sandervanbree.com
More info: tinyurl.com/NMO-ISPSM and tinyurl.com/Phimisci-Egan
#philsky
More info: tinyurl.com/NMO-ISPSM and tinyurl.com/Phimisci-Egan
#philsky
looking for a role with fun & complex technical challenges & within a great community. my main expertise is in signal processing/EEG/MEG, but topic-wise I am quite flexible.
science/industry both great! starting mid-year. nschawor.github.io/cv
looking for a role with fun & complex technical challenges & within a great community. my main expertise is in signal processing/EEG/MEG, but topic-wise I am quite flexible.
science/industry both great! starting mid-year. nschawor.github.io/cv
In this pre-reg study, our core claim was that we don’t just learn stimulus-reward. We infer hidden context and that inference re-wires attention and neural state space on the fly.
1/8
In this pre-reg study, our core claim was that we don’t just learn stimulus-reward. We infer hidden context and that inference re-wires attention and neural state space on the fly.
1/8
By @claudia-lopez.bsky.social
#neuroskyence
www.thetransmitter.org/brain-waves/...
By @claudia-lopez.bsky.social
#neuroskyence
www.thetransmitter.org/brain-waves/...
If you know great candidates interested in attention, memory transformation and EEG, please help spread the word:
Project (ReDAS) -> cimcyc.ugr.es/en/informati...
Job offer -> cimcyc.ugr.es/en/informati...
#MLSky #neurojobs #compneuro
#MLSky #neurojobs #compneuro
Seems many published studies miscalculated it, overestimating model performance. First, let's make this crystal clear:
NC = 2*r / (1+r)
where r is split-half correlation.
Have you ever computed noise ceilings to understand how well a model performs? We wrote a clarifying note on a subtle and common misapplication that can make models appear quite a lot better than they are.
osf.io/preprints/ps...
Seems many published studies miscalculated it, overestimating model performance. First, let's make this crystal clear:
NC = 2*r / (1+r)
where r is split-half correlation.
Have you ever computed noise ceilings to understand how well a model performs? We wrote a clarifying note on a subtle and common misapplication that can make models appear quite a lot better than they are.
osf.io/preprints/ps...
Have you ever computed noise ceilings to understand how well a model performs? We wrote a clarifying note on a subtle and common misapplication that can make models appear quite a lot better than they are.
osf.io/preprints/ps...
Have you ever computed noise ceilings to understand how well a model performs? We wrote a clarifying note on a subtle and common misapplication that can make models appear quite a lot better than they are.
osf.io/preprints/ps...
www.gao-unit.com/join-us/
If comp neuro, ML, and AI4Neuro is your thing, or you just nerd out over brain recordings, apply!
I'm at neurips. DM me here / on the conference app or email if you want to meet 🏖️🌮
www.gao-unit.com/join-us/
If comp neuro, ML, and AI4Neuro is your thing, or you just nerd out over brain recordings, apply!
I'm at neurips. DM me here / on the conference app or email if you want to meet 🏖️🌮
Here, we critique a recent paper by Rosas et al. We argue that "Bottom-up" and "Top-down" neuroscience have various meanings in the literature.
PDF: rdcu.be/eSKYI
Here, we critique a recent paper by Rosas et al. We argue that "Bottom-up" and "Top-down" neuroscience have various meanings in the literature.
PDF: rdcu.be/eSKYI
Neuroscience & Philo Salon: join our discussion with @elliot-murphy.bsky.social with commentaries by @wmatchin.bsky.social and @sandervanbree.bsky.social
Nov 5, 10:30 am eastern US
Register:
umd.zoom.us/my/luizpesso...
#neuroskyence
Neuroscience & Philo Salon: join our discussion with @elliot-murphy.bsky.social with commentaries by @wmatchin.bsky.social and @sandervanbree.bsky.social
Nov 5, 10:30 am eastern US
Register:
umd.zoom.us/my/luizpesso...
#neuroskyence
Really excited that this collaboration is finally out! It shows that combining oscillatory dynamics with hierarchical predictive frameworks generates speech perception that is robust to temporal distortions: similarly to human behavior and more so than current ASR models.
🔓 rdcu.be/eImit
Really excited that this collaboration is finally out! It shows that combining oscillatory dynamics with hierarchical predictive frameworks generates speech perception that is robust to temporal distortions: similarly to human behavior and more so than current ASR models.
I conned somebody into giving me a faculty job!
I’m starting as a W1 Tenure-Track Professor at Goethe University Frankfurt in a week (lol), in the Faculty of CS and Math
and I'm recruiting PhD students 🤗
"Open Questions about Time and Self-reference in Living Systems"
by Samson Abramsky, Wolfgang Banzhaf, Leo S. D. Caves, Michael Levin, Penousal Machado, Charles Ofria, Susan Stepney, Roger White
🧪
"Open Questions about Time and Self-reference in Living Systems"
by Samson Abramsky, Wolfgang Banzhaf, Leo S. D. Caves, Michael Levin, Penousal Machado, Charles Ofria, Susan Stepney, Roger White
🧪
On Tuesday, I'm presenting new work with @kathadobs.bsky.social on segregated vs. integrated face & body processing in visual cortex 😊🧍🧠
Using DNNs & fMRI, we test competing hypotheses, finding both distinct & shared selectivity.
Come by Poster A64 for more.
On Tuesday, I'm presenting new work with @kathadobs.bsky.social on segregated vs. integrated face & body processing in visual cortex 😊🧍🧠
Using DNNs & fMRI, we test competing hypotheses, finding both distinct & shared selectivity.
Come by Poster A64 for more.
We hope it sparks a smile (and maybe a few debates).
Read here: tinyurl.com/3ycv3vj2
We hope it sparks a smile (and maybe a few debates).
Read here: tinyurl.com/3ycv3vj2
In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈
www.biorxiv.org/content/10.1...
#neuroskyence
🧵 1/n
In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈
www.biorxiv.org/content/10.1...
#neuroskyence
🧵 1/n
royalsocietypublishing.org/doi/10.1098/...
royalsocietypublishing.org/doi/10.1098/...