Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-08-31 DOI:10.1088/1741-2552/ace657
Moosa Zaidi, Gorish Aggarwal, Nishal P Shah, Orren Karniol-Tambour, Georges Goetz, Sasidhar S Madugula, Alex R Gogliettino, Eric G Wu, Alexandra Kling, Nora Brackbill, Alexander Sher, Alan M Litke, E J Chichilnisky
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Abstract

Objective. Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs.Approach.First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated.Main results.A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data.Significance.These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.

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利用内在电信号推断灵长类视网膜神经节细胞的光反应。
客观的视网膜植入物旨在刺激视网膜神经节细胞(RGCs),使因光感受器退化而失明的人恢复视力。用这些设备再现高视力可能需要推断植入视网膜中不同RGC类型的自然光反应,而不能直接测量它们。在这里,我们展示了一种利用灵长类RGC内在电生理特征的推断方法。方法。首先,在猕猴视网膜的大规模多电极记录中,使用其内在电特征来识别ON parasol和OFF parasol RGC类型。然后,使用每个细胞类型的电推断的体细胞位置、推断的细胞类型和平均线性非线性泊松模型参数来推断每个细胞的光响应模型。评估了细胞类型分类的准确性和用该模型再现测量的光响应的准确性。主要结果。在148个视网膜的246个大规模多电极记录上训练的细胞类型分类器在29个测试视网膜上实现了95%的平均准确率。在测试的五个视网膜中,推断出的模型与测量的白噪声视觉刺激的发射率和自然场景刺激的发射速率的平均相关性分别为0.49和0.50,而与记录的光反应(上限)拟合的模型分别为0.65和0.58。根据一个视网膜中预测的RGC活动对自然图像进行线性解码,显示解码图像和真实图像之间的平均相关性为0.55,而使用拟合光响应数据的模型的上限为0.81。意义。这些结果表明,从RGC电活动的内在特征推断RGC光响应特性可能是高保真视觉恢复的一种有用方法。首先从电学特征推断细胞类型,然后利用细胞类型来帮助推断自然细胞功能的总体策略也可能被证明对神经接口广泛有用。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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