| Autor: |
Steyn-Ross ML; Department of Physics and Electronic Engineering, Private Bag 3105, University of Waikato, Hamilton, New Zealand. msr@waikato.ac.nz, Steyn-Ross DA, Sleigh JW, Wilson MT, Wilcocks LC |
| Jazyk: |
angličtina |
| Zdroj: |
Physical review. E, Statistical, nonlinear, and soft matter physics [Phys Rev E Stat Nonlin Soft Matter Phys] 2005 Dec; Vol. 72 (6 Pt 1), pp. 061910. Date of Electronic Publication: 2005 Dec 16. |
| DOI: |
10.1103/PhysRevE.72.061910 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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