***New preprint from the lab*** showing “Generative replay across hippocampal-neocortical circuits”. In sleep we show spiking sequences in V1 (sensory neocortex!) that represent information that has not been directly experienced www.biorxiv.org/content/10.6... @bndu.ox.ac.uk @ndcnoxford.bsky.social

Generative replay across hippocampal-neocortical circuits

To make flexible decisions, the brain needs to make inferences between events that were not directly experienced together. In sharp-wave ripple (SWR) events, during periods of rest and sleep, spiking ...

biorxiv.org

"..CA3 operates as a nonlinearly gated scene constructor and CA1 as a continuous landmark integrator to build and maintain the cognitive map." How visual scenes recruit spatial maps: threshold-gated activation in CA3 versus continuous integration in CA1 Inah Lee Lab: www.biorxiv.org/content/10.6...

How visual scenes recruit spatial maps: threshold-gated activation in CA3 versus continuous integration in CA1

The hippocampal cognitive map is constructed from environmental sensory inputs, but the conditions that recruit a spatial map from a quiescent baseline remain unclear. Using a parametric virtual reali...

biorxiv.org

After a light show, each drone flies itself back to an exact spot in its charging case, guided by GPS/RTK coordinates loaded before takeoff. No manual handling for thousands of units.

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

Hippocampal ripples initiate cortical dimensionality expansion for memory retrieval - Nature Communications

How past experiences are reconstructed from memory remains unclear. Here the authors used intracranial EEG to show that hippocampal ripples reshape the geometry of cortical representations, supporting...

nature.com

New Preprint! We gave neural networks infant-like curiosity and let them free to explore. They grew brain-like architecture, followed human synaptic development, and even started to display compositional thought. So we think curiosity doesn't just come from a complex mind... it helps us build one 👇

Curiosity shapes brain-like architectures and functions

How does complex cognition emerge from simpler underlying processes? We show that two components are sufficient: infant-like curiosity and brain-like biophysical constraints jointly drive the emergenc...

biorxiv.org

Very excited to share our new review on C-HORSE, our model of the hippocampus! We think there is very encouraging alignment between C-HORSE proposals and the extant empirical literature, though we also note plenty of areas for further model development. royalsocietypublishing.org/rstb/article...

Evidence for complementary learning systems within the hippocampus

Abstract. Decades of research have established the hippocampus as central to episodic memory, but growing evidence suggests that it also contributes to str

royalsocietypublishing.org

This is one of my favourite things. Direct replication of MEG and fMRI findings in intracranial recordings and a demonstration that cortical representations update in the right direction around hippocampal ripples.

Blake Richards@tyrellturing.bsky.social · 4w ago

For a long time I felt like there was a hesitation in neuroscience to ascribe anything like a planning function to the various "replay" events we see in the hippocampus, but I think this is rapidly changing. Here's another new entry in human epilepsy patients: www.nature.com/articles/s41... 🧠📈 🧪

I will assert, again, that the only reason to drive a Cybertruck is to save time going door to door to tell people you’re an asshole.

David Sacks argues that while Anthropic talks about open source models being “dangerous”, the real danger to businesses is deploying Claude and Anthropic uses your IP to compete with you. He implies this is what happened with Figma and Claude Design.

🚨 New Preprint! 🧠 We gave an AI model one simple rule: rearrange your neurons so that nearby ones respond alike. We never told it what a face, a voice, or a sentence was. It grew brain-like maps for all three anyway. 🧵👇 🌐 Website: topo-omni.epfl.ch

The revised version of our paper on the impact of top-down feedback is now out @elife.bsky.social: doi.org/10.7554/eLif... tl;dr: we show that using human-brain-like feedback/anatomy in a deep RNN leads to human-like visual biases! This work was led by @tmshbr.bsky.social #NeuroAI 🧠📈 🧪

Looking forward to digging into this, looks super cool. Congrats @sueyeonchung.bsky.social and crew! #NeuroAI 🧠📈 🧪

SueYeon Chung@sueyeonchung.bsky.social · 6mo ago

Our paper is out in @natneuro.nature.com! www.nature.com/articles/s41... We develop a geometric theory of how neural populations support generalization across many tasks. @zuckermanbrain.bsky.social @flatironinstitute.org @kempnerinstitute.bsky.social 1/14

This work by @mariaeckstein.bsky.social et al is a nice example of how progress in psychology can be expedited with machine learning. How long before this type of approach is expected for models-of-behavior papers? My guess: not long. (If you are a trainee, nudge!) www.nature.com/articles/s41...

Hybrid neural–cognitive models reveal how memory shapes human reward learning - Nature Human Behaviour

Using artificial neural networks applied to human data, Eckstein et al. show that good models of reinforcement learning require memory components that track representations of the past.

nature.com

"Spiking Networks Hate It! Find Out the One Plasticity Trick They Don’t Want You to Know! Never stabilise models by hand again." - I woke up thinking we missed an opportunity with the title of this one. :/ www.science.org/doi/10.1126/... Also: It snowed in Vienna, 10cm white fluffies! Happy Sunday!

