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 · last mo.

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...

My behavioral neuroscience lab at Northeastern is looking to recruit two new research techs! Job description and link to apply is here: northeastern.wd1.myworkdayjobs.com/careers/job/...

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...

The geometry of knowledge in the hippocampal-prefrontal system

Decision making is associated with frontal brain circuits and spatial navigation with the hippocampus. In addition, recent work in spatial decision making tasks found single neurons in both areas enco...

biorxiv.org

My main postdoc work is now published: www.nature.com/articles/s41...! We (myself, Isabel Low, Frances Cho, and @lgiocomo.bsky.social) discovered task-relevant remote representations in entorhinal cortex independent of CA1. #paperthread below! 1/13

Our latest publication grapples with how the brain could implement gradient descent by sending learning targets top-down, gating plasticity with dendritic inhibition, and updating synaptic weights with biologically observed learning rules like BTSP. www.cell.com/cell-reports...

Cellular and subcellular specialization enables biology-constrained deep learning

Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma- and dendrite-targeting inhibition and realistic connectivity co...

cell.com

Aaron Milstein@neurosutras.bsky.social · last yr.

New #NeuroAI #compneurosky preprint! To better understand how target-directed learning works in the brain, we sought to engineer an artificial neural network capable of solving complex image classification tasks that comprises only experimentally-supported biological building blocks. (1/15)

“Facilitatory surrounds resembled naturalistic continuations of the optimal center stimulus” Excellent: this physiology data supports our recent finding using patterned stimulation—together suggesting that visual cortex neurons go beyond the classical RF and reflect the structure of natural vision.

Neuron@cp-neuron.bsky.social · 7mo ago

First preprint from the lab! Using intracellular recordings & analysis of 2-photon imaging data, we show that spiking & neuromodulatory input during experience drive a reorganization of visuomotor inputs in V1 layer 2/3 neurons, consistent with enhanced visuomotor cancellation - bioRxiv link below.

Two-photon calcium imaging at 24,000 lines/s, with the resonant axis spanning 4x what other systems can do. Inertia-free. Diffraction-limited. No tradeoffs. Che-Hang Yu developed a 4x angle multiplier for laser scanning. His paper is out today: opg.optica.org/optica/fullt... 1/n #fluorescenceFriday

DNN models of the brain are getting bigger. Are we replacing one complicated system in vivo with another in silico? In new work, we seek the *smallest* DNN models of visual cortex, balancing prediction with parsimony. It turns out these compact models are surprisingly small! rdcu.be/e5H8G

Compact deep neural network models of the visual cortex

Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

rdcu.be

hey team! I need advice! If there were theoretically an organization whose sole purpose was to systematically identify and break nuisance and stupid IP in the biomedical space, creating shareware equivalents, what would be the highest value targets? e.g., AAV serotypes, antibodies, CRISPR, 1/n

Interested in deep learning-based spike inference with Cascade? We re-implemented the code in Torch -previously in Tensorflow - for better compatibility. Blog: gcamp6f.com/2026/02/17/c... GitHub: github.com/PTRRupprecht...

CascadeTorch: a PyTorch version of Cascade for spike inference

I’m glad to share a PyTorch-based implementation of spike inference from calcium imaging data: CascadeTorch, now available on GitHub. The original Cascade repository remains fully supported. …

gcamp6f.com

Applications are now open for the summer school: 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐨𝐬𝐜𝐢𝐞𝐧𝐜𝐞 🧠 Apply before March 15: www.compneuronrsn.org 📍 Located in beautiful Eresfjord 🇳🇴 🗓️ Between July 6-24 Supported by the @kavlifoundation.org In collaboration with @kavlintnu.bsky.social

Mathematical Methods in Computational Neuroscience

Summer school in Eresfjord, Norway (July 8th - 26th, 2024)

compneuronrsn.org

@sebastianseung.bsky.social Did you follow through on your plan to visit Epstein island?

Here’s a trainee sticking their neck out to ask a fair and important question. Admiration! (And obviously, follow @juliecharlton.bsky.social).

Julie Charlton@juliecharlton.bsky.social · 8mo ago

Hey @eboyden3.bsky.social care to explain?

Hey @eboyden3.bsky.social care to explain?

ScaryCatalyst 🎃@straycatalyst.bsky.social · 8mo ago

for fuck's sake www.justice.gov/epstein/file...

New @pnas.org paper out 🎉 “Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning” 👉 doi.org/10.1073/pnas... What drives representational drift in neural populations? Here’s the short version. 👇 🧪🧠 1/5

🚨 New preprint! Why do some insights from spikes translate to field potentials while others don't? In this paper we compare visual memory representations in spikes and LFPs to propose a general framework that answers this question. www.biorxiv.org/content/10.6... 🧵 (1/10) 🧠🟦 🧠💻

Neural representations of visual memory in inferotemporal cortex reveal a generalizable framework for translating between spikes and field potentials

Translating neurophysiological findings requires understanding the relationship between common measures of brain activity in animals (spiking activity) and humans (local field potentials, LFP). Prior ...

biorxiv.org

With Masashi launching his new lab, we’ll be recruiting a new postdoc in the Oldenburg Lab. Work: high-precision multiphoton holography, neural coding, motor cortex circuits, all-optical physiology. If you’re interested, just reach out.

