Cognitive neuroscientist (Assistant Professor at NYU), human episodic memory, M/EEG, ECoG, and behavior. How do we reinstate temporally dynamic, information-rich memories?
https://idemlab.github.io/
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
13/ In sum, we propose that episodic encoding does not require storing a copy of the past, but finding a starting point from which the past can be reconstructed. This reconstruction uses statistical building blocks stored in cortex, allowing unique memories to exploit shared structure.
14/ This account is parsimonious: it requires no symmetric back-projections. It is flexible: multiple starting points can support equivalent reinstatement. And it is adaptive: computation is front-loaded to encoding, when resources are available, and depth can be tuned to expected retrieval demands.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
11/ Encoding via generative approximation also benefits from expertise. Models fine-tuned on cartoon or pixel-art images need fewer approximation steps for images from their own domain than for images from the other domain: the expert knowledge in the weights makes starting points easier to find.
12/ The representational geometry of latent starting points resembles the MTL in 7T fMRI (Natural Scenes Dataset), and reconstruction through the weights traverses the visual hierarchy in reverse. This suggests that AI image generation is a neurally plausible model of memory reinstatement.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
9/ The model reproduces key findings from the Mnemonic Similarity Task. Closer approximation captures more details of objects and their similar-looking counterparts. Shallow encoding selectively impairs the rejection of similar lure objects, as observed in aging and mild cognitive impairment.
10/ When is deeper encoding worth it? A rational analysis balancing accuracy and speed shows that optimal encoding depth increases as lures become more similar. This suggests that encoding depth should be tuned to expected retrieval demands.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
7/ We operationalize encoding depth as the number of approximation steps in a toy model and, through image inversion, in AI image-generation models. Generative approximation realizes a trade-off: better starting points take longer to find but produce more detailed reconstructions at retrieval.
8/ In Stable Diffusion, latent starting points of related images are well separated, but keep enough structure for retrieval from similar cues. When codes are passed through the weights of the network, shared structure re-emerges as higher similarity between images from the same category.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
5/ Generative approximation learns the retrieval operation directly. Our model has no encoder; it can only generate. It searches for a starting point from which its fixed weights produce the target pattern. Instead of invertible bidirectional connections, it only needs a feedback signal.
6/ A memory becomes jointly represented across two complementary components: a latent episode-unique "starting point", and cortical weights that supply statistical building blocks for reinstatement. Each new experience has a unique code but the distributed representation recycles cortical knowledge.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
3/ Episodic memory requires the hippocampus, but widespread cortical networks are involved in representing its content. Because cortex learns slowly, the hippocampus needs a way to target the “right” cortical neurons when an episode is retrieved.
4/ We tend to think of encoding as transforming the cortical pattern into a hippocampal code, and retrieval as inverting that transformation. But how the inverse is computed is rarely spelled out. We propose an alternative where retrieval never needs to undo anything: generative approximation.
1/ Our brains probably don’t store a new copy of “red” when we encounter something red. Yet we can reinstate distinct memories of red objects. How do we reuse what we already know when encoding memories? Excited to share new work led by @haoliu4391.bsky.social, with @marcelomattar.bsky.social 🧠🧵
2/ In our preprint, we propose a computational framework in which episodic memory encoding and retrieval leverage a single, shared generative process that represents new memories by recombining existing knowledge:
www.biorxiv.org/content/10.6...#neuroscience#neuroskyence#memory#CompNeuro#CogSci
1/10 🧠 Imagine seeing a cute dog before it runs away. A few seconds later, your friend tells you this was a Corgi.
To remember this experience later, your brain has to link the dog (who is now out of sight) with the name you just heard.
Imagine two people standing in the same spot, looking at the same Goya. They inspect exactly the same parts of the painting, but in a different order. Later, they talk about what they saw. Ignoring all other differences: are the memories they formed identical?
Excited to share our work, now published in Neuropsychologia! We show how temporal expectation shifts priorities in working memory—and can lead to long-term forgetting. Huge thanks to
Dr. Jarrod Lewis-Peacock and @bulezz.bsky.social for their help and support!
www.sciencedirect.com/science/arti...
