Hu Brian, Niebur Ernst
Zanvyl Krieger Mind/Brain Institute and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Zanvyl Krieger Mind/Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21218, USA.
J Comput Neurosci. 2017 Dec;43(3):227-242. doi: 10.1007/s10827-017-0659-3. Epub 2017 Sep 19.
Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.
物体的视觉处理利用了前馈和反馈信息流。然而，反馈信号的本质在很大程度上尚不清楚，接收这些信号的低级视觉区域中的神经元群体的身份也是如此。在这里，我们开发了一个循环神经模型，以在轮廓整合和图形-背景分离的背景下解决这些问题。我们模型的一个关键特征是使用分组神经元，其活动基于局部特征信息的整合来表示暂定物体（“原物体”）。分组神经元从一组有组织的局部特征神经元接收输入，并向这些相同的神经元投射调节性反馈。此外，局部特征水平和物体表征水平的抑制都使视觉场景的解释产生偏差，这与格式塔心理学的原理一致。我们的模型解释了几组神经生理学结果（周等人，《神经科学杂志》，20(17)，6594 - 6611，2000年；邱等人，《自然神经科学》，10(11)，1492 - 1499，2007年；陈等人，《神经元》，82(3)，682 - 694，2014年），并对神经元反馈和注意力选择对不同视觉区域神经反应的影响做出了可测试的预测。我们的模型还提供了一个框架，用于理解基于物体的注意力如何能够选择物体及其相关特征。
