Joukes Jeroen, Yu Yunguo, Victor Jonathan D, Krekelberg Bart
Center for Molecular and Behavioral Neuroscience, Rutgers University, NewarkNJ, USA; Behavioral and Neural Sciences Graduate Program, Rutgers University, NewarkNJ, USA.
Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York NY, USA.
Front Syst Neurosci. 2017 Mar 15;11:12. doi: 10.3389/fnsys.2017.00012. eCollection 2017.
To discriminate visual features such as corners and contours, the brain must be sensitive to spatial correlations between multiple points in an image. Consistent with this, macaque V2 neurons respond selectively to patterns with well-defined multipoint correlations. Here, we show that a standard feedforward model (a cascade of linear-non-linear filters) does not capture this multipoint selectivity. As an alternative, we developed an artificial neural network model with two hierarchical stages of processing and locally recurrent connectivity. This model faithfully reproduced neurons' selectivity for multipoint correlations. By probing the model, we gained novel insights into early form processing. First, the diverse selectivity for multipoint correlations and complex response dynamics of the hidden units in the model were surprisingly similar to those observed in V1 and V2. This suggests that both transient and sustained response dynamics may be a vital part of form computations. Second, the model self-organized units with speed and direction selectivity that was correlated with selectivity for multipoint correlations. In other words, the model units that detected multipoint spatial correlations also detected space-time correlations. This leads to the novel hypothesis that higher-order spatial correlations could be computed by the rapid, sequential assessment and comparison of multiple low-order correlations within the receptive field. This computation links spatial and temporal processing and leads to the testable prediction that the analysis of complex form and motion are closely intertwined in early visual cortex.
为了辨别诸如角点和轮廓等视觉特征，大脑必须对图像中多个点之间的空间相关性敏感。与此一致的是，猕猴V2神经元对具有明确多点相关性的模式有选择性反应。在这里，我们表明标准的前馈模型（线性-非线性滤波器的级联）无法捕捉这种多点选择性。作为替代方案，我们开发了一种具有两个层次处理阶段和局部循环连接的人工神经网络模型。该模型忠实地再现了神经元对多点相关性的选择性。通过对模型进行探究，我们对早期形态处理有了新的见解。首先，模型中隐藏单元对多点相关性的多样选择性和复杂响应动态与在V1和V2中观察到的惊人相似。这表明瞬态和持续响应动态可能都是形态计算的重要组成部分。其次，模型自组织出具有速度和方向选择性的单元，这些选择性与对多点相关性的选择性相关。换句话说，检测到多点空间相关性的模型单元也检测到了时空相关性。这引出了一个新的假设，即高阶空间相关性可以通过对感受野内多个低阶相关性的快速、顺序评估和比较来计算。这种计算将空间和时间处理联系起来，并得出可测试的预测，即复杂形态和运动的分析在早期视觉皮层中紧密交织。
