title: Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex

authors: Steyn-Ross, Moira L.

subjects: REM

abstract: Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer's dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

text: Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer's dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

year: 2005

title: The Pepsi‐Jenner disaster: Translation and collaboration in global image markets

authors: Isaacs, Bronwyn

abstract: In April 2017, Pepsi released a video commercial featuring the American celebrity Kendall Jenner that rapidly ballooned into a pop culture moment with international import. The infamous commercial begins with a soft rock backing track and a young man playing the cello against an unspecified city skyline. The next scene is a clean-cut, multi-ethnic crowd of marching young people. Smiling and fist-pumping under their fedora hats and headscarves, the students are waving signs with slogans including ‘Join the conversation’ and ‘Peace’. As the music crescendos, the students face off in front of a silent line of police. A smiling Jenner then places a can of Pepsi in the muscled hands of a young male officer dressed in an American-style police uniform. A moment of shock. Silence. Then, inexplicably, the protesters burst forth in cheers and dancing.

text: The Pepsi‐Jenner disaster: Translation and collaboration in global image markets In April 2017, Pepsi released a video commercial featuring the American celebrity Kendall Jenner that rapidly ballooned into a pop culture moment with international import. The infamous commercial begins with a soft rock backing track and a young man playing the cello against an unspecified city skyline. The next scene is a clean-cut, multi-ethnic crowd of marching young people. Smiling and fist-pumping under their fedora hats and headscarves, the students are waving signs with slogans including ‘Join the conversation’ and ‘Peace’. As the music crescendos, the students face off in front of a silent line of police. A smiling Jenner then places a can of Pepsi in the muscled hands of a young male officer dressed in an American-style police uniform. A moment of shock. Silence. Then, inexplicably, the protesters burst forth in cheers and dancing.

year: 2022-06

title: When loanwords are not lone words: Using networks and hypergraphs to explore Māori loanwords in New Zealand English

authors: Trye, David

abstract: Networks are being used to model an increasingly diverse range of realworld phenomena. This paper introduces an exploratory approach to studying loanwords in relation to one another, using networks of co-occurrence. While traditional studies treat individual loanwords as discrete items, we show that insights can be gained by focusing on the various loanwords that co-occur within each text in a corpus, especially when leveraging the notion of a hypergraph. Our research involves a case-study of New Zealand English (NZE), which borrows Indigenous Māori words on a large scale. We use a topic-constrained corpus to show that: (i) Māori loanword types tend not to occur by themselves in a text; (ii) infrequent loanwords are nearly always accompanied by frequent loanwords; and (iii) it is not uncommon for texts to contain a mixture of listed and unlisted loanwords, suggesting that NZE is still riding a wave of borrowing importation from Māori.

text: When loanwords are not lone words: Using networks and hypergraphs to explore Māori loanwords in New Zealand English Networks are being used to model an increasingly diverse range of realworld phenomena. This paper introduces an exploratory approach to studying loanwords in relation to one another, using networks of co-occurrence. While traditional studies treat individual loanwords as discrete items, we show that insights can be gained by focusing on the various loanwords that co-occur within each text in a corpus, especially when leveraging the notion of a hypergraph. Our research involves a case-study of New Zealand English (NZE), which borrows Indigenous Māori words on a large scale. We use a topic-constrained corpus to show that: (i) Māori loanword types tend not to occur by themselves in a text; (ii) infrequent loanwords are nearly always accompanied by frequent loanwords; and (iii) it is not uncommon for texts to contain a mixture of listed and unlisted loanwords, suggesting that NZE is still riding a wave of borrowing importation from Māori.

year: 2023