Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Hebbian learning of pulse timing in the Barn Owl auditory system
Pulsed neural networks
The handbook of brain theory and neural networks
Generative character of perception: a neural architecture for sensorimotor anticipation
Neural Networks - Special issue on organisation of computation in brain-like systems
Solving Non-Markovian Control Tasks with Neuro-Evolution
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
The role of temporal parameters in a thalamocortical model of analogy
IEEE Transactions on Neural Networks
Evolution of recollection and prediction in neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Delay in the nervous system is a serious issue for an organism that needs to act in real time. For example, during the time a signal travels from a peripheral sensor to the central nervous system, a moving object in the environment can cover a significant distance which can lead to critical errors in the effect of the corresponding motor output. This paper proposes that facilitating synapses which show a dynamic sensitivity to the changing input may play an important role in compensating for neural delays, through extrapolation. The idea was tested in a modified 2D pole-balancing problem which included sensory delays. Within this domain, we tested the behavior of recurrent neural networks with facilitatory neural dynamics trained via neuroevolution. Analysis of the performance and the evolved network parameters showed that, under various forms of delay, networks utilizing extrapolatory dynamics are at a significant competitive advantage compared to networks without such dynamics. In sum, facilitatory (or extrapolatory) dynamics can be used to compensate for delay at a single-neuron level, thus allowing a developing nervous system to stay in touch with the present environmental state.