Columbia Psychology is hiring for a Cognitive Science/Neuroscience Assistant Professor position as part of our ongoing push to expand our Cognitive research program (including two hires last year). We'll be starting application reviews next week so get materials in asap!
apply.interfolio.com/193975
1/ How do we sustain mental continuity in the face of interruption? @xianl-explorer.bsky.social found that, while people listen to a story, posterior medial cortex expresses a slowly evolving "background" representation of the story's context, which survived through silence and interfering tasks.
First preprint from the lab from postdoc Carlo Cerquetella! Demonstrating that neuronal spiking in the human medial temporal lobe and prefrontal cortex cohere into a redundant, low-dimensional neural correlate of visual attention (captured mostly by saccades) during naturalistic movie viewing.
Human single-neuron recordings reveal population coding of attentional dynamics during naturalistic movie viewing https://www.biorxiv.org/content/10.64898/2026.09.08.748346v1
Thrilled to share this with the world! In this work led by Zihan Bai, we asked how episodic memory interacts with working memory and found evidence for a tight integration between the two systems. Zihan walks you through it in his thread 🧵🧠 🔥
Excited to share the first first-author preprint of my PhD at NYU with @s-michelmann.bsky.social andDaryl Fougnie. Using eye-tracking in a novel behavioral paradigm, we reveal the mechanisms by which episodic memory (EM) and working memory (WM) interact. 🧠
doi: doi.org/10.64898/202...
Symposium submission is now open! The deadline is 2nd November, so please get your applications in on time. We welcome not just traditional formats but also encourage panel discussions or other creative formats!
icom-memory.org/symposia-sub...#philsky#psychscisky#neuroskyence#cognition 🧪
📣 My lab at NYU currently has two open post-doc positions. We are interested in understanding perception, thinking, and hallucinations.
We employ a range of cutting-edge techniques in human neuroscience (e.g., 7T fMRI, MEG, iEEG/DES) and computational approaches (e.g., DNN modeling).
New preprint from the lab: We reproduced our 2p data in the accumulating towers task with e-phys and found low-dimensional neural manifolds in the frontal cortex that were aligned timepoint-by-timepoint with the hippocampus. www.biorxiv.org/content/10.6...
We’re recruiting a full-time lab manager to join the Shared Minds Lab at USC! This will be a great opportunity for someone who wants to get hands-on experience with research before starting a PhD program in psychology or neuroscience. More below:
New perspective piece out in @cp-neuron.bsky.social with Zaid Zada, @adelegoldberg.bsky.social, and Uri Hasson! We try to articulate some of our excitement about LLMs and discuss what kinds of insights they might provide into the neural computations supporting natural language in the human brain.
Come work with me at Google! We have an open 6-month temp role on my team - it’s a great opportunity for someone with a cog neuro or psych background interested in exploring big tech. Looking for comp modeling experience (e.g. RL, DDMs) and Python/JS. Reach out to learn more or pass along any names!
How do we make good decisions with little experience? Adults can rely on memories of past choices. In our new study in @pnas.org we show kids & teens do something similar–but what they remember turns out to be quite different! www.pnas.org/doi/10.1073/...
The Naturalistic Cognitive Computational Neuroscience Lab is launching at UT Austin and is looking for founding members at all levels, including lab manager, PhD students, and postdocs! #NeuroJobs
We will study human brain🧠 and memory & attention, using fMRI and modeling.
thesonglab.github.io
New paper in Imaging Neuroscience by Hayley Bree Caldwell, Sebastian Michelmann, et al:
Phase similarity between similar objects indicates representational merging across retrieval training but not sleep
doi.org/10.1162/IMAG...
My paper exploring EEG phase dynamics across sleep and retrieval training has just been published online! This paper summarises the work I conducted with Sebastian Michelmann and Simon Hanslmayr, during my PhD. The paper is open access, so feel free to give it a read🧠
direct.mit.edu/imag/article...
I'm recruiting two postdocs (or potentially PhD students) to work on projects at the intersection of cognition, neuroscience, and AI (with a particular emphasis on mechanistic interpretability). Apply here: docs.google.com/forms/d/e/1F... and please share!
Excited to see this out today and so happy to have been part of this! Amazing work by @ckerren.bsky.social showing how the dimensionality of memory representations expands in a ripple-locked reinstatement process.
