The society of mind
A recurrent neural network for real-time matrix inversion
Applied Mathematics and Computation
The evolution of evolvability in genetic programming
Advances in genetic programming
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Machine Learning - Special issue on inductive transfer
Adaptive Behavior
Reinforcement learning with self-modifying policies
Learning to learn
Adaptive internal state space construction method for reinforcement learning of a real-world agent
Neural Networks - Special issue on organisation of computation in brain-like systems
The theory of evolution strategies
The theory of evolution strategies
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Multiple model-based reinforcement learning
Neural Computation
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Polychronization: Computation with Spikes
Neural Computation
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive mixtures of local experts
Neural Computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
A (very) brief introduction to fluid construction grammar
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Evolino: hybrid neuroevolution / optimal linear search for sequence learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving deep memory POMDPs with recurrent policy gradients
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Unify and merge in fluid construction grammar
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro-nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cognitive architectures. Replicators overcome some limitations of selectionist models of neuronal search. Hebbian learning is combined with replication to structure exploration on the basis of associations learned in the past. Neuromodulatory gating of sets of bistable neurons allows patterns of activation to be copied with mutation. If the probability of copying a set is related to the utility of that set, then an evolutionary algorithm can be implemented at rapid timescales in the brain. Populations of neuronal replicators can undertake a more rapid and stable search than can be achieved by serial modification of a single solution. Hebbian learning added to neuronal replication allows a powerful structuring of variability capable of learning the location of a global optimum from multiple previously visited local optima. Replication of solutions can solve the problem of catastrophic forgetting in the stability-plasticity dilemma. In short, neuronal replication is essential to explain several features of flexible cognition. Predictions are made for the experimental validation of the neuronal replicator hypothesis.