Center for Neural Science, New York University, New York, NY 10003, USA.
Neural Comput. 2013 Jan;25(1):1-45. doi: 10.1162/NECO_a_00383. Epub 2012 Sep 28.
In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.
在视觉和听觉场景中，我们能够识别感官对象之间的共享特征，并根据它们的相似性对其进行分组。这种分组是前注意的，快速的，被认为是一种基本的分类形式，其中具有相似特征的对象在某些抽象的感知空间中聚类。目前尚不清楚这种快速分类的神经机制是什么。在这里，我们提出了一种基于连续吸引子网络框架的快速特征分类的神经机制模型。类别形成的机制不依赖于学习，而是基于合理的生物学假设，例如存在对特征值调谐的神经元群体、特征特异性相互作用以及在单个物体呈现时的亚阈值诱发反应。当网络接收到一系列以某种特征为特征的刺激时，网络会对诱发反应进行求和，并提供输入流中特征分布的实时估计。如果特征的分布被组织成不同的成分或峰值（即多模态），则递归兴奋会放大激活神经元的反应，并且类别被突出为新兴的局部神经元活动升高模式（凸起），以每个簇的质心为中心。通过顺序的、亚阈值的激活以及对输入统计数据的依赖性来实现凸起状态的出现，这是吸引子网络的一种新应用。我们表明，通过网络丰富的吸引子结构，可以促进多个类别的提取和表示，该结构可以为与皮层生理学兼容的稳健范围的连接参数维持多个稳定的活动模式。
