Many recent studies find striking similarities between representations in biological brains
and artificial neural networks
trained to solve analogous tasks.
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When we then compared network distances using fixed point topology, we found that there was no real difference across architecture (e), suggesting the topology may be universal.pic.twitter.com/yMCy4Ds1qe
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Linearization of the dynamics reveals a common motif with some small differences across architectures. The common motif is a nearly linear solution to produce the oscillations. The varied motif is a small degree of nonlinearity used to generate the oscillations.pic.twitter.com/Uj1a6GTLZz
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We hope this kind of in silico
study helps advance a discussion about the use of ANNs in neuroscience. There are more examples in the preprint https://arxiv.org/abs/1907.08549 .Prikaži ovu nit
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and universality
in the solutions across different RNN architectures, with the geometry of representations tending to be more varied and the dynamics tending to be more universal.
. To do this, we used tools from dynamical systems theory (fixed points and linearization) to extract a simple dynamical portrait for each network.