Chronic intrathecal infusion of T-type calcium channel blockers attenuates CaV3.2 upregulation in nerve-ligated rats.

OBJECTIVE: T-type channel (TCC) CaV3.2 plays a pivotal role in pain transmission. In this study, we examined the effects of intrathecal TCC blockers on CaV3.2 expression in a L5/6 spinal nerve ligation (SNL) pain model. The neurotoxicity of TCC blockers were also evaluated. METHODS: Male Sprague-Dawley rats (200-250 g) were used for right L5/6 SNL to induce neuropathic pain. Intrathecal infusion of saline or TCC blockers [mibefradil (0.7 µg/h) or ethosuximide (60 µg/h)] was started after surgery for 7 days. Fluorescent immunohistochemistry and Western blotting were used to determine the expression pattern and protein level of CaV3.2. Hematoxylin-eosin and toluidine blue staining were used to evaluate the neurotoxicity of tested agents. RESULTS: Seven days after SNL, CaV3.2 protein levels were upregulated in ipsi-lateral L5/6 spinal cord and dorsal root ganglia (DRG) in immunofluorescence and Western blotting studies. Compared with the saline-treated group, rats receiving mibefradil or ethosuximide showed significant lower CaV3.2 expression in the spinal cord and DRG. No obvious histopathologic change in hematoxylin-eosin and toluidine blue staining were observed in all tested groups. CONCLUSION: In this study, we demonstrate that SNL-induced CaV3.2 upregulation in the spinal cord and DRG was attenuated by intrathecal infusion of mibefradil or ethosuximide. No obvious neurotoxicity effects were observed in all the tested groups. Our data suggest that continuous intrathecal infusion of TCC blockers may be considered as a promising alternative for the treatment of nerve injury-induced pain.

Toward a new task assignment and path evolution (TAPE) for missile defense system (MDS) using intelligent adaptive SOM with recurrent neural networks (RNNs).

In this paper, a new adaptive self-organizing map (SOM) with recurrent neural network (RNN) controller is proposed for task assignment and path evolution of missile defense system (MDS). We address the problem of N agents (defending missiles) and D targets (incoming missiles) in MDS. A new RNN controller is designed to force an agent (or defending missile) toward a target (or incoming missile), and a monitoring controller is also designed to reduce the error between RNN controller and ideal controller. A new SOM with RNN controller is then designed to dispatch agents to their corresponding targets by minimizing total damaging cost. This is actually an important application of the multiagent system. The SOM with RNN controller is the main controller. After task assignment, the weighting factors of our new SOM with RNN controller are activated to dispatch the agents toward their corresponding targets. Using the Lyapunov constraints, the weighting factors for the proposed SOM with RNN controller are updated to guarantee the stability of the path evolution (or planning) system. Excellent simulations are obtained using this new approach for MDS, which show that our RNN has the lowest average miss distance among the several techniques.

A new learning algorithm for a fully connected neuro-fuzzy inference system.

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN).

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems.

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.