Working memory (WM) is an important cognitive function that is necessary to perform our daily activities. The present review briefly describes the most accepted models underlying WM and the neural networks involved in its processing. The review focuses on how the neurophysiological mechanisms develop with age in the periods from childhood to adolescence and young adulthood. Studies using behavioral, neuroimaging, and electrophysiological techniques showed the progress of WM throughout the development. The present review focuses on the neurophysiology of the basic processes underlying WM operations, as indicated by electroencephalogram-derived signals, in order to take advantage of the excellent time resolution of this technique. Children and adults use similar cerebral mechanisms and areas to encode, recognize, and keep the stimuli in memory and update the WM contents, although adults rely more on anterior sites. The possibility that a functional reorganization of WM brain processing occurs around the adolescent period is suggested, and would partly justify the high prevalence of the emergence of mental pathology in the adolescent period.

Background: The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old).Results: The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0–20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults.Conclusions: The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages.