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Thrilled to share our latest work on @biorxivpreprint demonstrating the first real-time closed-loop ultrasonic brain-machine interface (#BMI)! 🔊🧠-->🎮🖥️
Paper link - biorxiv.org/content/10.110…
A #tweetprint. 🧵1/n
Paper link - biorxiv.org/content/10.110…
A #tweetprint. 🧵1/n
First, this work would not have been possible without co first-author @SumnerLN, co-authors @DeffieuxThomas, @GeelingC, @BrunoOsmanski, and Florian Segura, and PIs Richard Andersen, @mikhailshapiro, @TanterM, @VasileiosChris2, and Charles Liu. (2/n)
Brain-machine interfaces (BMIs) can be transformative for people with paralysis from neurological injury/disease. BMIs translate brain signals into computer commands, enabling users to control computers, robots, and more – with nothing but thought.
bit.ly/3EAgl94
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bit.ly/3EAgl94
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Thrilled to share our latest work out today in @NeuroCellPress using functional #ultrasound (fUS) neuroimaging to decode movement from single trials in non-human primates 🐒 - a new benchmark for fUS & a precursor to closed-loop #BCI.
authors.elsevier.com/c/1cnFe3BtfGzj…
#tweetprint 🧵 1/n
authors.elsevier.com/c/1cnFe3BtfGzj…
#tweetprint 🧵 1/n
Congrats to all co-authors @DMarescaLab, @VasileiosChris2, @MisterGriggz, @CharlieDemene,
dream-team collaborators @TanterM, @CharlieDemene (@INSERM @PhysMedParis, @ESPCI_Paris),
& PIs/ #neuroscience luminaries Richard Andersen & @mikhailshapiro (@Caltech, @CaltechN) 2/n
dream-team collaborators @TanterM, @CharlieDemene (@INSERM @PhysMedParis, @ESPCI_Paris),
& PIs/ #neuroscience luminaries Richard Andersen & @mikhailshapiro (@Caltech, @CaltechN) 2/n
Background: Brain-machine interfaces (BMI) are powerful devices for restoring function to people living with paralysis. Already, our patients control external devices like computers and robotic limbs with incredible fidelity. 3/n
gph.is/g/ZkxRqpA
gph.is/g/ZkxRqpA
1/ #tweetprint time! how would you like to detect 99% of vocals in recordings automagically? ;)
Then checkout our new preprint on @biorxiv
work lead by @aho_fonseca, with @gumadeiras
Paper: biorxiv.org/cgi/content/sh…
Code: github.com/ahof1704/Vocal…
Dataset: osf.io/bk2uj/
Then checkout our new preprint on @biorxiv
work lead by @aho_fonseca, with @gumadeiras
Paper: biorxiv.org/cgi/content/sh…
Code: github.com/ahof1704/Vocal…
Dataset: osf.io/bk2uj/
2/ we present VocalMat, an open-source MATLAB-based tool for automatic detection and classification of ultrasonic vocalizations (USVs) from mice 🐭
3/ VocalMat uses adaptive signal and image processing to automatically detect USVs in experimental recordings
New work out on arXiv! Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics (arxiv.org/abs/1906.10720), with fantastic co-authors @ItsNeuronal, @MattGolub_Neuro, @SuryaGanguli and @SussilloDavid. #tweetprint summary below! 👇🏾 (1/4)
We analyze recurrent networks trained to perform sentiment classification, a standard natural language processing (NLP) task. We find that RNNs trained on this task as surprisingly interpretable using tools from dynamical systems. (2/4)
The network dynamics are organized around a roughly 1-D approximate line attractor, which we identify by studying the eigendecomposition of the recurrent Jacobian of the dynamics at approximate fixed points. (3/4)
