PDF(Pubmed)
Abstract：
Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.
摘要：
了解环境刺激的编码和解码基础的计算机制是神经科学中的一项至关重要的研究。这种追求的核心是探索大脑如何在其分层结构中代表视觉信息。一个突出的挑战在于辨别动态自然视觉场景处理的神经基础。尽管已经做出了大量的研究努力来表征视觉通路的各个组成部分，对与视觉刺激相关的独特神经编码的系统理解，当他们穿越这个等级森严的景观时，仍然难以捉摸。在这项研究中,我们利用全面的Allen视觉编码-Neuropixels数据集，并利用深度学习神经网络模型的功能来研究神经编码，以响应广泛的大脑区域阵列中的动态自然视觉场景。我们的研究表明，我们的解码模型巧妙地破译了每个不同大脑区域内表现出的神经尖峰模式的视觉场景。从解码性能的比较分析中得出了令人信服的观察结果，表现为视觉皮层和皮层下细胞核内的显着编码能力，与海马神经元内相对降低的编码活性相反。引人注目的是，我们的结果揭示了我们的解码指标与已建立的解剖学和功能层次指数之间的稳健相关性.这些发现证实了与人工视觉刺激相关的视觉编码的现有知识，并阐明了使用动态刺激的这些更深的大脑区域的功能作用。因此,我们的结果提出了一种新的观点，即解码神经网络模型作为量化由神经响应表示的动态自然视觉场景的编码质量的度量标准。从而促进我们对大脑复杂层次结构中视觉编码的理解。
