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The Hop field model is a well-known dynamic associative-memory model. In this paper, we investigate various aspects of the Hop field model for associative memory. We conduct a systematic simulation investigation of several storage algorithms for Hop field networks, and conclude that the perceptron learning based storage algorithms can achieve much better storage capacity than the Hebbian learning based algorithms. [ABSTRACT FROM PUBLISHER]

This paper proposes a new type of hybrid network that can classify the dynamic temporal behavior of vectors. The proposed system consists of Self-organizing map (SOM) and a supervised learning network with feedback. The SOM performs stimulus classification and a supervised network identifies the dynamic behavior of input vectors. The supervised network is trained by using Hebbian learning. To demonstrate the feasibility of the proposed network, it is applied to recognize dynamic hand gestures. Experimental results show that the system can recognize nine gestures with the accuracy of 93%. [ABSTRACT FROM PUBLISHER]

It is pointed out that recurrent lateral connectivity in a layer of processing units gives rise to a rich variety of nonlinear response properties, such as overall gain control, emergent periodic response on a preferred spatial scale (collective excitations), and distributed winner-take-all response. This diversity of response properties is observed in several different classes of simple network architectures, including the additive linear network, the additive sigmoidal network, and the nonlinear shunting network. When Hebbian learning is coupled with network dynamics, these models have been shown to support the development of modular connectivity structures analogous to cortical columns [ABSTRACT FROM PUBLISHER]

An isolated routing algorithm for adaptive dynamic routing in communications networks that is based on a neural network approach has been developed. A neural network is located within each site of a communications network, and it uses the patterns of incoming traffic to maintain and dynamically update an internal representation of the global topology of the communications network. The information stored in the neural network models is updated using various routing paradigms, including Hebbian learning. The scheme has been implemented in a software simulation running on a Sun workstation [ABSTRACT FROM PUBLISHER]