Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, and Division of Biology, California Institute of Technology, Pasadena, California 91125.
J Neurosci. 2013 Oct 23;33(43):16971-82. doi: 10.1523/JNEUROSCI.2257-13.2013.
A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.
许多生物的视觉系统面临的一个基本任务是准确跟踪移动猎物的位置。视网膜是处理此类刺激的第一阶段；这里的从光子到尖峰序列的转换性质不仅限制了跟踪信号的最终保真度，还限制了其他大脑区域提取它的容易程度。在这里，我们证明了蝾螈视网膜中的一群快速 OFF 神经节细胞，其动力学由一个非线性电路控制，用于计算目标在数百毫秒内的未来位置。目标的外推位置不是通过刺激重建找到的，而是通过神经节细胞输出的加权和，即群体矢量平均值（PVA）计算得出的。PVA 外推的幅度与目标大小、速度和加速度系统地变化，使得大目标在高速下跟踪最准确，小目标在低速下跟踪最准确，就像真实猎物的运动一样。跟踪精度达到单个光感受器的分辨率，并且 PVA 算法比几种替代算法更稳健。如果如我们所建议的，蝾螈大脑将快速 OFF 细胞电路用于目标外推，那么电路动力学应该会在行为上留下微观结构，未来的实验可能会测量到这种结构。我们的分析强调了简单计算的实用性，这些计算虽然不是全局最优的，但在有限但与行为相关的刺激范围内，它们的实现效率高，性能接近最优。
