Distributed In-Memory Computing on Binary Memristor-Crossbar for Machine Learning

Advances in Memristors, Memristive Devices and Systems - Studies in Computational Intelligence ◽

10.1007/978-3-319-51724-7_12 ◽

2017 ◽

pp. 275-304 ◽

Cited By ~ 2

Author(s):

Hao Yu ◽

Leibin Ni ◽

Hantao Huang

Keyword(s):

Machine Learning ◽

Memristor Crossbar

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A digital neuromorphic VLSI architecture with memristor crossbar synaptic array for machine learning

2012 IEEE International SOC Conference ◽

10.1109/socc.2012.6398336 ◽

2012 ◽

Cited By ~ 33

Author(s):

Yongtae Kim ◽

Yong Zhang ◽

Peng Li

Keyword(s):

Machine Learning ◽

Vlsi Architecture ◽

Memristor Crossbar ◽

Neuromorphic Vlsi

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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