Ursino Mauro, Magosso Elisa, La Cara Giuseppe-Emiliano, Cuppini Cristiano
Department of Electronics, Computer Science, and Systems, University of Bologna, Cesena, Italy.
Biosystems. 2006 Sep;85(3):201-18. doi: 10.1016/j.biosystems.2006.01.005. Epub 2006 Apr 25.
Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.
物体识别需要解决绑定和分割问题，即对不同特征进行分组以实现连贯的表征。与格式塔感知相关的伽马波段神经活动同步，常被认为是解决这些问题的一种可能机制，不仅适用于低级处理，也适用于更高层次的皮层功能。在本研究中，通过实施两条格式塔规则：相似性和先验知识，使用威尔逊 - 考恩振荡器网络对同时出现的物体进行分割，并从部分或受损信息中恢复物体。该网络由H个不同区域组成，根据拓扑组织，每个区域专门用于表征物体的特定特征。相似性法则通过排列成“墨西哥帽”的区域内横向连接来实现。先验知识通过区域间连接来实现，这些连接将属于先前记忆物体的属性联系起来。一个全局抑制剂可避免多个物体之间的干扰，从而实现分割。使用三个同时输入的物体进行的模拟结果表明，即使在困难条件下（即某些特征缺失或相对于原始特征发生偏移时），该网络也能够检测到物体。此外，通过控制横向突触的范围（即通过接受的相似性水平），可以控制灵敏度（检测真阳性的能力）和特异性（拒绝假阳性的能力）之间的权衡。最后，该网络还可以处理相关物体，即具有一些共同特征的物体。使用不同数量的物体（2、3、4或5）进行的模拟表明，该网络能够分割并召回多达四个物体，但同时出现的物体数量越少，振荡频率必须越高。该模型虽然与神经生理学相比相当简单，但可能代表了一个理论框架，用于分析物体表征、记忆、学习和伽马波段活动之间的关系。特别是，它扩展了先前关于自联想记忆的研究，因为它不仅利用了振荡动力学，还利用了特征的拓扑组织。
