usedto optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations.

Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be usedto optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations.

For nearly 100 years, we have understood the idea that it might be possible to restore sight to those who have become blind through a device that delivers electrical stimulation to the brain [Mirochnik, Pezaris, 2019]. Visual prostheses, as they are called, form part of a constellation of approaches that seek to deliver input to the brain to replace a lost or missing sense, including cochlear implants for the deaf, and cortical implants for the insensate, such as amputees with robotic arms. The challenges faced by each approach are similar: biological compatibility, long-term functional stability, and interpretability of the evoked sensations. Biological compatibility has thus far been addressed by careful selection of materials and implant techniques, but much remains to be done to create devices that the body will tolerate for decades with a low risk of infection or rejection. The first major challenge is long-term functional stability; ensuring that the effectiveness of the devices do not degrade over time.

A new robotic contact lens which is controlled by small eye movements, including double blinks to zoom in and out, has been created by scientists. The contact lens, which is made from just salt water, works by mimicking the natural electric signals in the human eyeball. There is a steady electrical potential between the eyeball's front and back, even when your eyes are closed or in total darkness. When you move your eyes to look around or blink, the motion of the electrical potential can be measured. Researchers from the University of California, San Diego, developed the lens using these signals, called electro-oculograms, to control a soft lens.