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
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Recurrent network model of the neural mechanism of short-term active memory
Neural Computation
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Neural Networks - 2005 Special issue: IJCNN 2005
Emergence of attention within a neural population
Neural Networks
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
A simple model of prefrontal cortex function in delayed-response tasks
Journal of Cognitive Neuroscience
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During the past decades, the symbol grounding problem, as has been identified by Harnard [Harnard, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42, 335-346], became a prominent problem in the cognitive science society. The idea that a symbol is much more than a mere meaningless token that can be processed through some algorithm, sheds new light on higher brain functions such as language and cognition. We present in this article a computational framework that may help in our understanding of the nature of grounded representations. Two models are briefly introduced that aim at emphasizing the difference we make between implicit and explicit representations.