Now: Brain-Computer Interfaces research at Snap Inc.
Past: Co-founder at NextMind; academic research at UCL and ENS
Neuroscience and machine learning in general.
📍47.589810, 7.589029
| 🔬 Research | BCI, AI, neuroscience (vision, audition, attention...), DSP |
| 👾 Github | https://github.com/nbara/ |
| https://www.linkedin.com/in/nicolas-barascud/ |
Friends don't let friends make shitty data plots!
A great guide to 13 common enemies by @chenxinli2.bsky.social
New paper!
Cytoelectric Coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure.
Brain waves carry info and alter the brain on the molecular level. This tunes the cytoskeleton, optimizing network function.
Work by Dimitris Pinotsis.
https://doi.org/10.1016/j.pneurobio.2023.102465
Convolutional neural networks with retinal-like pre-processing are fun.
Unlocking the Secrets of the Primate Visual Cortex: A CNN-Based Approach Traces the Origins of Major Organizational Principles to Retinal Sampling
https://www.biorxiv.org/content/10.1101/2023.04.25.538251v1
Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.
Our paper is out in Nature Human Behaviour
‘Evidence of a predictive coding hierarchy in the human brain listening to speech’
📄: http://nature.com/articles/s41562-022-01516-2
💡: Unlike language models, our brain makes distant & hierarchical predictions
with Charlotte Caucheteux and Alexandre Gramfort
Thread: https://twitter.com/c_caucheteux/status/1632740588352151556
@cbrnr @abompard Though looking at some recent PRs, it might be a matter of time before those pycodestyle errors can be toggled on https://github.com/charliermarsh/ruff/issues/2402 ?
EDIT: looks like it might be happening very soon actually https://github.com/charliermarsh/ruff/issues/3361#issuecomment-1456451532
Helena Jambor 💔 🇮🇱 🇺🇦
Earl K. Miller
Grace Lindsay
Heleen Slagter
Jonathan Z Simon
Jean-Rémi King