Insulin concentrations in cerebellum and body balance in diabetic male rats: Aerobic training effects

Brain insulin has had widespread metabolic, neurotrophic, and neuromodulatory functions and has been involved in the central regulation of food intake and body weight, learning and memory, neuronal development, and neuronal apoptosis. Purpose: The present study investigated the role of swimming training on cerebral metabolism on insulin concentrations in cerebellum and the body balance performance of diabetic rats. Methods: Forty Male Wistar rats were divided in four groups: sedentary control (SC), trained control (TC), sedentary diabetic (SD), and trained diabetic (TD). Diabetes was induced by alloxan (32 mg kg b.w.), single dose injection. The mean blood glucose of diabetic groups was 367 ± 40 mg/dl. Training program consisted in swimming 5 days/week, 1 h/day, 8 weeks, supporting a workload corresponding to 90% of maximal lactate steady state (MLSS). For the body balance testing rats were trained to traverse for 5 min daily for 5-7 days. All dependent variables were analyzed by one-way analysis of variance (ANOVA) and a significance level of p < 0.05 was used for all comparisons. Results: The body balance testing scores were different between groups. Insulin concentrations in cerebellum were not different between groups. Conclusion: It was concluded that in diabetic rats, aerobic training does not induce alterations on cerebellum insulin but induces important metabolic, hormonal and behavioral alterations which are associated with an improvement in glucose homeostasis, serum insulin concentrations and body balance. © 2013 Elsevier Inc.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.