Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.
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TLDR
An argument for the primacy of naturalistic paradigms is developed, and recent developments in machine learning are pointed to as an example of the transformative power of relinquishing control.About:
This article is published in NeuroImage.The article was published on 2020-11-15 and is currently open access. It has received 140 citations till now.read more
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The default mode network: where the idiosyncratic self meets the shared social world.
TL;DR: The authors suggest that the default mode network (DMN) is an active and dynamic sense-making network that integrates incoming extrinsic information with prior intrinsic information to form rich, context-dependent models of situations as they unfold over time.
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Extensive sampling for complete models of individual brains
TL;DR: The authors argue that extensive sampling of experimental conditions is essential for understanding how human brains process complex stimuli, and that a model of how any one brain does this is likely to generalize to most other brains.
Journal ArticleDOI
The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex
TL;DR: In this article, a review of human and non-human primate studies is presented to demonstrate the involvement of the prefrontal cortex in emotional, cognitive, and behavioral alterations in drug addiction, with particular attention to the impaired response inhibition and salience attribution (iRISA) framework.
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Narratives: fMRI data for evaluating models of naturalistic language comprehension
Samuel A. Nastase,Yun-Fei Liu,Hanna Hillman,Asieh Zadbood,Liat Hasenfratz,Neggin Keshavarzian,Janice Chen,Christopher J. Honey,Yaara Yeshurun,Mor Regev,Mai Nguyen,Claire H.C. Chang,Christopher Baldassano,Olga Lositsky,Erez Simony,Erez Simony,Michael Chow,Yuan Chang Leong,Paula P. Brooks,Emily Micciche,Gina Choe,Ariel Goldstein,Tamara Vanderwal,Yaroslav O. Halchenko,Kenneth A. Norman,Uri Hasson +25 more
TL;DR: The Narratives collection as discussed by the authors collects a variety of functional MRI datasets collected while human subjects listen to naturalistic spoken stories and provides rich metadata, preprocessed versions of the data ready for immediate use and the spoken story stimuli with time-stamped phoneme-and word-level transcripts.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
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FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.