Mamalian-like neural networks for dynamic information processing and its learning algorithm

(1)从数学上研究了什么样的内部表征可以被三层馈入神经网络的单个输出单元分离。给出了输入空间分区按输出单元分类的拓扑描述的充分必要条件。然后，提出了一种有效的算法来检查输入空间的给定分区是否导致输出单元的线性分离。(3)这些论文提高了机器学习中PAC可学习性中可靠泛化所需的样本复杂度。通过引入一种病态学习算法，使经验误差最小化所获得的候选网络实现的误差更大，我们可以改进样本复杂度的顺序，而以前的方法寻求在整个构型空间上的一致误差。引入小于或等于可修改系统参数个数的概念类的基本VC维来代替传统的VC维分析来计算泛化误差。嘈杂的学习也得到了治疗。(4)本文提出了一种非常简单的递归神经网络（VSRN）结构，它是一个三层网络，隐藏层只包含自环递归连接。网络动态解释了循环连接的作用，其功能将通过学习有限的例子（如哺乳动物的动作）来获得。在学习过程中，网络自动获得了哺乳动物听觉系统中观察到的一些特征功能。它们包括开神经元、关神经元和开-关神经元。该体系结构可以利用这些特征函数进行实时的音素定位。通过仿真实验研究了该算法的识别性能。

(1)It is mathematically investigated as to what kind of internal representations are separable by a single output unit of a three layr feednext neural network. A topologically described necessary and sufficient condition is shown for partitions of input spaces to be classified by the output unit. Then an efficient algorithm is proposed for checking if a given partition of the input space is resulted in linear separation at the output unit.(2)(3)These papers improves the sample complexity needed for reliable generalization in the PAC learnability in machine learning. By introducing an ill-posed learning algorithm which gives error worse over the candidates of network realizarions that are attained by minimizing empirical error, we can refine the order of the sample complexity, whereas the previous methods seek the uniform error over the whole configuration space. Essential VC dimension of concept classes, which is smaller than or equal to the number of modifiable system parameters, is introduced for calculating the generalization error instead of the traditional VC dimension analysis. Noisy learning is also treated.(4)In this paper we propose a very simple recurrent neural network(VSRN)architecture which is a three-layr network and contains only self-loop recurrent connections in the hidden layr. The role of the recurrent connection is explained by the network dynamic and its function will be acquired by learning from finite examples like a mamalian action. Through the learning process some characteristic functions observed in the mamalian auditory systems are found automatically acquired by the network. These contain on-neuron, off-neuron and on-off-neuron. This architecture can perform phoneme spotting in real time by utilizing these characteristic functions. Some simulation experiments are done to investigate the recognition performance.