Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Department of Physics, Stanford University, Stanford, CA, USA.
Nat Commun. 2024 Sep 11;15(1):7964. doi: 10.1038/s41467-024-52304-7.
Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.
固视眼动改变了从视网膜传向大脑的尖峰信号的数量和时间，但这些变化是增强还是降低了视网膜信号尚不清楚。为了量化这一点，我们开发了一种贝叶斯方法，用于从猕猴视网膜中记录的数百个神经节细胞（RGC）的尖峰中重建自然图像（雄性），该方法将 RGC 光反应的似然模型与隐式嵌入用于去噪的人工神经网络中的自然图像先验相结合。该方法与以前的重建算法的性能相匹配或超越，并为表征视网膜信号提供了一个可解释的框架。通过模拟固视眼动的人工刺激抖动来改善重建，即使眼动轨迹被假设为未知，并且必须从视网膜尖峰中推断出来。通过对尖峰时间进行微小的人工扰动来降低重建质量，这比以前的研究表明的更精确的时间编码。最后，当从忽略细胞间相互作用的模型中得出重建时，表明了刺激诱发相关性的重要性。因此，固视眼动提高了视网膜表示的精度。
