Unified theories of cognition
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Neural Networks - Special issue on organisation of computation in brain-like systems
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Human Problem Solving
Polychronization: Computation with Spikes
Neural Computation
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Fodor and Pylyshyn in their 1988 paper denounced the claims of the connectionists, claims that continue to percolate through neuroscience. In they proposed that a physical symbol system was necessary for open-ended cognition. What is a physical symbol system, and how can one be implemented in the brain? A way to understand them is by comparison of thought to chemistry. Both have systematicity, productivity and compositionality, elements lacking in most computational neuroscience models. To remedy this woeful situation, I examine cognitive architectures capable of open-ended cognition, and think how to implement them in a neuronal substrate. I motivate a cognitive architecture that evolves physical symbol systems in the brain. In Part 2 of this paper pair develops this architecture and proposes a possible neuronal implementation.