The spreading activation model is a model in connectionist theory sometimes designed to represent how concepts are activated in relation to one another.
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Attempts have been made to model the developmental process of language acquisition in children from a computational angle, leading to both connectionist models.
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Through the influence of Dreyfus's critique and neurophysiological alternative, Merleau-Ponty became associated with neurophysiological, connectionist accounts of cognition.
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All gradient descent learning in connectionist models involves changing each weight by the partial derivative of the error surface with respect to the weight.
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Connectionist theories is a pattern-learning procedure and defines language as a system composed of smaller subsystems or patterns of sound or meaning.
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The connectionist model evolved out of Parallel Distributed Processing framework that formulates a metatheory from which specific models can be generated for specific applications.
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A very common strategy in connectionist learning methods is to incorporate gradient descent over an error surface in a space defined by the weight matrix.
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In connectionist systems, representations are distributed across all the nodes and connection weights of the system and thus are said to be sub symbolic.
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In the philosophy of mind, he is known for his attempts to apply connectionist models of coherence to theories of human thought and action.
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Many connectionist models of the brain have been developed in which the processes of language learning and other forms of representation are highly distributed and parallel.