Decisions about where to move the eyes depend on neurons in frontal eye field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called salience by competitive and recurrent interactions (SCRI), based on the competitive interaction model of attentional selection and decision-making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identification information to yield a dynamically evolving representation of salience across the visual field. SCRI accounts for neural spiking of individual FEF visual neurons, explaining idiosyncratic differences in neural dynamics with specific parameters. Many visual neurons resolve the competition between search items through feedforward inhibition between signals representing different search items, some also require lateral inhibition, and many act as recurrent gates to modulate the incoming flow of information about stimulus identity. SCRI was tested further by using simulated spiking representations of visual salience as input to the gated accumulator model of FEF movement neurons (Purcell et al., 2010, 2012). Predicted saccade response times fit those observed for search arrays of different set sizes and different target-distractor similarities, and accumulator trajectories replicated movement neuron discharge rates. These findings offer new insights into visual decision-making through converging neuro-computational constraints and provide a novel computational account of the diversity of FEF visual neurons.

中文翻译：

---

通过竞争性和循环性交互的显着性：在视觉注意中桥接神经尖峰和计算。

眼睛向何处移动的决定取决于额眼场 (FEF) 的神经元。FEF 中的运动神经元积累来自 FEF 视觉神经元的显着证据，以在干扰因素中选择眼跳目标的位置。视觉神经元如何实现这种显着性表示尚不清楚。我们基于注意力选择和决策的竞争性交互模型，提出了一种称为竞争性和循环交互显着性（SCRI）的目标选择神经计算模型（Smith & Sewell，2013）。SCRI 通过综合定位和识别信息来选择目标，以产生整个视野中显着性的动态演变表示。SCRI 解释了单个 FEF 视觉神经元的神经尖峰，解释了具有特定参数的神经动力学的特殊差异。许多视觉神经元通过代表不同搜索项目的信号之间的前馈抑制来解决搜索项目之间的竞争，一些视觉神经元还需要侧向抑制，并且许多视觉神经元充当循环门来调节有关刺激身份的信息的传入流。SCRI 通过使用视觉显着性的模拟尖峰表示作为 FEF 运动神经元门控累加器模型的输入进行了进一步测试（Purcell 等，2010，2012）。预测的扫视响应时间符合对不同集合大小和不同目标干扰器相似性的搜索阵列观察到的响应时间，并且累加器轨迹复制了运动神经元放电率。这些发现通过融合神经计算约束为视觉决策提供了新的见解，并为 FEF 视觉神经元的多样性提供了一种新颖的计算解释。