Sebastian Michelmann

Sebastian Michelmann

@s-michelmann.bsky.social

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/

939 followers550 following76 posts

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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.

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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.

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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.

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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.

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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.

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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.

Sebastian Michelmann reposted

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.

bioRxiv Neuroscience@biorxiv-neursci.bsky.social

The human hippocampus retroactively binds experiences across time https://www.biorxiv.org/content/10.64898/2026.10.20.760394v1

Sebastian Michelmann reposted

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?

Sebastian Michelmann reposted

Sebastian Michelmann reposted

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.

Sebastian Michelmann reposted

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.

bioRxiv Neuroscience@biorxiv-neursci.bsky.social

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 🧵🧠 🔥

Zihan Bai@zihanbai.bsky.social

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...

Sebastian Michelmann reposted

And we’re off! 🚀 Symposium submissions for ICOM7 are open! Can’t wait to see what ideas you’ll be bringing to Glasgow!!

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 🧪

Sebastian Michelmann reposted

📣 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).

Sebastian Michelmann reposted

Sebastian Michelmann reposted

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:

Sebastian Michelmann reposted

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!

Sebastian Michelmann reposted

Sebastian Michelmann reposted

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

Sebastian Michelmann reposted

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.

Casper Kerrén@ckerren.bsky.social

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

Sebastian Michelmann reposted

Sebastian Michelmann reposted

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).

Sebastian Michelmann reposted

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!

Jonathan Nicholas@jonathannicholas.bsky.social

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.

Sebastian Michelmann reposted

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.

gunselilab.comMemory, Attention, & Cognitive Control Lab | Istanbul | Sabanci Uni.Memory, Attention, & Cognitive Control Lab at Sabancı University - Eren Günseli. We study how memory and attention interact, how humans control what to remember, what to forget, what to attend to, and...

Sebastian Michelmann reposted

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:

nature.comHuman learning of noninvasive brain–computer interfaces via manifold geometry - Nature NeuroscienceBusch et al. use nonlinear neural manifolds to help humans gain rapid control over a noninvasive brain–computer interface, allowing them to learn how to play a video game with real-time fMRI neurofeed...

Sebastian Michelmann reposted

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!

Sebastian Michelmann reposted

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. 🧠😄

Imaging Neuroscience@imagingneurosci.bsky.social

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...

Sebastian Michelmann reposted

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

Imaging Neuroscience@imagingneurosci.bsky.social

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...

Sebastian Michelmann reposted

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.

annualreviews.orgPlanning in the Brain: It&apos;s Not What You Think It IsThe neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neu...