Brosch Tobias, Neumann Heiko, Roelfsema Pieter R
University of Ulm, Institute of Neural Information Processing, Ulm, Germany.
Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam, The Netherlands; Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands; Psychiatry Department, Academic Medical Center, Amsterdam, The Netherlands.
PLoS Comput Biol. 2015 Oct 23;11(10):e1004489. doi: 10.1371/journal.pcbi.1004489. eCollection 2015 Oct.
The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies.
视觉刺激的处理过程可细分为多个阶段。在呈现刺激时，存在一个前馈处理的早期阶段，视觉信息从较低视觉区域传播到较高视觉区域，以提取基本和复杂的刺激特征。随后是一个后期阶段，此时区域内的水平连接以及从较高区域到较低区域的反馈连接开始发挥作用。在这个后期阶段，行为相关的图像元素根据格式塔分组规则进行分组，并在皮层中通过增强的神经元活动进行标记（心理学中的基于对象的注意）。最近的神经生理学研究表明，基于奖励的学习会影响这些循环分组过程，但奖励如何训练用于知觉组织的循环回路尚不清楚。本文研究了基于奖励学习新分组规则的机制。我们推导了一个学习规则，该规则可以解释奖励如何影响通过前馈、水平和反馈连接的信息流。我们用两个用于研究早期视觉皮层中知觉组织的神经元相关性的任务来说明其有效性。第一个任务称为轮廓整合，要求将共线的轮廓元素整合为一条细长曲线。我们展示了基于奖励的学习如何在循环神经网络的早期水平上增强待分组元素的表征，正如在猴子的视觉皮层中观察到的那样。第二个任务是曲线追踪，其目的是确定由相连图像元素组成的细长曲线的端点。如果用新的学习规则进行训练，神经网络会学会根据神经生理学数据在曲线上传播增强的活动。我们在论文结尾提出了一些模型预测，这些预测可在未来的神经生理学和计算研究中进行测试。
