IFIBIO HOUSSAY   25014
INSTITUTO DE FISIOLOGIA Y BIOFISICA BERNARDO HOUSSAY
Unidad Ejecutora - UE
artículos
Título:
The spacing effect for structural synaptic plasticity provides specificity and precision in plastic changes
Autor/es:
BRUCE GELB; LORENA RELA; ALVARO SAN MARTIN; MARIO RAFAEL PAGANI
Revista:
JOURNAL OF NEUROSCIENCE
Editorial:
SOC NEUROSCIENCE
Referencias:
Lugar: Washington; Año: 2017 vol. 37 p. 4992 - 5007
ISSN:
0270-6474
Resumen:
In contrast to trials of training without intervals (massed training), training trials spaced over time (spaced training) induce a more persistent memory identified as long-term memory (LTM). This phenomenon known as "the spacing effect for memory" is poorly understood. LTM is supported by structural synaptic plasticity; however, how synapses integrate spaced stimuli remains elusive. Here, we analyzed events of structural synaptic plasticity at the single synapse level after distinct patterns of stimulation in motoneurons of Drosophila We found that the spacing effect is a phenomenon detected at synaptic level, which determine the specificity and the precision in structural synaptic plasticity. Whereas a single pulse of stimulation (massed) induced structural synaptic plasticity, the same amount of stimulation divided in three spaced stimuli completely prevented it. This inhibitory effect was determined by the length of the inter-stimulus intervals. The inhibitory effect of the spacing was lost by suppressing the activity of Ras or MAPK, while the overexpression of Ras-WT enhanced it. Moreover, dividing the same total time of stimulation into five or more stimuli produced a higher precision in the number of events of plasticity. Ras mutations associated with intellectual disability abolish the spacing effect and lead neurons to decode distinct stimulation patterns as massed stimulation. This evidence suggests that the spacing effect for memory may results from the effect of the spacing in synaptic plasticity, which appear to be a property not limited to neurons involved in learning and memory. We propose a model of spacing-dependent structural synaptic plasticity.