Optimal computation of flow field variables from natural visual signals

根据自然视觉信号对流场变量的优化计算许多动物使用视觉信息来导航它们的环境，在这种情况下，估计它们如何在空间中移动是很重要的。视觉输入由眼睛收集，包含与自我运动有关的信息，但我们所看到的和我们如何移动之间的联系是间接的，有时是模棱两可的。此外，光是由光子携带的，因为光子是随机到达的，所以视觉输入形成了对周围环境的嘈杂表示。为了从视觉输入中对自身运动做出最优估计，大脑必须使用一种算法，该算法考虑视觉输入信号的统计以及视觉输入和自我运动之间的概率关系。本项目将把自然世界中的运动估计作为统计估计问题来研究，并将统计最优处理的预测与生物系统中的测量进行比较。研究这个问题既有基本的原因，也有实际的原因，但还有一个额外的动机。也就是说，研究生物系统中的神经计算是否以及在多大程度上可以被理解为针对自然环境中的特定任务进行了优化。这通常是一个很难解决的问题，因为答案取决于鲜为人知的自然感觉信号的统计特性。本案例提供了一个例子，其中可以测量和量化需要估计的信号(旋转)和该估计所基于的数据(原始视觉输入)。此外，可以达到统计抽样的水平，从而允许将统计推断直接应用于代表自然感觉信号的数据。研究人员将建造一台精密的高速相机，具有代表飞行视觉系统的空间采样特征，并配备相关的陀螺传感器来测量相机沿三个轴(偏航、俯仰和侧滚)的旋转。有了这款相机，他们将在自然环境中同时精确测量相机的旋转运动和视觉输入。这种同步采样使得能够有效地测量描述运动和视觉输入之间的关系的概率分布。从该分布可以推导出最优统计运动估计器的特性。这些对最优估计器计算结构的预测将与苍蝇视觉大脑记录的运动敏感神经元的行为进行比较。这种比较将允许定量评估苍蝇的生物运动处理在多大程度上接近最佳性能。初步实验表明，苍蝇和最优估计器在其输出中都显示出特定的偏差，这取决于输入信号的统计。有强烈的迹象表明，包括人类在内的其他动物也有类似的偏见，这表明这些偏见是自然感觉信号最佳处理的必然和普遍结果。

Optimal computation of flow field variables from natural visual signals.R.R. de Ruyter van Steveninck, W. BialekMany animals use visual information to navigate their environment, and in this context it is important for them to estimate how they move through space. Visual input, gathered by the eye, contains information that is related to self motion, but the connection between what we see and how we are moving is indirect and sometimes ambiguous. Moreover, light is carried by photons, and because these arrive at random the visual input forms a noisy representation of the surroundings. To make optimal estimates of self motion from visual input, the brain must therefore use an algorithm that takes into account the statistics of the visual input signals and the probabilistic relation between visual input and self motion.This project will study motion estimation in the natural world as a statistical estimation problem, and compare the predictions of statistically optimal processing to measurements in a biological system. There are fundamental as well as practical reasons for studying the problem, but there is an additional motivation. That is to investigate if, and to what extent, neural computations in biological systems can be understood as being optimized for their specific tasks in the natural environment. This is a hard issue to solve in general, since the answer depends on poorly known statistical properties of natural sensory signals. The present case provides an example where one can measure and quantify both the signals that need to be estimated (rotations) and the data on which this estimate is based (raw visual input). Moreover, a level of statistical sampling can be achieved that allows a direct application of statistical inference to data that are representative for natural sensory signals. The investigators will construct a precise high speed camera, with spatial sampling characteristics representative for the fly visual system, and with associated gyrosensors to measure camera rotation along three axes (yaw, pitch and roll). With this camera they will make precise simultaneous measurements of rotational camera motion and visual input in a natural environment. This simultaneous sampling makes it possible to effectively measure the probability distributions that describe the relation between motion and visual input. From this distribution, the characteristics of the optimal statistical motion estimator can be derived. These predictions for the computational structure of the optimal estimator will be compared to the behavior of motion sensitive neurons recorded from the visual brain of the blowfly. That comparison will allow quantitative assessement of the extent to which biological motion processing in the blowfly approaches optimal performance. Preliminary experiments have shown that both the blowfly and the optimal estimator show specific biases in their output, depending on the statistics of the input signals. There are strong indications that other animals, including humans, share similar biases, suggesting that these biases are an inevitable and universal consequence of the optimal processing of natural sensory signals.