Effects of exposure to 50 Hz magnetic field of 1 mT on the performance of detour learning task by chicks

In the present study, we examined the effects of exposure to an extremely low-frequency magnetic field of 1 mT intensity on learning and memory in Lohmann brown domestic chicks using detour learning task. These results show that 20 h/day exposure to a low

Lead exposure through gestation-only caused long-term learning/memory deficits in young adult offspring

Numerous observations in clinical and preclinical studies indicate that the developing brain is particular sensitive to lead (Pb)'s pernicious effects. However, the effect of gestation-only Pb exposure on cognitive functions at maturation has not been studied. We investigated the potential effects of three levels of Pb exposure (low, middle, and high Pb: 0.03%, 0.09%, and 0.27% of lead acetate-containing diets) at the gestational period on the spatial memory of young adult offspring by Morris water maze spatial learning and fixed location/visible platform tasks. Our results revealed that three levels of Pb exposure significantly impaired memory retrieval in male offspring, but only female offspring at low levels of Pb exposure showed impairment of memory retrieval. These impairments were not due to the gross disturbances in motor performance and in vision because these animals performed the fixed location/visible platform task as well as controls, indicating that the specific aspects of spatial learning/memory were impaired. These results suggest that exposure to Pb during the gestational period is sufficient to cause long-term learning/memory deficits in young adult offspring. (C) 2003 Elsevier Inc. All rights reserved.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

A VSLMS Style Tap-length Learning Algorithm for Structure Adaptation

Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller., lower power consumption and higher computational complexity output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style Variable Tap-Length Algorithm (also called Fractional Tap-Length Algorithm or FT Algorithm) proposed by Y.Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called 'VSLMS' (Variable Step-size LMS) Style Tap-Length Learning Algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.