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The Gated Attractors Learning Instruction Sequences (GALIS) framework, used to solve a card-matching task, as well as the n-back task, appears to be trying to unite high-level symbolic reasoning to neural networks. This is also the goal of the NEF/SPA.

What are the differences in approach and what trade-offs exist?

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