Tanaka Ryosuke, Zhou Baohua, Agrochao Margarida, Badwan Bara A, Au Braedyn, Matos Natalia C B, Clark Damon A
Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA.
Present Address: Institute of Neuroscience, Technical University of Munich, Munich 80802, Germany.
bioRxiv. 2023 Jul 11:2023.01.04.522814. doi: 10.1101/2023.01.04.522814.
In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly to suppress inappropriate stabilizing rotational behavior. experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.
在选择适当行为时，动物应权衡支持和反对关于世界的特定信念的感官证据。例如，动物测量光流以估计和控制自身旋转。然而，现有的光流检测模型可能会将外部物体的运动与真正的自身运动混淆。在这里，我们表明果蝇利用视网膜上的静止模式（这构成了反对自身旋转的负面证据）来抑制不适当的稳定旋转行为。实验表明，经过优化以区分自身和世界运动的人工神经网络同样能检测到静止状态并纳入负面证据。通过神经测量和基因操作，我们确定了用于静止模式检测的神经回路组件，该回路与果蝇的运动和光流探测器并行运行。我们的结果例证了果蝇紧凑的大脑如何利用视觉世界的几何约束纳入负面证据以提高航向稳定性。
