Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
The conscious mind: in search of a fundamental theory
The conscious mind: in search of a fundamental theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolving neural networks through augmenting topologies
Evolutionary Computation
Contour integration and segmentation with self-organized lateral connections
Biological Cybernetics
Facilitating neural dynamics for delay compensation and prediction in evolutionary neural networks
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Efficient evolution of neural network topologies
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
IEEE Transactions on Neural Networks
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A large number of neural network models are based on a feedforward topology (perceptrons, backpropagation networks, radial basis functions, support vector machines, etc.), thus lacking dynamics. In such networks, the order of input presentation is meaningless (i.e., it does not affect the behavior) since the behavior is largely reactive. That is, such neural networks can only operate in the present, having no access to the past or the future. However, biological neural networks are mostly constructed with a recurrent topology, and recurrent (artificial) neural network models are able to exhibit rich temporal dynamics, thus time becomes an essential factor in their operation. In this paper, we will investigate the emergence of recollection and prediction in evolving neural networks. First, we will show how reactive, feedforward networks can evolve a memory-like function (recollection) through utilizing external markers dropped and detected in the environment. Second, we will investigate how recurrent networks with more predictable internal state trajectory can emerge as an eventual winner in evolutionary struggle when competing networks with less predictable trajectory show the same level of behavioral performance. We expect our results to help us better understand the evolutionary origin of recollection and prediction in neuronal networks, and better appreciate the role of time in brain function.