Proud to share that my first postdoc, Masashi Hasegawa, will be starting his own lab as a Chica and Heinz Schaller Research Group Leader at Heidelberg University! He’ll be continuing our holography-based work on cortical circuits. Phenomenal scientist, even better human.

*Fully funded PhD student positions available to discover what changes in 🧠 when we learn something new.* You think virtual reality + laser scanning microscopy are cool? You like 💻 and 🐭? Come join us! Apply either through www.udel.edu/academics/co... or www.udel.edu/academics/co...

Our work with @pawa-pawa.bsky.social is out in Nature Machine Intelligence! The choice of activation function affects the representations, dynamics, and circuit solutions that emerge in RNNs trained on cognitive tasks. Activation matters! www.nature.com/articles/s42...

Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks - Nature Machine Intelligence

Recurrent neural networks are widely used to model brain dynamics. Tolmachev and Engel show that single-unit activation functions influence task solutions that emerge in trained networks, raising the ...

nature.com

Pavel Tolmachev@pawa-pawa.bsky.social · 12mo ago

Excited to share our new work with @engeltatiana.bsky.social! RNNs are often used to explore how the brain may solve specific tasks. We show that, depending on the architecture, RNNs find distinct circuit solutions, behaving differently when exposed to novel stimuli. www.nature.com/articles/s42...

Excited to share more widely: I am starting the Dorkenwald Lab at MIT (McGovern Institute + BCS) in January 2026. We study the organizational principles of neuronal circuits using connectomics. And we are hiring postdocs & tech associates! dorkenwaldlab.com/join

MIT’s McGovern Institute and Department of Brain and Cognitive Sciences welcome new faculty member Sven Dorkenwald - MIT McGovern Institute

The McGovern Institute and the Department of Brain and Cognitive Sciences are pleased to announce the appointment of Sven Dorkenwald as an assistant professor starting in January 2026. A trailblazer i...

mcgovern.mit.edu

Even colleagues who should know better are claiming that we are halting animal research. No, we're not. Now this NYT opinion piece appears (gift link) www.nytimes.com/2025/10/08/o... You can't just move everything to cell culture, organoids, and computers. Even this NYT piece admits that. 1/3

🚨Our NeurIPS 2025 competition Mouse vs. AI is LIVE! We combine a visual navigation task + large-scale mouse neural data to test what makes visual RL agents robust and brain-like. Top teams: featured at NeurIPS + co-author our summary paper. Join the challenge! Whitepaper: arxiv.org/abs/2509.14446

Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness and Neural Alignment

Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environ...

arxiv.org

From the popular series "Figures that look like Kraftwerk Albums". Fig. 3 in Georgopoulos et al. Science 1986. Contributions of individual neurons to a shared coding direction in state space — pubmed.ncbi.nlm.nih.gov/3749885/

Using calcium imaging with single cell resolution across brain areas while mice solve two VR navigation problems, we discovered that neural manifolds can change in size based on working memory demands! Check out Mika Diamanti's amazing work: www.biorxiv.org/content/10.1...

Working memory expands shared task representations in cortex

Cognition is thought to emerge from the flexible organization of neural activity, yet how this organization reconfigures across behaviors varying in cognitive load remains unclear. We investigated how...

biorxiv.org

Working memory expands shared task representations in cortex https://www.biorxiv.org/content/10.1101/2025.09.29.679345v1

How does the brain decide? 🧠 Our new @nature.com paper shows that neural activity switches from an 'evidence gathering' to a 'commitment' state at a precise moment we call nTc. After nTc, new evidence is ignored, revealing a neural marker for the instant when the mind is made up. rdcu.be/eGUrv

Transitions in dynamical regime and neural mode during perceptual decisions - Nature

Simultaneous recordings were made of hundreds of neurons in the rat frontal cortex and striatum, showing that decision commitment involves a rapid, coordinated transition in dynamical regime and neura...

nature.com

Thrilled that our new review "Motor Working Memory" is now in press at TiCS! @cp-trendscognsci.bsky.social @cellpress.bsky.social By me + Hanna Hillman We argue that a dedicated research program on 'working memory for movements' is long overdue Link: authors.elsevier.com/a/1lmMX4sIRv...

(1/8) New paper from our team! Yu Duan & Hamza Chaudhry introduce POCO, a tool for predicting brain activity at the cellular & network level during spontaneous behavior. Find out how we built POCO & how it changes neurobehavioral research 👇 arxiv.org/abs/2506.14957