Sompolinsky H, Golomb D, Kleinfeld D
AT&T Bell Laboratories, Murray Hill, NJ 07974.
Proc Natl Acad Sci U S A. 1990 Sep;87(18):7200-4. doi: 10.1073/pnas.87.18.7200.
An oscillator neural network model is presented that is capable of processing local and global attributes of sensory input. Local features in the input are encoded in the average firing rate of the neurons while the relationships between these features can modulate the temporal structure of the neuronal output. Neurons that share the same receptive field interact via relatively strong feedback connections, while neurons with different fields interact via specific, relatively weak connections. This pattern of connectivity mimics that of primary visual cortex. The model is studied in the context of processing visual stimuli that are coded for orientation. We compare our theoretical results with recent experimental evidence on coherent oscillatory activity in the cat visual cortex. The computational capabilities of the model for performing discrimination and segmentation tasks are demonstrated.
提出了一种振荡神经网络模型，该模型能够处理感觉输入的局部和全局属性。输入中的局部特征编码在神经元的平均放电率中，而这些特征之间的关系可以调节神经元输出的时间结构。具有相同感受野的神经元通过相对较强的反馈连接进行交互，而具有不同感受野的神经元通过特定的、相对较弱的连接进行交互。这种连接模式模仿了初级视觉皮层的连接模式。在处理按方向编码的视觉刺激的背景下对该模型进行了研究。我们将理论结果与最近关于猫视觉皮层相干振荡活动的实验证据进行了比较。展示了该模型执行辨别和分割任务的计算能力。
