Traditionally, VLSI implementations of spiking neural nets have featured large neuron counts for fixed computations or small exploratory, configurable nets. This paper presents the system architecture of a large configurable neural net system employing a dedicated mapping algorithm for projecting the targeted biology-analog nets and dynamics onto the hardware with its attendant constraints. Key...

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificiaa intelligence presents major problems as well. We claim that a promising way out of this impas...

In this paper an hybrid system and a hierarchical neural net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of non-neural clustering algorithms directly to the output of a neural net; the second one is based on a multi-layer organization of neural units. Both methods are a substantial improvement with res...

Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) speech recognizers. Both attempt to improve speech pattern discrimination while retaining the temporal processing advantages of HMMs. One approach used neural nets to provide second-stage discrimination following an HMM recognizer. On a small vocabulary task, Radial Basis Function (RBF) and back-p...

By network reliability, we mean the probability of survival of a network for a speciÞed period of time. Obtaining this probability boils down to the formulation and the evaluation of the reliability polynomial. This is a formidable task for networks with a large number of nodes, and an almost impossible task if the node life-times cascade and interact. Consequently, implementing a mathematical ...

We present a McCulloch-Pitts neural net to recognize even linear languages. The language class is studied in order to define the net topology. Finally, the equivalence between the language class and the languages recognized by the net is proved.