Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong.
Elife. 2018 Jun 18;7:e31557. doi: 10.7554/eLife.31557.
Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few moments of video or audio in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimized to extract those features with the most capacity to predict future input.
感觉皮层中的神经元对自然场景中的各种特征具有选择性。但是，是什么决定了神经元会对哪些特征产生选择性呢？在这里，我们探讨了这样一种观点，即神经元的选择性是经过优化的，可以表示最近的感官过去中那些最能预测当前输入的特征。我们使用简单的前馈神经网络来检验这一假设，这些网络经过训练，可以预测自然场景片段中视频或音频的下几个瞬间。这些网络发展出的感受野与不同哺乳动物物种中真实皮质神经元的感受野非常匹配，包括初级视觉皮层的定向空间调谐、初级听觉皮层的频率选择性，以及最显著的是它们的时间调谐特性。此外，一个网络对未来输入的预测越好，它的感受野就越接近大脑中的感受野。这表明，感觉处理是经过优化的，可以提取那些最有能力预测未来输入的特征。
