Intrinsic excitabilities of acutely isolated medial vestibular nucleus (MVN) neurons of rats with normal labyrinth and with undergoing vestibular compensation from 30 min to 24 h after unilateral vestibular deafferent...Intrinsic excitabilities of acutely isolated medial vestibular nucleus (MVN) neurons of rats with normal labyrinth and with undergoing vestibular compensation from 30 min to 24 h after unilateral vestibular deafferentation (UVD) were compared. In control rats, proportions of type A and B cells were 30 and 70%, respectively, however, the proportion of type A cells increased following UVD. Bursting discharge and irregular firing patterns were recorded from 2 to 12 h post UVD. The spontaneous discharge rate of neurons in the ipsilesional MVN increased significantly at 2 h post-UVD and remained high until 12 h post-UVD in both type A and type B cells. After-hyperpolarization (AHP) of the MVN neurons decreased significantly from 2 h post-UVD in both types of cells. These results suggest that the early stage of vestibular compensation after peripheral neurectomy is associated with an increase in intrinsic excitability due to reduction of AHP in MVN neurons.展开更多
Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-valued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation function of...Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-valued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation function of MVN is not differentiable. Therefore, we can not apply backpropagation when constructing multilayer structures. In this paper, we propose a new neuron model, MVN-sig, to simulate the mechanism of MVN with differentiable activation function. We expect MVN-sig to achieve higher performance than MVN. We run several classification benchmark datasets to compare the performance of MVN-sig with that of MVN. The experimental results show a good potential to develop a multilayer networks based on MVN-sig.展开更多
Neurons with complex-valued weights have stronger capability because of their multi-valued threshold logic. Neurons with such features may be suitable for solution of different kinds of problems including associative ...Neurons with complex-valued weights have stronger capability because of their multi-valued threshold logic. Neurons with such features may be suitable for solution of different kinds of problems including associative memory,image recognition and digital logical mapping. In this paper,robustness or tolerance is introduced and newly defined for this kind of neuron ac-cording to both their mathematical model and the perceptron neuron's definition of robustness. Also,the most robust design for basic digital logics of multiple variables is proposed based on these robust neurons. Our proof procedure shows that,in robust design each weight only takes the value of i or -i,while the value of threshold is with respect to the number of variables. The results demonstrate the validity and simplicity of using robust neurons for realizing arbitrary digital logical functions.展开更多
文摘Intrinsic excitabilities of acutely isolated medial vestibular nucleus (MVN) neurons of rats with normal labyrinth and with undergoing vestibular compensation from 30 min to 24 h after unilateral vestibular deafferentation (UVD) were compared. In control rats, proportions of type A and B cells were 30 and 70%, respectively, however, the proportion of type A cells increased following UVD. Bursting discharge and irregular firing patterns were recorded from 2 to 12 h post UVD. The spontaneous discharge rate of neurons in the ipsilesional MVN increased significantly at 2 h post-UVD and remained high until 12 h post-UVD in both type A and type B cells. After-hyperpolarization (AHP) of the MVN neurons decreased significantly from 2 h post-UVD in both types of cells. These results suggest that the early stage of vestibular compensation after peripheral neurectomy is associated with an increase in intrinsic excitability due to reduction of AHP in MVN neurons.
文摘Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-valued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation function of MVN is not differentiable. Therefore, we can not apply backpropagation when constructing multilayer structures. In this paper, we propose a new neuron model, MVN-sig, to simulate the mechanism of MVN with differentiable activation function. We expect MVN-sig to achieve higher performance than MVN. We run several classification benchmark datasets to compare the performance of MVN-sig with that of MVN. The experimental results show a good potential to develop a multilayer networks based on MVN-sig.
文摘Neurons with complex-valued weights have stronger capability because of their multi-valued threshold logic. Neurons with such features may be suitable for solution of different kinds of problems including associative memory,image recognition and digital logical mapping. In this paper,robustness or tolerance is introduced and newly defined for this kind of neuron ac-cording to both their mathematical model and the perceptron neuron's definition of robustness. Also,the most robust design for basic digital logics of multiple variables is proposed based on these robust neurons. Our proof procedure shows that,in robust design each weight only takes the value of i or -i,while the value of threshold is with respect to the number of variables. The results demonstrate the validity and simplicity of using robust neurons for realizing arbitrary digital logical functions.