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Plasticity at inhibitory synapses maintains balanced excitatory and inhibitory synaptic inputs at cortical neurons.

science.org

🚨New Preprint! How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715 + @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social 🧵 1/14

Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex

Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...

arxiv.org

Kazuki Irie has a forthcoming paper in NeurIPS that studies the following idea: Linear attention has cheap, unbounded memory but low precision, whereas softmax attention has expensive, bounded memory but high precision. These can be combined to build better transformers. arxiv.org/abs/2506.00744

Blending Complementary Memory Systems in Hybrid Quadratic-Linear Transformers

We develop hybrid memory architectures for general-purpose sequence processing neural networks, that combine key-value memory using softmax attention (KV-memory) with fast weight memory through dynami...

arxiv.org

How does the brain find its way in realistic environments? 🧠 Using deep RL and neural data, we show that hippocampal-like networks support navigation, learning, and generalisation in partially observable environments—mirroring real animal behaviour. Now out: www.nature.com/articles/s41... #neuroAI

Hippocampus supports multi-task reinforcement learning under partial observability - Nature Communications

Neural mechanisms underlying reinforcement learning in naturalistic environments are not fully understood. Here authors show that reinforcement learning (RL) agents with hippocampal-like recurrence, u...

nature.com

🚨Pre-print alert! 🚨https://www.biorxiv.org/content/10.1101/2025.10.17.683171v1 Our new study tackles the question: do all neurons in motor cortices (MC) encode movement & coordinate as we move? Answering this question will be key for effectively targeting motor representations in BCIs.

The spatiotemporal structure of neural activity in motor cortex during reaching

Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regi...

biorxiv.org

Really interesting work by Bakhurin and colleagues challenging the reward prediction error hypothesis of dopamine: www.nature.com/articles/s41... I love this figure which both echoes and undermines the famous figure from Schultz et al. (1997).

🧠🚨 How does the hippocampus transform the visual similarity space to resolve memory interference? In this new preprint, we found that the hippocampus sequentially inverts the behaviorally relevant dimensions of similarity 🧵 www.biorxiv.org/content/10.1...

Hippocampal transformations occur along dimensions of memory interference

The role of the hippocampus in resolving memory interference has been greatly elucidated by considering the relationship between the similarity of visual stimuli (input) and corresponding similarity o...

biorxiv.org

Which one should I do next? The big Swedish study that RFK & his buddies pretend doesn’t exist? Or one of the other 2 studies he mentioned at the press conference? Vote by commenting!

Dr Ellie Murray, ScD@epiellie.bsky.social · 10mo ago

If you’ve been following the RFK Jr autism news, then you’ve probably heard that there’s a systematic review “proving” Tylenol causes autism. Here’s my review of that paper👇🏼 open.substack.com/pub/epiellie...

Delighted to share our work on replay and successor representations! We find replay during very short task pauses in human visual cortex that is linked to learning SRs & happens when learning is implicit. Study led by @lnnrtwttkhn.bsky.social #compneuro #neuroskyence www.pnas.org/doi/10.1073/...

Replay in the human visual cortex during brief task pauses is linked to implicit learning of successor representations | PNAS

Humans can implicitly learn about multistep sequential relationships between events in the environment from their statistical co-occurrence. Theore...

pnas.org

New paper with @nathanieldaw.bsky.social in Nature Communications: an RL model that builds a successor map compositionally. The new model plans as well as the best models, and it links components of the map used for planning to neural codes in the medial entorhinal cortex. rdcu.be/eAofi

Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map

Nature Communications - How the brain creates compositional cognitive maps that support both flexible and efficient planning remains poorly understood. Here, authors propose a...

rdcu.be

Some similarities matter, others don’t—a great Labatut book with a yellow cover means books by Labatut are good, not yellow books are good. Factorized representations support such selective generalization. Check out our study on select. gen. by Sam HallMcMaster in collab with @gershbrain.bsky.social

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · last yr.

Entorhinal cortex signals dimensions of past experience that can be generalised in a novel environment https://www.biorxiv.org/content/10.1101/2025.08.01.668096v1

Excited to share a new preprint w/ @annaschapiro.bsky.social! Why are there gradients of plasticity and sparsity along the neocortex–hippocampus hierarchy? We show that brain-like organization of these properties emerges in ANNs that meta-learn layer-wise plasticity and sparsity. bit.ly/4kB1yg5

A gradient of complementary learning systems emerges through meta-learning

Long-term learning and memory in the primate brain rely on a series of hierarchically organized subsystems extending from early sensory neocortical areas to the hippocampus. The components differ in t...

bit.ly

Pleased to say that our story of how a theory of self-supervised learning in cortical layers accounts for several experimental observations is now out 🎉 www.nature.com/articles/s41...

Self-supervised predictive learning accounts for cortical layer-specificity - Nature Communications

How the cortex learns an internal representation of the world and why the cortex relies on a layered structure remain poorly understood. Here, the authors propose a computational theory in which the n...

nature.com

This is an excellent and very clear piece from Sergey Levine about the strengths and limitations of Large Language models. sergeylevine.substack.com/p/language-m...

Language Models in Plato's Cave

Why language models succeeded where video models failed, and what that teaches us about AI

sergeylevine.substack.com

Pleased to say "Space, Time, and Memory", an academic book by Oxford University Press edited by the inimitable Lynn Nadel & Sara Aronowitz is now out. I contributed a chapter, "Memory and Planning in Brains and Machines". You can download the entire book for free: library.oapen.org/bitstream/ha...