Analysis of neural excitability and oscillations
Methods in neuronal modeling
Binding hierarchies: a basis for dynamic perceptual grouping
Neural Computation
Temporal reasoning for planning and scheduling
ACM SIGART Bulletin
Temporal reasoning in Timegraph I–II
ACM SIGART Bulletin
A metric time-point and duration-based temporal model
ACM SIGART Bulletin
Original Contribution: Hamiltonian dynamics of neural networks
Neural Networks
A temporal connectionist approach to natural language
ACM SIGART Bulletin
A survey on temporal reasoning in artificial intelligence
AI Communications
Maintaining knowledge about temporal intervals
Communications of the ACM
Sequence Learning - Paradigms, Algorithms, and Applications
Prediction of oil well production: A multiple-neural-network approach
Intelligent Data Analysis
A Testbed for Neural-Network Models Capable of Integrating Information in Time
Anticipatory Behavior in Adaptive Learning Systems
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After the revival of interest in connectionism in the eighties and its successful application to pattern recognition problems, the time has come to consider its role in the field of temporal processing. We present here a general overview of the field of temporal neural networks. In order to give a broad framework to this presentation, we first present general properties of time that are used by AI models. This sets out the properties of time: - on its own, - with respect to a problem, - with respect to a model. We then present a short summary of time processing in symbolic AI. The main part of this article, a classification of temporal neural models, is introduced by a short presentation of basic connectionist models. This classification is then made and several relevant examples are presented. We conclude the article with underlining the difference between temporal reasoning and neural temporal processing, and give an introduction to the following papers of this Sigart special section.