Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how...

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Veröffentlicht in:	Human brain mapping 2018-05, Vol.39 (5), p.2269-2282
Hauptverfasser:	Shi, Junxing, Wen, Haiguang, Zhang, Yizhen, Han, Kuan, Liu, Zhongming
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Brain Brain Mapping Computation Computational neuroscience Cortex (temporal) deep learning Distributed memory Eye Movements Feature extraction Feature recognition Female Humans Image Processing, Computer-Assisted Information processing Learning Magnetic Resonance Imaging Male Memory Memory - physiology Models, Neurological natural vision neural encoding Neural networks Oxygen - blood process memory Recognition, Psychology recurrent neural network Recurrent neural networks Representations temporal receptive window Vision Vision, Ocular - physiology Visual cortex Visual observation Visual Pathways - diagnostic imaging Visual Pathways - physiology Visual perception Visual stimuli Visual task performance
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