We are hiring a research specialist, to start this summer! This position would be a great fit for individuals looking to get more experience in computational and cognitive neuroscience research before applying to graduate school. #neurojobs Apply here: research-princeton.icims.com/jobs/21503/r...
Posts by Norman Lab
What if we could tell you how well you’ll remember your next visit to your local coffee shop? ☕️
In our new Nature Human Behaviour paper, we show that the 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗮 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 can be measured with neuroimaging – and 𝘁𝗵𝗮𝘁 𝘀𝗰𝗼𝗿𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝘀 𝗵𝗼𝘄 𝘄𝗲𝗹𝗹 𝗻𝗲𝘄 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝘄𝗶𝗹𝗹 𝘀𝘁𝗶𝗰𝗸.
Come work with us! @princetonneuro.bsky.social and the Department of Psychology at Princeton University are searching for a tenure-track Assistant Professor in the area of human cognitive neuroscience, to be hired jointly in Psychology and Neuroscience: puwebp.princeton.edu/AcadHire/app...
New paper led by @codydong.bsky.social now out in @cp-trendscognsci.bsky.social, exploring the relationship between memory-augmented LLMs and human episodic memory – see Cody’s post below for a short thread and a non-paywalled paper link! #NeuroAI doi.org/10.1016/j.ti...
New paper led by @jayneuro.bsky.social: Repetition of musical themes in Eternal Sunshine of the Spotless Mind reactivates memories of earlier scenes, and this neural reactivation correlates with subsequent memory for those scenes! Check out Jamal's thread below 👇
Thrilled that this collaborative project with the @maureenritchey.bsky.social lab, co-led by @gushennings.bsky.social and Paula Brooks, is out in preprint form!
@qlu.bsky.social is starting his lab at City U of Hong Kong! This is a truly amazing opportunity for trainees interested in computational cognitive neuroscience and neuroAI
Thank you to Ingrid Wickelgren and the team at Quanta for putting together this great piece, describing work by my lab and others on the neural representations of events
Our paper on how neural codes track prior events in a narrative and predict subsequent memory for details, led by @collinsilvy.bsky.social, is now out in Communications Psychology! rdcu.be/d93Vc #neuroskyence #psychscisky
Excited to share our preprint "Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search" 🧠✨ We leverage iEEG to elucidate the fast neural mechanisms by which long multimodal narratives are unfurled in continuous memory-search tinyurl.com/wjkr3dvf
Training in the Method of Loci promotes the development of widespread conjunctive representations in neocortex that bind items and loci -- very excited to be part of this project led by @huangjiawen.bsky.social and @chrisbaldassano.bsky.social
So happy that our paper on event segmentation in large language models is now out in Behavior Research Methods! tinyurl.com/2j76882b With @mtoneva.bsky.social, @ptoncompmemlab.bsky.social, and Manoj Kumar, we show that LLMs can segment narrative text into meaningful events similarly to humans.
Thrilled that this epic study led by @coralineiordan.bsky.social is now out in PNAS!
🔔𝐍𝐄𝐖 𝐏𝐑𝐄𝐏𝐑𝐈𝐍𝐓 𝐀𝐋𝐄𝐑𝐓🔔 Beyond excited to present our new work showcasing 𝐡𝐨𝐰 𝐰𝐞 𝐜𝐚𝐧 𝐩𝐫𝐞𝐝𝐢𝐜𝐭 𝐡𝐨𝐰 𝐰𝐞𝐥𝐥 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐢𝐧𝐬𝐭𝐚𝐭𝐞 𝐚 𝐧𝐞𝐰 𝐦𝐞𝐦𝐨𝐫𝐲 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐚𝐭 𝐦𝐞𝐦𝐨𝐫𝐲 𝐢𝐬 𝐟𝐨𝐫𝐦𝐞𝐝! Wait what? Exciting collab w/ @ptoncompmemlab.bsky.social & @chrisbaldassano.bsky.social Link: www.biorxiv.org/content/10.1... (1/11)
When memories are identified as targets for representational change, some of the plasticity required to implement those changes may occur later, during offline REM sleep. (9/9)
Our findings support the hypothesis that REM sleep drives representational change in the hippocampus, showing one way that REM sleep may support memory consolidation… (8/9)
We also hypothesized that neural differentiation would be correlated with the amount the predicted item came to mind during prediction errors, more so in the REM group than the Wake and non-REM sleep-only groups. This pattern was reliable (at an uncorrected threshold) in bilateral DG. (7/9)
We found more differentiation in the group with REM sleep than the Wake and non-REM sleep-only groups in the right CA2/3/DG (significant at an uncorrected threshold). An exploratory analysis found that the effect was concentrated in the right DG. (6/9)
Using the same task as Kim et al. (2017), we measured how the representations of A and B changed across a period of consolidation with fMRI. We manipulated the presence or absence of REM sleep in a daytime nap during that consolidation period (we also included a quiet wake control group). (5/9)
Kim et al. found that, when an item predicted in a particular context (e.g., A predicts B) failed to appear and was later restudied in a different context, the representations of A and B became less similar in the CA2/3/DG region of the hippocampus (Kim et al., 2017). (4/9)
Here, we sought to test the preregistered hypothesis that learning during REM sleep helps differentiate the neural representations of related memories, by expanding on a prior fMRI study by Kim et al. (2017) showing that prediction errors lead to neural differentiation in the hippocampus… (3/9)
Myriad studies have contributed to our understanding of non-REM sleep and its role in memory consolidation, but the role of REM sleep largely remains a puzzle. (2/9)
Excited to share a new preprint with Elizabeth McDevitt, Ghootae Kim, and Nick Turk-Browne investigating the role of REM sleep in neural differentiation of memories in the hippocampus! URL: www.biorxiv.org/content/10.1101/2024.11.... (1/9)
New paper led by Kailong Peng: Real-time fMRI neurofeedback that promotes coactivation of cortical representations drives integration in the hippocampus #neuroskyence #psychscisky royalsocietypublishing.org/doi/10.1098/...
The paper also features explainer videos! For example, this video explains why associating two scenes to the same face in Favila et al. (2016) leads to differentiation: https://www.youtube.com/watch?v=dRIW6zVp4qw
(5/5)
The model predicts that, when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. (4/5)
We provide an unsupervised NN model that can explain these and other related findings by implementing the Nonmonotonic Plasticity Hypothesis (NMPH), whereby moderate coactivity weakens weights, leading to differentiation, and strong coactivity strengthens weights, leading to integration (3/5)
Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations integrate. However, these models have recently been challenged by studies showing that pairing stimuli with a shared associate can sometimes cause differentiation. (2/5)
Our neural network model of differentiation and integration of competing memories, developed by Victoria Ritvo and Alex Nguyen, has now been published in @elife.bsky.social:
Paper: https://tinyurl.com/4b3k95rd
Code: https://tinyurl.com/mr25rk9e
#neuroskyence #psychscisky #neuroai (1/5)