Pattern Recognition in Medical Image Diagnosis

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誤差逆伝播ネットワークによる重ね書き記憶

We propose a novel neural network for incremental learning tasks where networks are required to learn new knowledge without forgetting the old one. An essential core of the proposed neural learning structure is a transferring scheme from short-term memory (STM) into long-term memory (LTM) as in brains by using dynamic changing weights. As the number of LTMs increases, a new network structure is superimposed on the previous one without disturbing the past LTMs by introducing a lateral inhibition mechanism. Superiority of the proposed neural structure to the conventional backpropagation networks is proven with respect to the learning ability

Design of an Error-Based Adaptive Controller for a Flexible Robot Arm Using Dynamic Pole Motion Approach

Design of an adaptive controller for complex dynamic systems is a big challenge faced by the researchers. In this paper, we introduce a novel concept of dynamic pole motion (DPM) for the design of an error-based adaptive controller (E-BAC). The purpose of this novel design approach is to make the system response reasonably fast with no overshoot, where the system may be time varying and nonlinear with only partially known dynamics. The E-BAC is implanted in a system as a nonlinear controller with two dominant dynamic parameters: the dynamic position feedback and the dynamic velocity feedback. For illustrating the strength of this new approach, in this paper we give an example of a flexible robot with nonlinear dynamics. In the design of this feedback adaptive controller, parameters of the controller are designed as a function of the system error. The position feedback Kp(e,t) and the velocity feedback Kv(e,t) are continuously varying and formulated as a function of the system error e(t). This approach for formulating the adaptive controller yields a very fast response with no overshoot

Quantitative activation-induced manganese-enhanced MRI reveals severity of Parkinson’s disease in mice

We demonstrate that activation-induced manganese-enhanced magnetic resonance imaging with quantitative determination of the longitudinal relaxation time (qAIM-MRI) reveals the severity of Parkinson’s disease (PD) in mice. We first show that manganese ion-accumulation depends on neuronal activity. A highly active region was then observed by qAIM-MRI in the caudate-putamen in PD-model mice that was significantly correlated to the severity of PD, suggesting its involvement in the expression of PD symptoms