Selective Attention Model of Moving Objects
Selective Attention Model of Moving Objects
复制标题
DOI：
10.1007/978-3-540-87559-8_37
复制
发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
R. Borisyuk;D. Chik;Y. Kazanovich
中科院分区：
文献类型：
--
作者：
R. Borisyuk;D. Chik;Y. Kazanovich
跟踪运动物体是动物生存的重要视觉任务。我们描述了振荡神经网络模型的视觉注意力与一个中心元素，可以跟踪移动目标之间的一组干扰在屏幕上。在初始阶段，该模型将注意力集中在被视为目标的任意对象上。其他物体被视为干扰物。我们在这里介绍两种模型：1）相位振荡器的基于同步的AMCO模型和2）尖峰神经模型，其基于资源有限的并行视觉指针的思想。选择性注意和跟踪过程由中央单元和外围元素子组之间的部分同步表示。仿真结果与心理学实验结果基本一致：目标和干扰物的重叠是错误的主要来源。未来的研究包括跟踪性能和神经元频率之间的依赖关系。
Tracking moving objects is a vital visual task for the survival of an animal. We describe oscillatory neural network models of visual attention with a central element that can track a moving target among a set of distracters on the screen. At the initial stage, the model forms focus of attention on an arbitrary object that is considered as a target. Other objects are treated as distracters. We present here two models: 1) synchronisation based AMCO model of phase oscillators and 2) spiking neural model which is based on the idea of resource-limited parallel visual pointers. Selective attention and the tracking process are represented by partial synchronization between the central unit and subgroup of peripheral elements. The simulation results are in overall agreement with the findings from psychological experiments: overlapping between target and distractor is the main source of error. Future investigations include the dependence between tracking performance and neuron frequency.