从视频中预测大规模小鼠视觉皮层活动的动态感官竞赛。

ArXiv Pub Date : 2024-07-12
Polina Turishcheva, Paul G Fahey, Michaela Vystrčilová, Laura Hansel, Rachel Froebe, Kayla Ponder, Yongrong Qiu, Konstantin F Willeke, Mohammad Bashiri, Eric Wang, Zhiwei Ding, Andreas S Tolias, Fabian H Sinz, Alexander S Ecker
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引用次数: 0

摘要

由于神经元反应和高维视觉输入之间的复杂非线性关系,理解生物视觉系统如何处理信息具有挑战性。人工神经网络已经通过允许计算神经科学家创建预测模型并桥接生物和机器视觉,提高了我们对该系统的理解。在Sensorium 2022比赛期间,我们引入了具有静态输入(即图像)的视觉模型基准。然而,动物在动态环境中运作并表现出色,因此研究和理解大脑在这些条件下的功能至关重要。此外,许多生物学理论,如预测编码,表明先前的输入对当前的输入处理至关重要。目前,还没有标准化的基准来识别鼠标视觉系统的最先进的动态模型。为了解决这一差距,我们提出了具有动态输入的Sensorium 2023基准竞赛(https://www.sensorium-competition.net/)。这项比赛包括从五只小鼠的初级视觉皮层收集一个新的大规模数据集,其中包含38000多个神经元对每个神经元超过2小时的动态刺激的反应。主要基准赛道的参与者将竞争确定用于动态输入(即视频)的神经元反应的最佳预测模型。我们还将主持一个奖励跟踪,在该跟踪中,将使用对统计数据与训练集不同的动态输入刺激的抑制神经元反应,对域外输入的提交性能进行评估。两首曲目都将提供行为数据和视频刺激。和以前一样,我们将提供代码、教程和强大的预训练基线模型,以鼓励参与。我们希望这场比赛将继续加强附带的Sensorium基准集合,作为衡量整个鼠标视觉层次及其他层次的大规模神经系统识别模型进展的标准工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.

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