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- W2611284055 abstract "Recent models of associative long term memory (LTM) have emerged in the field of neuro-inspired computing. These models have interesting properties of error correction, robustness, storage capacity and retrieval performance. In this context, we propose a connectionist model of written word recognition with correction properties, using associative memories based on neural cliques. Similarly to what occurs in human language, the model takes advantage of the combination of phonological and orthographic information to increase the retrieval performance in error cases. Therefore, the proposed architecture and principles of this work could be applied to other neuro-inspired problems that involve multimodal processing, in particular for language applications." @default.
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- W2611284055 date "2018-01-01" @default.
- W2611284055 modified "2023-09-27" @default.
- W2611284055 title "A Connectionist Model of Reading with Error Correction Properties" @default.
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- W2611284055 doi "https://doi.org/10.1007/978-3-319-93782-3_22" @default.
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