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#prostheses

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earthling

Colombia’s Foreign Minister Luis Gilberto Murillo said his government is planning to help up to 50 Palestinian children from Gaza who have lost limbs by bringing them to Colombia and providing them with #prostheses.

Under the programme, the children, aged between 12 and 15 years, would be flown from Egypt to #Colombia alongside a family member or guardian, where they would spend six months in treatment and rehabilitation.

@palestine
#Gaza
#children
#amputation

Palestinian girl Eman al-Kholi, whose limb was amputated after being wounded in an Israeli strike that killed her parents in the Gaza Strip, lies on a bed in the European Hospital in Rafah in the southern Gaza Strip in December 2023 [Arafat Barbakh/Reuters]
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The vOICe vision BCI 😎🧠

Human-in-the-loop optimization for deep stimulus encoding in arxiv.org/abs/2306.13104 Still lacks in the loop for human learning with veridical and undistorted visual sensory feedback; blind humans can learn too;

arXiv.orgHuman-in-the-Loop Optimization for Deep Stimulus Encoding in Visual ProsthesesNeuroprostheses 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 used to 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. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies.
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