SummaryWe analyze the effect of chronic undernourishment on extensor digitorum longus (EDL) muscle maturation in the rat. Cytochrome c oxidase (COX) and alkaline ATPase histoenzymatic techniques were used to determine the relative proportion of different fiber types (oxidative/glycolytic and type I, IIa/IId, or IIb, respectively) and their cross-sectional area in control and undernourished EDL muscles at several postnatal (PN) ages. From PN days 15 to 45, undernourished EDL muscles showed predominance of oxidative and type IIa/IId fibers, but from PN days 60 to 90, there were a larger proportion of oxidative fibers and an equal proportion of type IIa/IId and IIb fibers. Meanwhile, in adult stages (from PN days 130-365), the relative proportion of fiber types in control and undernourished EDL muscles showed no significant differences. In addition, from PN days 15 to 90, there was a significant reduction in the cross-sectional area of all fibers (slow: 13-53%; intermediate: 24-74%; fast: 9-80%) but no differences from PN days 130 to 365. It is suggested that chronic undernourishment affects the maturation of fast-type muscle fibers only at juvenile stages (from PN days 15-45) and the probable occurrence of adaptive mechanisms in muscle fibers, allowing adult rats to counterbalance the alterations provoked by chronic food deprivation. (J Histochem Cytochem 61:372-381, 2013)

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This work deals with the presentation of a spiking neural network as a means for efficiently solving the reduction of dimensionality of data in a nonlinear manner. The underneath neural model, which can be integrated as neuromorphic hardware, becomes suitable for intelligent processing in edge computing within Internet of Things systems. In this sense, to achieve a meaningful performance with a low complexity one-layer spiking neural network, the training phase uses the metaheuristic Artificial Bee Colony algorithm with an objective function from the principals in the machine learning science, namely, the modified Stochastic Neighbor Embedding algorithm. To demonstrate this fact, complex benchmark data were used and the results were compared with those generated by a reference network with continuous-sigmoid neurons. The goal of this work is to demonstrate via numerical experiments another method for training spiking neural networks, where the used optimizer comes from metaheuristics. Therefore, the key issue is defining the objective function, which can relate optimally the information at both sides of the spiking neural network. Certainly, machine learning techniques have advanced in defining efficient loss functions that can become suitable objective function candidates in the metaheuristic training phase. The practicality of these ideas is shown in this article. We use MSE values for evaluating the relative quality of the results and also co-ranking matrices.

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