Turishcheva Polina, Fahey Paul G, Vystrčilová Michaela, Hansel Laura, Froebe Rachel, Ponder Kayla, Qiu Yongrong, Willeke Konstantin F, Bashiri Mohammad, Wang Eric, Ding Zhiwei, Tolias Andreas S, Sinz Fabian H, Ecker Alexander S
Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.
Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
ArXiv. 2024 Jul 12:arXiv:2305.19654v2.
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.
由于神经元反应与高维视觉输入之间存在复杂的非线性关系，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在生物视觉和机器视觉之间架起桥梁，增进了我们对这个系统的理解。在2022年感官竞赛期间，我们推出了针对静态输入（即图像）视觉模型的基准测试。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，表明先前的输入对于当前输入的处理至关重要。目前，尚无标准化基准来识别小鼠视觉系统的先进动态模型。为了填补这一空白，我们提出了具有动态输入的2023年感官基准竞赛（https://www.sensorium-competition.net/）。该竞赛包括从十只小鼠的初级视觉皮层收集一个新的大规模数据集，其中包含超过78,000个神经元对每个神经元超过2小时动态刺激的反应。主要基准赛道的参与者将竞争识别动态输入（即视频）神经元反应的最佳预测模型。我们还将举办一个奖励赛道，其中提交的性能将根据域外输入进行评估，使用对统计数据与训练集不同的动态输入刺激的保留神经元反应。两个赛道都将提供行为数据以及视频刺激。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。我们希望这次竞赛将继续加强随附的感官基准测试集，作为衡量整个小鼠视觉层次及其他领域大规模神经系统识别模型进展的标准工具。
