Emerging Trends of Biomedical Circuits and Systems

Yang, Jie; Tarkhan, Mahdi; Chen, Jinbo; Wang, Qingwen; Xia, Fen; Chen, Yun-Hsuan

doi:10.1561/3500000005

Cited by 15 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Retinal prostheses may integrate dedicated processing algorithms to replace the function of degenerated cell layers by adopting electric-based or optogeneticbased stimulation techniques of corresponding retinal cells to generate visual perception. Hence, retinal prostheses and optogenetic-based stimulation techniques are promising therapies for patients with severe AMD or RP [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Wang

Fang

Zou³

et al. 2023

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

Abstract. Objective. Retinal prostheses are promising devices to restore vision for patients with severe AMD or RP disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina’s working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina’s working principle and state-of-the-arts computer vision models are combined together. Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models. Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect. Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What’s more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Wang

Fang

Zou³

et al. 2023

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The image sensors first perceive external scenes, and the recorded signals will be transmitted to a processing framework that replaces the function of the bypassed cell layer to generate a stimulation pattern. This device utilizes these patterns to stimulate ganglion cells to fire spikes, which can be interpreted by the human brain to produce visual perception (Sawan, Yang, Tarkhan, Chen, Wang, Wang, Xia, Chen et al (2021)).…”

Section: Introductionmentioning

confidence: 99%

SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses

Fang¹,

Zou²,

Yang³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Intelligent and low-power retinal prostheses are highly demanded in this era, where wearable and implantable devices are used for numerous healthcare applications. In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses. The spike representation encoding technique could interpret dynamic scenes with sparse spike trains, decreasing the data volume. The SRNN model, inspired by the human retina's special structure and spike processing method, is adopted to predict the response of ganglion cells to dynamic scenes. Experimental results show that the Pearson correlation coefficient of the proposed SRNN model achieves 0.93, which outperforms the state-of-the-art processing framework for retinal prostheses. Thanks to the spike representation and SRNN processing, the model can extract visual features in a multiplication-free fashion. The framework achieves 12 times power reduction compared with the convolutional recurrent neural network (CRNN) processing-based framework. Our proposed SpikeSEE predicts the response of ganglion cells more accurately with lower energy consumption, which alleviates the precision and power issues of retinal prostheses and provides a potential solution for wearable or implantable prostheses.

show abstract

“…N EURAL recording circuits and systems have undergone rapid development in recent years as they are indispensable to brain-machine interface (BMI) systems that are critical for the brain science discovery and treatment of psychological disorders and neurodegenerative diseases [1], [2]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, low on-chip power density is required to solve the tissue heating issue. Thirdly, input-referred noise from the inherent noise of tissue, electrodes, and circuits is expected to be small enough to avoid overwhelming small [2], [4].…”

Section: Introductionmentioning

confidence: 99%

Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

Jinbo

Tarkhan

et al. 2022

IEEE Trans. Circuits Syst. II

Self Cite

View full text Add to dashboard Cite

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.

show abstract

Emerging Trends of Biomedical Circuits and Systems

Cited by 15 publications

References 0 publications

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses

Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

Contact Info

Product

Resources

About