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Samuel J. Gershman on Muck Rack

Samuel J. Gershman

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Articles

Training Collaborators for Effective Division of Labor

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By Samuel J. Gershman, Yang Xiang, Fangze Tian
| Wiley Online Library Verified
Supporting Information Filename Description cogs70247-supp-0001-SuppMat.pdf141.8 KB Data S1 References (1964). Human capital: A theoretical and empirical analysis, with special reference to education. New York: National Bureau of Economic Research. (2006). Pattern recognition and machine learning. Springer. . (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. , , , & . (2021).
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Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks

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By Arthur Prat-Carrabin, Samuel J. Gershman, Maximilian V. Harl
| Nature Verified
Abstract As the statistics of sensory environments often change, neural sensory systems must adapt to maintain useful representations. Efficient coding prescribes that neuronal tuning curves should be optimized to the prior, but whether they can adapt rapidly is unclear.
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Trading places: What happens when neuroscience turns into machine learning, and machine learning turns into neuroscience?

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By Samuel J. Gershman, Matthew R. Whiteway, Marcus Ghosh
| The Transmitter Verified
Professor in the Department of Psychology and Center for Brain Science Harvard University Share this article: Tags: The big picture, Artificial intelligence, Machine learning, Methods Like most sciences, neuroscience has historically sought causal explanations for empirical phenomena. Machine learning, in contrast, has historically sought to engineer systems capable of prediction.
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