Mahta 💻🧠(@Mahtaao) 's Twitter Profile Photo

Generative Models of Brain Dynamics – A

🎁 After a year of work, our present is freshly out: arxiv.org/abs/2112.12147 🎄

Get a bird's eye view with the synthesis of >200 refs at the intersection of , , and !

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Generative Models of Brain Dynamics – A #review

🎁 After a year of work, our #Xmas present is freshly out: arxiv.org/abs/2112.12147 🎄

Get a bird's eye view with the synthesis of >200 refs at the intersection of #ML, #DynamicalSystems, and #Neuroscience!

A thread…🧵 (1/5)
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MathType(@MathType) 's Twitter Profile Photo

In the 60s, Edward N. Lorenz stumbled upon the concept of chaos. When using early computers to predict weather he realized that small differences in the initial conditions of the system had a huge impact long term: the Butterfly Effect

In the 60s, Edward N. Lorenz stumbled upon the concept of chaos. When using early computers to predict weather he realized that small differences in the initial conditions of the system had a huge impact long term: the Butterfly Effect #DynamicalSystems #ChaosTheory #MathType
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DurstewitzLab(@DurstewitzLab) 's Twitter Profile Photo

In science we often access the same system via many diff. data channels simultaneously, e.g. neural activity and behavior in .
Here we develop a general framework for nonlinear reconstruction based on *multi-modal* RNNs: arxiv.org/abs/2111.02922

In science we often access the same system via many diff. data channels simultaneously, e.g. neural activity and behavior in #neuroscience. #ML
Here we develop a general framework for nonlinear #DynamicalSystems reconstruction based on *multi-modal* RNNs: arxiv.org/abs/2111.02922
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DurstewitzLab(@DurstewitzLab) 's Twitter Profile Photo

Our Perspective on reconstructing computat. system dynamics from neural data finally out in Nature Rev Neurosci!
nature.com/articles/s4158…
We survey generative models that can be trained on time series to mimic the behavior of the neural substrate.

Our Perspective on reconstructing computat. system dynamics from neural data finally out in @NatRevNeurosci!
nature.com/articles/s4158…
We survey generative models that can be trained on time series to mimic the behavior of the neural substrate.
#AI #neuroscience #DynamicalSystems
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DurstewitzLab(@DurstewitzLab) 's Twitter Profile Photo

Can we learn from time series data a dynamical systems model that *generalizes* to unobserved dynamical regimes (basins of attraction), like a good scientific theory should?

Out-of-domain generalization in reconstruction:
arxiv.org/abs/2402.18377


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Can we learn from time series data a dynamical systems model that *generalizes* to unobserved dynamical regimes (basins of attraction), like a good scientific theory should?

Out-of-domain generalization in #DynamicalSystems reconstruction: 
arxiv.org/abs/2402.18377

#AI #ML
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SIAM(@TheSIAMNews) 's Twitter Profile Photo

Innovations in —particularly —have yielded new insights into the connection between and . In SIAM News, Qianxiao Li and Weinan E introduce some recent lines of work at this intersection. Read more: sinews.siam.org/Details-Page/m…

Innovations in #MachineLearning—particularly #DeepLearning—have yielded new insights into the connection between #DynamicalSystems and #DataScience. In SIAM News, Qianxiao Li and Weinan E introduce some recent lines of work at this intersection. Read more: sinews.siam.org/Details-Page/m…
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SIAM(@TheSIAMNews) 's Twitter Profile Photo

The dynamic mode decomposition is a powerful technique for the discovery of from high-dimensional . In SIAM News, J. Nathan Kutz, Steven Brunton, Bing Wen Brunton, and Joshua L. Proctor present an excerpt from their book on DMD. Read more: sinews.siam.org/Details-Page/d…

The dynamic mode decomposition is a powerful technique for the discovery of #DynamicalSystems from high-dimensional #data. In SIAM News, J. Nathan Kutz, @eigensteve, @bingbrunton, and Joshua L. Proctor present an excerpt from their book on DMD. Read more: sinews.siam.org/Details-Page/d…
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DurstewitzLab(@DurstewitzLab) 's Twitter Profile Photo

How to reconstruct from many different data modalities observed simultaneously?
Here we introduce a novel generative modeling framework for this, based on control-theoretic ideas for efficiently guiding the training process: arxiv.org/abs/2212.07892

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How to reconstruct #DynamicalSystems from many different data modalities observed simultaneously?
Here we introduce a novel generative modeling framework for this, based on control-theoretic ideas for efficiently guiding the training process: arxiv.org/abs/2212.07892
#AI #ML
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Bibliothèque du CIRM(@Bibli_CIRM) 's Twitter Profile Photo

CIRM 🆕🎬Multidimensional symbolic dynamics and lattice models of quasicrystals
Kucherenko, Tamara (2024). Phase transitions on one-dimensional symbolic systems. CIRM. Audiovisual resource.
dx.doi.org/10.24350/CIRM.…
CIRM

@_CIRM 🆕🎬Multidimensional   symbolic dynamics and lattice models of quasicrystals
Kucherenko, Tamara (2024). Phase transitions on one-dimensional symbolic systems. CIRM. Audiovisual resource. 
dx.doi.org/10.24350/CIRM.…
@_CIRM #Maths #conference #Video #DynamicalSystems
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Bibliothèque du CIRM(@Bibli_CIRM) 's Twitter Profile Photo

🆕🎬Multidimensional symbolic dynamics and lattice models of quasicrystals
Kra, Bryna (2024). Symmetries in symbolic dynamics. CIRM. Audiovisual resource. dx.doi.org/10.24350/CIRM.…
CIRM

🆕🎬Multidimensional   symbolic dynamics and lattice models of quasicrystals
Kra, Bryna (2024). Symmetries in symbolic dynamics. CIRM. Audiovisual resource. dx.doi.org/10.24350/CIRM.…
@_CIRM #Maths #conference #Video #DynamicalSystems
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