Dynamic Neuronal Ensembles: Neurobiologically Inspired Discrete Event Neural Networks

  • S. Vahie


The past decade has seen a resurgence in the research, development and application of artificial neural networks. This is due, in part, to the adaptive and autonomous nature of these computational mechanisms that lend themselves to real-world application. Although the early motivation for the development of artificial neural networks was inspired by biological neural networks, today’s artificial neural networks have little or nothing in common with their biological counterparts. However, recent advances in neuroscience have uncovered some of the mechanisms responsible for the adaptive, communicative, and computational power of biological neural networks. Such neurobiological theories, though not always supported by mathematical formalisms, provide new insights and solutions to some of the problems facing today’s neural network formalisms. This paper highlights some of the benefits of incorporating biological mechanisms in traditional and current state-of-the-art neural networks. We employ the discrete event abstractions using the DEVS formalism to incorporate such mechanisms. Due to their dynamic topology and their analogy to biology, we call them dynamic neuronal ensembles. An application to biological defensive response demonstrates the validity of the simulation model. Potential applications to flexible control systems are discussed.


Motor Neuron Sensory Neuron Classical Conditioning Hebbian Learning Dynamic Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 2001

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  • S. Vahie

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