• Fabian H Sinz, Xaq Pitkow, Jacob Reimer, Matthias Bethge, and Andreas S Tolias.
• Institute Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany; Bernstein Center for Computational Neuroscience, University of Tübingen, Germany; Center for Neuroscience and Artificial Intelligence, BCM, Houston, TX, USA. Electronic address: fabian.sinz@uni-tuebingen.de.
• Neuron. 2019 Sep 25; 103 (6): 967-979.

AbstractDespite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a learning algorithm, such as network architecture and learning rule. Their joint effect, called "inductive bias," determines how well any learning algorithm-or brain-generalizes: robust generalization needs good inductive biases. Artificial networks use rather nonspecific biases and often latch onto patterns that are only informative about the statistics of the training data but may not generalize to different scenarios. Brains, on the other hand, generalize across comparatively drastic changes in the sensory input all the time. We highlight some shortcomings of state-of-the-art learning algorithms compared to biological brains and discuss several ideas about how neuroscience can guide the quest for better inductive biases by providing useful constraints on representations and network architecture.Copyright © 2019 Elsevier Inc. All rights reserved.

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