#tweeprint
Universality and individuality in neural dynamics across large populations of recurrent networks
https://arxiv.org/abs/1907.08549 .
With fantastic collaborators @niru_m, @ItsNeuronal, @MattGolub_Neuro, @SuryaGanguli.
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Next, we asked if we would reach the same conclusion if we compared the underlying dynamics
. To do this, we used tools from dynamical systems theory (fixed points and linearization) to extract a simple dynamical portrait for each network.pic.twitter.com/ARueBiVffQ
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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 artificial neural networks
trained to solve analogous tasks.


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.