The Kalman filter: an introduction to concepts
Autonomous robot vehicles
Self-organizing maps
X Vision: a portable substrate for real-time vision applications
Computer Vision and Image Understanding
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A modular architecture for a multi-purpose mobile robot
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Imitation learning with spiking neural networks and real-world devices
Engineering Applications of Artificial Intelligence
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Learning anticipation via spiking networks: application to navigation control
IEEE Transactions on Neural Networks
International Journal of Systems, Control and Communications
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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The prediction of time series is an important task in finance, economy, object tracking, state estimation and robotics. Prediction is in general either based on a well-known mathematical description of the system behind the time series or learned from previously collected time series. In this work we introduce a novel approach to learn predictions of real world time series like object trajectories in robotics. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. The pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. The main advantages of this approach are that due to the nonlinear projection of the input data to a high-dimensional space simple learning algorithms can be used, that the liquid state machine provides temporal memory capabilities and that this kind of computation appears biologically more plausible than conventional methods for prediction. Our results support the idea that learning with a liquid state machine is a generic powerful tool for prediction.