Department of Neurobiology, Stanford University, 299 W. Campus Drive, Stanford, CA 94305, USA.
Neuron. 2012 Jan 12;73(1):193-205. doi: 10.1016/j.neuron.2011.10.037.
As a precursor to the selection of a stimulus for gaze and attention, a midbrain network categorizes stimuli into "strongest" and "others." The categorization tracks flexibly, in real time, the absolute strength of the strongest stimulus. In this study, we take a first-principles approach to computations that are essential for such categorization. We demonstrate that classical feedforward lateral inhibition cannot produce flexible categorization. However, circuits in which the strength of lateral inhibition varies with the relative strength of competing stimuli categorize successfully. One particular implementation--reciprocal inhibition of feedforward lateral inhibition--is structurally the simplest, and it outperforms others in flexibly categorizing rapidly and reliably. Strong predictions of this anatomically supported circuit model are validated by neural responses measured in the owl midbrain. The results demonstrate the extraordinary power of a remarkably simple, neurally grounded circuit motif in producing flexible categorization, a computation fundamental to attention, perception, and decision making.
作为选择注视和注意的刺激物的前奏，中脑网络将刺激物分为“最强”和“其他”。这种分类实时灵活地跟踪最强刺激物的绝对强度。在这项研究中，我们采用了一种基本原理方法来进行计算，这些计算对于这种分类是必不可少的。我们证明经典的前馈侧抑制不能产生灵活的分类。然而，其中侧抑制的强度随竞争刺激物的相对强度而变化的电路可以成功地进行分类。一个特殊的实现——前馈侧抑制的相互抑制——在结构上是最简单的，并且在快速可靠地灵活分类方面表现优于其他。这种具有解剖学支持的电路模型的强烈预测得到了在猫头鹰中脑测量的神经反应的验证。结果表明，一种非常简单、基于神经的电路模式在产生灵活分类方面具有非凡的力量，这种计算是注意力、感知和决策的基础。
