公开(公告)号：US20240359032A1

公开(公告)日：2024-10-31

申请号：US18648286

申请日：2024-04-26

Applicant: Westlake University

Inventor： Chuanqing WANG ,  Jie YANG ,  MOHAMAD SAWAN

IPC: A61N5/06

CPC classification number: A61N5/0622 ,  A61N2005/0626 ,  A61N2005/0648

Abstract: Embodiments of the present disclosure relate to the biomedical technical field, and disclose a retinal prosthesis and a visual perception method based on the retinal prosthesis. The retinal prosthesis includes: a capturing assembly, a neuromorphic processor and a light stimulator. The capturing assembly is configured to capture an external scenario and encode the captured external scenario as spike sequences. The neuromorphic processor is configured to predict spike responses of ganglion cells of an implant recipient of the retinal prosthesis according to a preset deep learning algorithm and the spike sequences. The light stimulator is configured to stimulate the ganglion cells based on the spike responses of the ganglion cells. The retinal prosthesis can further reduce the data size and the amount of computation effectively, so that the power consumption is greatly reduced on the premise of keeping a relatively high processing speed.

公开(公告)号：US20240358895A1

公开(公告)日：2024-10-31

申请号：US18648289

申请日：2024-04-26

Applicant: Westlake University

Inventor： Chuanqing WANG ,  Jie YANG ,  MOHAMAD SAWAN

IPC: A61L27/36 ,  G06V10/774 ,  G06V20/40

CPC classification number: A61L27/3695 ,  A61L27/3604 ,  A61L27/3675 ,  G06V10/774 ,  G06V20/46 ,  A61L2430/16

Abstract: Disclosed are a model training method, a visual perception method, an electronic device and a storage medium. The model training method is applicable to a spiking recurrent model in a retinal prosthesis and includes: determining labels respectively corresponding to ganglion cells based on a preset ganglion cell response dataset; obtaining multiple spike signals as training samples; inputting the spike signals into the spiking recurrent model, obtaining spike responses of the ganglion cells predicted by the spiking recurrent model, and computing a loss value based on the spike responses of the ganglion cells, the labels and a preset Poisson loss function; and updating a weight for each layer in the spiking recurrent model based on the loss value and a preset time backpropagation function recursively until the spiking recurrent model converges, thereby scientifically and quickly training the spiking recurrent model for predicting the responses of retinal ganglion cells.