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The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding
2017
Frontiers in Computational Neuroscience
The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As
doi:10.3389/fncom.2017.00013
pmid:28377709
pmcid:PMC5360096
fatcat:kdfbgen7anawpl2p3v4u3aqnse