An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications

An Engineering Platform for Clinical Application of Optogenetic Therapy in Retinal Degenerative Diseases

Optogenetics is a new approach for controlling neural circuits with numerous applications in both basic and clinical science. In retinal degenerative diseases, the photoreceptors die, but inner retinal cells remain largely intact. By expressing light sensitive proteins in the remaining cells, optogenetics has the potential to offer a novel approach to restoring vision. In the past several years, optogenetics has advanced into an early clinical stage, and promising results have been reported. At the current stage, there is an urgent need to develop hardware and software for clinical training, testing, and rehabilitation in optogenetic therapy, which is beyond the capability of existing ophthalmic equipment. In this paper, we present an engineering platform consisting of hardware and software utilities, which allow clinicians to interactively work with patients to explore and assess their vision in optogenetic treatment, providing the basis for prosthetic design, customization, and prescription. This approach is also applicable to other therapies that utilize light activation of neurons, such as photoswitches.Clinical and Translational Impact Statement-The engineering platform allows clinicians to conduct training, testing, and rehabilitation in optogenetic gene therapy for retinal degenerative diseases, providing the basis for prosthetic design, customization, and prescription.

NeuroSEE: A Neuromorphic Energy Efficient Processing Framework for Visual Prostheses

Visual prostheses with both comprehensive visual signal processing capability and energy efficiency are becoming increasingly demanded in the age of intelligent personal healthcare, particularly with the rise of wearable and implantable devices. To address this trend, we propose NeuroSEE, a neuromorphic energy-efficient processing framework that combines a spike representation encoding technique and a bio-inspired processing method. This framework first utilizes sparse spike trains to represent visual information, and then a bio-inspired spiking neural network (SNN) is adopted to process the spike trains. The SNN model makes use of an IF neuron with multiple spikefiring rates to decrease the energy consumption without compensating for prediction performance. The experimental results indicate that when predicting the response of the primary visual cortex, the framework achieves a state-ofthe- art Pearson correlation coefficient performance. Spikebased recording and processing methods simplify the storage and transmission of redundant scene information and complex calculation processes. It could reduce power consumption by 15 times compared with the existing Convolutional neural network (CNN) processing framework. The proposed NeuroSEE framework predicts the response of the primary visual cortex in an energy efficient manner, making it a powerful tool for visual prostheses.

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics-genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)-are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.