The nature of statistical learning theory
The nature of statistical learning theory
Computation of smooth optical flow in a feedback connected analog network
Proceedings of the 1998 conference on Advances in neural information processing systems II
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Movement prediction from real-world images using a liquid state machine
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Movement prediction from real-world images using a liquid state machine
Applied Intelligence
Improving reservoirs using intrinsic plasticity
Neurocomputing
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
Topological constraints and robustness in liquid state machines
Expert Systems with Applications: An International Journal
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We propose an alternative paradigm for processing time-varying visual inputs, in particular for tasks involving temporal and spatial integration, which is inspired by hypotheses about the computational role of cortical microcircuits. Since detailed knowledge about the precise structure of the microcircuit is not needed for that, it can in principle also be implemented with partially unknown or faulty analog hardware. In addition, this approach supports parallel real-time processing of time-varying visual inputs for diverse tasks, since different readouts can be trained to extract concurrently from the same microcircuit completely different information components.