Finally out in @natneuro.nature.com! A forgotten memory isn't always gone. We show that forgotten memories persist as silent memory traces, can be recovered by reminders, and can even be reconstructed into false memories. Excited to finally share this work! www.nature.com/articles/s41...

If an action results in error, each neuron requires an individualized teaching signal that guides change in its output. This is the credit assignment problem of learning. Are there neurons in the brain that can compute such a sophisticated teaching signal? Yes. www.biorxiv.org/content/10.6...

Climbing fibers encode the gradient of a loss function for the cerebellum

Neurons in the brain are often many synapses away from motoneurons, yet if a movement results in error, each distant neuron needs a teacher that considers its specific contribution to production of th...

biorxiv.org

New preprint (well, very updated). 🤖🧠🧪 We find that an abstract model of neuromodulation lets spiking neural networks perform much better, particularly in challenging noisy environments, using less energy. Relevant to #neuroscience and #neuromorphic computing. 🧵👇 www.biorxiv.org/content/10.1...

Neuromodulation enhances the capability and efficiency of spiking neural networks

Spiking neurons underlie the brain’s extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency in practice has proven challenging. We ...

biorxiv.org

Two brain circuits. Same learning problem. Shared learning outcomes. Different dynamical implementations. Our work shows that learning is not a single canonical solution but can emerge through distinct population dynamics shaped by circuit architecture. Excited to share our latest preprint! 🧵1/12

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 2mo ago

Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus https://www.biorxiv.org/content/10.64898/2026.06.04.730137v1

Story time friends... Ring attractor networks rely on fine-tuned symmetric connectivity. The fly head direction network has ring attractor dynamics but heterogeneous connectivity. How is this possible? 1/🧵 Link: www.biorxiv.org/content/10.6...

1/7 New paper accepted as ICML spotlight arxiv.org/abs/2605.03517! We unify self-supervised learning (SSL) algorithms (e.g., contrastive, VICReg, stopgrad) via latent distribution matching (LDM), which matches an induced latent distribution to an explicit latent model.

For nearly a century, we believed the therapeutic effect of ECT is the seizure. Our latest research suggests we may have been looking at the wrong event. A thread on why cortical spreading depression (CSD) might be the driver of therapeutic benefit. Work led by @therehugolad.bsky.social

Our new preprint on parallelizing training of temporally precise spiking neural networks is out! We show up to 44x speedups over a conventional sequential baseline. 1/N

Biology is full of coconuts. 🥥

Joseph Guhlin@josephguhlin.bsky.social · 4mo ago

Coconuts

🧵 New preprint led by @bingbrunton.bsky.social, @elliottabe.bsky.social, @lawrencehu.bsky.social We gave a worm brain control of a fly body and it walked What did we learn? Nothing, other than deep reinforcement learning is effective We call it the digital sphinx www.biorxiv.org/content/10.6...

‪Ever wondered how GABAergic interneurons shape cognition? The IN-CODE consortium's latest NeuroView article introduces a "population approach", shifting the focus from individual interneurons to cooperative networks. Dive into the future of interneuron research here: doi.org/10.1016/j.ne...

Pleased to share that our paper "Representation Biases: Variance is Not Always a Good Proxy for Importance" is now out as Theory/New Concepts paper in eNeuro! www.eneuro.org/content/13/3... 1/

Representation Biases: Variance Is Not Always a Good Proxy for Importance

A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and repr...

eneuro.org

Come see our Cosyne 2026 posters! Friday: 2-069 (Atena & Manu), 2-096 (Julian), Saturday: 3-091 (Julia) More info zenkelab.org/2026/03/cosy...

New paper hot off the (pre-)press! We dig into the evolutionary origins of neural computations for behavioral control across mice, monkeys, and humans: www.biorxiv.org/content/10.6.... As our lab's first foray into comparative analysis of neural dynamics, I’m super excited about this work! 1/18

Happy to announce our latest preprint with Friedrich Schuessler and Simone Ciceri: www.biorxiv.org/content/10.6... A good part of animal behaviour and cognition is innate. Have you ever wondered how the underlying neural circuits develop? We may have a suggestion.

We introduce epiplexity, a new measure of information that provides a foundation for how to select, generate, or transform data for learning systems. We have been working on this for almost 2 years, and I cannot contain my excitement! arxiv.org/abs/2601.03220 1/7

Wanna compare dynamics across neural data, RNNs, or dynamical systems? We got a fast and furious method🏎️ The 1st preprint of my PhD 🥳 fast dynamical similarity analysis (fastDSA): 📜: arxiv.org/abs/2511.22828 💻: github.com/CMC-lab/fast... I’ll be @cosynemeeting.bsky.social - happy to chat 😉

1/ Why does RL struggle with social dilemmas? How can we ensure that AI learns to cooperate rather than compete? Introducing our new framework: MUPI (Embedded Universal Predictive Intelligence) which provides a theoretical basis for new cooperative solutions in RL. Preprint🧵👇 (Paper link below.)

