Max Thurm

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Max Thurm

Max Thurm

@maxinthur

Mannheim, Baden-Württemberg شامل ہوئے Ekim 2021
116 فالونگ46 فالوورز
Max Thurm ری ٹویٹ کیا
HanganuOpatzLab
HanganuOpatzLab@HanganuOpatzLab·
So happy to see this finally out! 🥳Published today in @NeuroCellPress cell.com/neuron/fulltex… @Jastyn_Poepplau spearheaded the study of prefrontal circuits during late development and identified a “critical period” of cognitive processing. Great teamwork!👏 Short thread below👇
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allen institute
allen institute@AllenInstitute·
Freely access electrophysiological, morphological, and transcriptomic data measured from thousands of individual mouse & human brain cells via the Allen Cell Types Database: celltypes.brain-map.org #openscience #SfN23
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Nature Rev Neurosci
Nature Rev Neurosci@NatRevNeurosci·
Reconstructing computational system dynamics from neural data with recurrent neural networks — a Perspective by Daniel Durstewitz, Georgia Koppe & Max Ingo Thurm go.nature.com/3PPAufT @DurstewitzLab
Nature Rev Neurosci tweet media
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Nature Rev Neurosci
Nature Rev Neurosci@NatRevNeurosci·
Reconstructing computational system dynamics from neural data with recurrent neural networks — a Perspective by Daniel Durstewitz, Georgia Koppe & Max Ingo Thurm go.nature.com/3PPAufT @DurstewitzLab
Nature Rev Neurosci tweet media
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FOR5159
FOR5159@for5159·
Highlight of the week from @DurstewitzLab: Valuable insights on using Recurrent Neural Networks to study biological neural networks as dynamical systems! nature.com/articles/s4158…
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David Sussillo
David Sussillo@SussilloDavid·
Excellent perspective/primer/review of 10 years of dynamical systems theory and apps to neuroscience, w/ glossary and 200+ citations. "RNNs...may one day be transformative for our understanding of brain function, perhaps comparable in impact to the advances in optogenetics." 👀
DurstewitzLab@DurstewitzLab

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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Towards Data Science
Towards Data Science@TDataScience·
In this article, @brenner_manuel dives into the fundamental mechanisms of several classes of generative models, shedding light on their inner workings and exploring their origins in and connections to neuroscience and cognition. buff.ly/3LSDiYx
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DurstewitzLab
DurstewitzLab@DurstewitzLab·
We also discuss various ways of linking model latent spaces to the biological substrate through specific configuration of decoder models. This adds another layer of mechanistic interpretability to such models. Jointly with @GeorgiaKoppe & @maxinthur
DurstewitzLab tweet media
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Manuel Brenner
Manuel Brenner@brenner_manuel·
Find us tommorow, 9am at @CNSorg Leipzig in the Goethesaal for our tutorial on reconstructing dynamical systems from neural measurements using RNNs, including a hands-on tutorial with code!
Manuel Brenner tweet media
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DurstewitzLab
DurstewitzLab@DurstewitzLab·
Interested in inferring interpretable, low-dim generative dynamical systems models from empirical time series? That after training also mimic the underlying system's long term behavior? Code from our #icml2023 oral now online: github.com/DurstewitzLab/…
DurstewitzLab@DurstewitzLab

Our #ICML2023 paper introd. an adaptive gradient rescaling trick to fully resolve the exploding grad. problem when training #RNN on chaotic #DynamicalSystems. By this we can reconstruct DS underlying even complex empirical data like #EEG incl. attractors: arxiv.org/abs/2306.04406

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DurstewitzLab
DurstewitzLab@DurstewitzLab·
While many algos manage good short-term predictions, what is tricky is capturing the long-term dynamics. If you know of any other #ML/#AI/ #DataScience algos which can achieve this on real world data, please let us know! Great work by Florian, @brenner_manuel, @Zahra__Monfared !
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دریافت کریں

@NeuroCellPress @Jastyn_Poepplau @NeurIPSConf @Zahra__Monfared @lukasironman @DurstewitzLab @GeorgiaKoppe @brenner_manuel