Dynamics of analog logic-gate networks for machine learning

Itamar Shani; Liam Shaughnessy; John Rzasa; Alessandro Restelli; Brian R. Hunt; Heidi Komkov; Daniel P. Lathrop

doi:10.1063/1.5123753

Dynamics of analog logic-gate networks for machine learning

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5123753 ◽

2019 ◽

Vol 29 (12) ◽

pp. 123130 ◽

Cited By ~ 1

Author(s):

Itamar Shani ◽

Liam Shaughnessy ◽

John Rzasa ◽

Alessandro Restelli ◽

Brian R. Hunt ◽

...

Keyword(s):

Machine Learning ◽

Logic Gate ◽

Analog Logic

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Control of electrochemical signals from quantum dots conjugated to organic materials by using DNA structure in an analog logic gate

Bioelectrochemistry ◽

10.1016/j.bioelechem.2016.04.004 ◽

2016 ◽

Vol 111 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Qi Chen ◽

Si-Youl Yoo ◽

Yong-Ho Chung ◽

Ji-Young Lee ◽

Junhong Min ◽

...

Keyword(s):

Quantum Dots ◽

Organic Materials ◽

Logic Gate ◽

Dna Structure ◽

Analog Logic

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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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