1/6 New preprint 🚀 How does the cortex learn to represent things and how they move without reconstructing sensory stimuli? We developed a circuit-centric recurrent predictive learning (RPL) model based on JEPAs. 🔗 doi.org/10.1101/2025... Led by @atenagm.bsky.social @mshalvagal.bsky.social

Excited to see the paper fully published. It's an important milestone for training SNNs with exact gradients, replacing our earlier tricks of a "delay line augmentation" to capture temporal relationships. Delays can now be learnt alongside weights naturally. Amazing work @mbalazs98.bsky.social !

Balázs@mbalazs98.bsky.social · 8mo ago

Our paper on event-based delay learning is now published! @neworderofjamie.bsky.social @drtnowotny.bsky.social TL;DR: It’s now possible to train synaptic delays in large-scale spiking neural networks with high temporal precision—even in recurrent connections. www.nature.com/articles/s41...

Spiking neural networks people, this message is for you! The annual SNUFA workshop is now open for abstract submission (deadline Sept 26) and (free) registration. This year's speakers include Elisabetta Chicca, Jason Eshraghian, Tomoki Fukai, Chengcheng Huang, and... you? snufa.net/2025/ 🤖🧠🧪

What makes visual processing in the brain so powerful and flexible? Very excited to share our new work where we started from SOTA models that accurately predict dynamic brain activity during hours of video watching, and investigated core computations underlying visual perception

Yingtian (David) Tang@davidtyt.bsky.social · last yr.

🧠 NEW PREPRINT Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective www.biorxiv.org/content/10.1...

There might be a bit of misconception here. What the paper very convincingly shows is that visual cortex does not compute global oddball prediction errors and does not receive any top-down predictions that could be used to compute such prediction errors.

It's officially published!! In my main postdoc work with @markplitt.bsky.social and @lgiocomo.bsky.social, we found that the hippocampus simultaneously encodes an animal's spatial position and its experience relative to reward in parallel population codes. 🧵 www.nature.com/articles/s41...

A flexible hippocampal population code for experience relative to reward - Nature Neuroscience

Sosa et al. find that hippocampal neural activity in mice encodes both environmental location and experience relative to rewards, spanning distances far from reward, through parallel and flexible popu...

nature.com

We just pushed “Memory by a 1000 rules” onto bioRxiv, where we use clever #ML to find #plasticity quadruplets (EE, EI, IE, II) that learn basic stability in spiking nets. Why is it cool? We find 1000s!! of solutions, and they don’t just stabilise. They #memorise! www.biorxiv.org/content/10.1...

Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety & degeneracy at the heart of learning

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of str...

biorxiv.org

Forelimb movement control at the basal ganglia - brainstem interface! Happy to finally share this work from me and @harsh-kanodia.bsky.social with Silvia Arber! @biozentrum.unibas.ch @fmiscience.bsky.social www.nature.com/articles/s41...

Dynamic basal ganglia output signals license and suppress forelimb movements - Nature

Basal ganglia output neurons fire dynamically in bidirectional and movement-specific patterns to license forelimb movements. 

nature.com

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)

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

Cellular and subcellular specialization enables biology-constrained deep learning www.biorxiv.org/content/10.1...

I've spent much of my PhD thinking about E/I balance, and our latest preprint represents the culmination of that journey. Huge thanks to @fzenke.bsky.social for guiding me. Looking forward to your thoughts & comments.

Friedemann Zenke@fzenke.bsky.social · last yr.

1/6 Why does the brain maintain such precise excitatory-inhibitory balance? Our new preprint explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signals for learning. www.biorxiv.org/content/10.1... led by @jrbch.bsky.social

New preprint with my postdoc, Navid Shervani-Tabar, and former postdoc, Marzieh Alireza Mirhoseini. Oja’s plasticity rule overcomes challenges of training neural networks under biological constraints. arxiv.org/abs/2408.08408

Oja's plasticity rule overcomes several challenges of training neural networks under biological constraints

Deep neural networks have achieved impressive performance through carefully engineered training strategies. Nonetheless, such methods lack parallels in biological neural circuits, relying heavily on n...

arxiv.org

Top-down feedback is ubiquitous in the brain and computationally distinct, but rarely modeled in deep neural networks. What happens when a DNN has biologically-inspired top-down feedback? 🧠📈 Our new paper explores this: elifesciences.org/reviewed-pre...