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Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network

机译:振动神经网络感知处理过程中的时空动力学

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We examine the dynamics of object recognition in a multilayer network of oscillatory elements. The derivation of network dynamics is based on principles of sparse representation, and results in system behavior that achieves binding through phase synchronization. We examine the behavior of the network during recognition of objects with missing contours. We observe that certain network units respond to missing contours with reduced amplitude and temporal delay, similar to neuroscien-tific findings. Furthermore, these units maintain synchronization with a high-level object representation only in the presence of feedback. Our results suggest that the illusory contour phenomena are formal consequences of a system that dynamically solves the binding problem, and highlight the functional relevance of feedback connections.
机译:我们研究了振荡元素的多层网络中对象识别的动力学。网络动力学的推导基于稀疏表示的原理,并导致系统行为通过相位同步来实现绑定。我们在识别轮廓缺失的对象期间检查网络的行为。我们观察到某些网络单元对缺失的轮廓具有减小的幅度和时间延迟的响应,类似于神经科学的发现。此外,这些单元仅在存在反馈的情况下才与高级对象表示保持同步。我们的结果表明,虚幻的轮廓现象是系统的形式化结果,该系统动态地解决了绑定问题,并突出了反馈连接的功能相关性。

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