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Table of Contents
1 Introduction
2 Scenarios of Agents in a Continuously Evolving Environment
3 Neural Networks, Hybrid Automata, and Continuous Time
4 Continuous-Time Neural Networks
Summary
This paper discusses the importance of neural networks modeling continuous time, specifically for synthesizing and analyzing continuous and possibly periodic processes. It addresses scenarios ranging from deductive reasoning tasks to robot behavior on a conveyor belt. The comparison with other neural network models like Fourier Neural Networks, Time-Delay Neural Networks, and Hybrid Automata is provided, highlighting the advantages and limitations of each model. The introduction of Continuous-Time Neural Networks (CTNN) is presented as an enhanced model capable of handling temporal processing explicitly. The CTNN units are defined with four stages: summation, integration, activation, and oscillation, allowing for the modeling of complex behaviors with temporal dependencies.