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- W3048749966 abstract "Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons." @default.
- W3048749966 created "2020-08-18" @default.
- W3048749966 creator A5036179371 @default.
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- W3048749966 date "2020-08-12" @default.
- W3048749966 modified "2023-09-29" @default.
- W3048749966 title "Engineering recurrent neural networks from task-relevant manifolds and dynamics" @default.
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- W3048749966 doi "https://doi.org/10.1371/journal.pcbi.1008128" @default.
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