Synchronization and array-enhanced resonances in delayed coupled neuronal network with channel noise.

This paper studies the combined effect of transmission delay and channel fluctuations on population behaviors of an excitatory Erdös-Rényi neuronal network. First, it is found that the network reaches a perfect spatial temporal coherence at a suitable membrane size. Such a coherence resonance is stimulus-free and is array-enhanced.

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons.

Universal perceptron and DNA-like learning algorithm for binary neural networks: non-LSBF implementation.

Implementing linearly nonseparable Boolean functions (non-LSBF) has been an important and yet challenging task due to the extremely high complexity of this kind of functions and the exponentially increasing percentage of the number of non-LSBF in the entire set of Boolean functions as the number of input variables increases. In this paper, an algorithm named DNA-like learning and decomposing algorithm (DNA-like LDA) is proposed, which is capable of effectively implementing non-LSBF. The novel algorithm first trains the DNA-like offset sequence and decomposes non-LSBF into logic XOR operations of a sequence of LSBF, and then determines the weight-threshold values of the multilayer perceptron (MLP) that perform both the decompositions of LSBF and the function mapping the hidden neurons to the output neuron.