Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex.
Phys Rev E Stat Nonlin Soft Matter Phys
; 72(6 Pt 1): 061910, 2005 Dec.
Article
em En
| MEDLINE
| ID: mdl-16485977
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Sono REM
/
Relógios Biológicos
/
Córtex Cerebral
/
Memória
/
Modelos Neurológicos
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Rede Nervosa
/
Neurônios
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article