Neuroscience Program, Stanford University School of Medicine, 299 Campus Drive West, Stanford, CA, USA.
Department of Neurobiology, Stanford University School of Medicine, 299 Campus Drive West, Stanford, CA, USA.
Curr Opin Neurobiol. 2014 Apr;25:63-9. doi: 10.1016/j.conb.2013.11.012. Epub 2013 Dec 22.
The retina performs a diverse set of complex, nonlinear, computations, beyond the simple linear photoreceptor weighting assumed in the classical understanding of ganglion cell receptive fields. Here we attempt to organize these computations and extract rules that correspond to three distinct goals of early sensory systems. First, the retina acts efficiently to transmit information to the higher brain for further processing. We observe that although the retina adapts to a number of complex statistics, many of these may be explained by local adaptation to the mean signal strength at that stage. Second, ganglion cells signal the detection of a diverse set of features. Recent results indicate that feature selectivity arises through the action of specific inhibition, rather than through the convergence of excitation as in classical cortical models. Finally, the retina conveys predictions about the stimulus, a function usually attributed only to the higher brain. We expect that computational and mechanistic rules associated with these classes of functions will be an important guide in the study of other neural circuits.
视网膜执行了一系列多样化的复杂、非线性计算，超越了经典理解中视神经元感受野所假设的简单线性光感受器权重。在这里，我们试图组织这些计算，并提取出与早期感觉系统的三个不同目标相对应的规则。首先，视网膜有效地将信息传递给大脑的更高层，以便进一步处理。我们观察到，尽管视网膜适应了许多复杂的统计数据，但其中许多可以通过局部适应该阶段的平均信号强度来解释。其次，神经节细胞发出对各种特征的检测信号。最近的结果表明，特征选择性是通过特定的抑制作用产生的，而不是像经典皮质模型那样通过兴奋的汇聚产生的。最后，视网膜传递了关于刺激的预测，这一功能通常仅归因于大脑的更高层。我们期望与这些功能类别相关的计算和机械规则将成为研究其他神经回路的重要指南。