Excited to finally share our new paper in @natcomms.nature.com:
Hippocampal ripples initiate cortical dimensionality expansion for memory retrieval
www.nature.com/articles/s41...
With @s-michelmann.bsky.social and @doellerlab.bsky.social
My lab is hiring! We are recruiting a full time research coordinator to support our projects on memory brain states. More info and application: jobs.virginia.edu/us/en/job/R0...
📣 In meiner Arbeitsgruppe (LMU München) ist eine PhD-Stelle zu besetzen. Gesucht werden motivierte Bewerber:innen mit Interesse an experimenteller kognitiver Neurowissenschaft. Bewerbungsfrist: 10.08.2026. Bewerbungen bitte per Email. Mehr Infos hier:
job-portal.lmu.de/jobposting/f...
Looking to work with a postdoc. Prior experience with fMRI and computational modeling required, interest in psychiatry desired.
Fellowship provides three years salary plus independent travel/equipment funds.
Send CV and 2-3 manuscripts (preprints OK).
——
Open Mind is MIT Press’s Diamond Open Access Cognitive Science journal.
We now have our first Impact Factor: 2.9 as of June 2026
Submit your papers to Open Mind
direct.mit.edu/opmi
Check out @jonathannicholas.bsky.social 's new preprint measuring eye movements to infer individual memory retrievals during decision making, plus a cool RNN modelof that process. Can't express how excited I am about this new technique!
We make flexible choices in new situations by knitting together information from separate relevant memories.
But what governs which memories are retrieved and when?
In a new preprint, we captured how people build decision variables from different memories by tracking their gaze on a blank screen.
I am looking for a postdoctoral researcher to join my lab at Sabancı University in Istanbul for a 2-year TÜBİTAK-funded position, with possible extension.
Interested candidates can email me with a CV and a brief note about their research interests.
More details on gunselilab.com soon.
Our new paper is out this week in Nature Neuroscience! www.nature.com/articles/s41... We built a BCI that works with the brain's natural geometry — and we found that people could learn to play a video game with their brains in <1 hr of training. This efficiency is groundbreaking & here's why:
Excited to share I will be joining UC Davis as an assistant professor starting July 2027! Thank you dearly to all mentors and friends who have supported me over the years ❤️ and Cal Poly friends, I'll miss you!
Excited to see the final version of our manuscript out! We find that OPM-MEG reliably captures neural activity comparable to other neuroimaging methods.
Thank you to the reviewers for their thoughtful feedback and to @s-michelmann.bsky.social for his mentorship on this project. 🧠😄
New paper in Imaging Neuroscience by Olivia R. Christiano and Sebastian Michelmann:
Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm
doi.org/10.1162/IMAG...
New paper in Imaging Neuroscience by Olivia R. Christiano and Sebastian Michelmann:
Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm
doi.org/10.1162/IMAG...
In collaboration with @monicarosenb.bsky.social , we showed that individual diffs in LTM encoding is uniquely predicted by inter-electrode correlations even controlling for working memory abilities. This suggests that WM & LTM encoding are separate abilities coded by different neural signatures! 1/n
New paper in Imaging Neuroscience by Chong Zhao, Edward K. Vogel, and Monica D. Rosenberg:
A unique neural signature of long-term memory encoding from EEG inter-electrode correlation
doi.org/10.1162/IMAG...
🧠 ICOM is back!
The International Conference on Memory (ICOM7) returns 26–30 July 2027 in Glasgow, bringing together researchers from neuroscience, psychology, and beyond.
🔗 Learn more and sign-up for the newsletter here: icom-memory.org
#ICOM7#MemoryResearch#Neuroscience#Psychology
New Annual Review with @nathanieldaw.bsky.social: “Planning in the Brain: It's Not What You Think It Is.” We argue that the brain's 'planning' machinery is mostly used for learning from simulated experience, and that thinking prospectively at decision time is just one special case of this process.
@michael.muthukrishna.com and I are hiring a postdoc to join our labs at NYU! We're looking for someone excited to work on one of society's newly emerging and potentially generation-shaping challenges: the multi-agent alignment problem.