The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the Sensorium benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (Sensorium) or stimulus plus behavior (Sensorium+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond.
翻译:生物视觉系统的神经基础是实验性研究的艰巨任务,特别是因为神经活动在视觉输入方面日益变得非线性。人工神经网络(ANNS)可以服务于各种提高我们对这个复杂系统的了解的目标,不仅作为感官皮层的预测数字双胞胎,在硅的新假设生成过程中可以作为感官皮层的新假设的预测数字双胞胎,而且还包括生物激发的建筑构件,以逐步弥合生物和机器视觉之间的差距。鼠标最近成为研究视觉信息处理的流行模型系统,但没有建立标准化的大规模基准,以确定鼠标视觉系统的最新神经模型。为填补这一空白,我们建议Sensorium基准竞争。我们从鼠标一级视觉皮层收集了一个大型数据集,其中包含了7个小鼠超过28 000个神经元的反应,并同时进行行为测量,包括运行速度、学生放大和眼睛运动。基准挑战将以神经神经反应的预测性表现模型为基础,用于维持直径直径直线的直径测试系统,包括开始的直径直径直径的直径的直径直径直径直径直径直径直径直径直的直径直径直径直径直径直径直径直径直进入一个测试系统,,并包含的S进入一个螺旋进入一个分导的系统的系统,作为S的缓径直进入一个模型的系统,以及一个螺旋压的螺旋系统,作为空间标的螺旋系统,,作为空间压的动力基,作为一个模型的动力路路路路路路路路路路路路路,作为一个模型。