🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Introducing BrainGPT.org, a large language model (LLM) to assist neuroscience research. BrainGPT is trained on the vast neuroscience literature and can be used to optimize study design, detect anomalous results, and evaluate models against broad data patterns. (1/3)

1/9 Hey everyone, new preprint, new data new 🧵. Let's delve into the complex relationship between 'brain entropy' & psychedelics. There's literally no replication in this field yet so we went hard looking at a range of quantifications in a new dataset. medrxiv.org/content/10.110…

✨ MEET THE TEAM ✨ Holly is working with Onaya on our neuroimaging research, in line with her ongoing PhD in at University of Bath 🎓 Holly has focused a lot on the neural correlates of imagination - a great fit for our work translating Shipibo practices into Western science 🔬

Using MRCSA we separated the oscillatory and scale-free components of the auto- and cross-spectral exponents (βxy) of resting-state EEG. Several regions and connections show decreased βxy during aging. tinyurl.com/3akzenc6 #AcademicTwitter #neuroscience

Are Emergent Abilities in Large Language Models just In-Context Learning? Spoiler: YES 🤯 Through a series of over 1,000 experiments, we provide compelling evidence: arxiv.org/abs/2309.01809 Our results allay safety concerns regarding latent hazardous abilities. A🧵👇 #NLProc

`Decoding speech perception from non-invasive brain recordings`, led by the one an only Alexandre Défossez is just out in the latest issue of Nature Machine Intelligence: - open-access paper: nature.com/articles/s4225… - full training code: github.com/facebookresear…

Pleased to have published a comprehensive literature review of ayahuasca and its various uses! 🍃 Co-authored by six of my colleagues from @Onaya_Science plus collaborators from Psychae Institute and beyond. Summary below...👇🏼 (1/n) onlinelibrary.wiley.com/doi/epdf/10.10…

1/🧵 Do you see what I see? Don’t miss the chance to participate in #ThePerceptionCensus – a unique, large-scale study of ‘perceptual diversity’ – the differences in how we each experience the world. There’s not long left 🏃🏽‍♀️ perceptioncensus.dreamachine.world

I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by Marcus Ghosh. YouTube Videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.

Congratulations to Holly Wilson Holly on successfully passing her PhD on "Neural Decoding - Exploring Novel Inputs for Generalisable Brain Computer Interfaces"!

Our review on the 'Feasibility of decoding visual information from EEG' is out. Hope some BCI researchers out there find it useful! tandfonline.com/doi/full/10.10… Michael J. Proulx

"Why the simplest explanation isn’t always the best" - commentary with Eva Dyer highlighting how dimensionality reduction does not usually give us what we want. pnas.org/doi/10.1073/pn… "Major Achievement: Dino scatterplot in paper" unlocked.

To preprocess or not to preprocess your #EEG (when building #ML models) 🔮? 💫We are thrilled to share our latest #preprint, studying the challenge of learning brain-specific biomarkers from EEG 🧠📶 using #ML ⚙️🖥️: biorxiv.org/content/10.110… We compile arguments and evidence from

Looking for recommendations on papers or books to read and researchers to follow on the topic of neural entrainment to music 🎶🎵 🙏

Illusory generalizability of clinical prediction models science.org/doi/10.1126/sc… #AI

We made a free, open course on neuroscience for people with a machine learning / quantitative background. Get deep in computational neuroscience, or just indulge your neurocuriousity. I believe that neuro and ML can learn from each other and do better together than on their own.

Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.