Friedemann Zenke

We're hiring,, of interest to people in spiking neural networks and neuromorphic particularly. See below. 👇

Note the deadline for applications is very soon! Apologies for this but various admin necessities made it unavoidable.

Publishing with eLife: “the future of science lies in greater transparency” Neuroscientist Magdalena Solyga shares her latest study and her experience publishing with eLife.

Ditching months-long delays for fast, constructive feedback.

This interview with @solygamagda.bsky.social dives into the experience of publishing with eLife and what it could mean for a more open and efficient future in science.

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.

Got prov. approval for 2 major grants in Neuro-AI & Dynamical Systems Reconstruction, on learning & inference in non-stationary environments, out-of-domain generalization, and DS foundation models. To all AI/math/DS enthusiasts: Expect job announcements (PhD/PostDoc) soon! Feel free to get in touch.

Here is your last reminder that the application deadline for Imbizo.Africa is nearing quickly, the 1st of July, in fact tomorrow. Still the place where diversity is at its best in the world! Tell all who need to hear. #africa #neuro

My latest Aronov lab paper is now published @Nature!

When a chickadee looks at a distant location, the same place cells activate as if it were actually there 👁️

The hippocampus encodes where the bird is looking, AND what it expects to see next -- enabling spatial reasoning from afar

bit.ly/3HvWSum

How does in-context learning emerge in attention models during gradient descent training?

Sharing our new Spotlight paper @icmlconf.bsky.social: Training Dynamics of In-Context Learning in Linear Attention
arxiv.org/abs/2501.16265

Led by Yedi Zhang with @aaditya6284.bsky.social and Peter Latham

The deadline for the VIB.AI group leader positions is approaching - send in your CV and short research plan before 14th June to start your BioML research lab in Leuven or Ghent

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...

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...

How brain networks balance learning and memory FMI researchers have provided new insights into how the brain organizes and processes memories, thanks to a study that looks at the balance between excitatory and inhibitory neurons. Memory networks h...

In a @elife.bsky.social study, FMI researchers show that memory storage may rely on dynamic interplay between excitatory and inhibitory neurons — challenging the idea of stable activity patterns in memory networks. www.fmi.ch/news-events/...

Absolutely. The idea is that in addition to the very important role of keeping this delicate equilibrium, deviations from balance could serve yet another purpose.

6/6 BCP does not only work. Trained networks also replicate in-vivo dynamics of SOM and VIP interneurons during motor learning and fear conditioning experiments. Our model takes a step toward linking neuronal circuits with plasticity and behavior.

5/6 In multi-layer networks, BCP enables online learning without separate training phases and segregated dendrites, while only using feedback-driven E/I balance perturbations.

4/6 We formalize this idea as Balance-Controlled Plasticity (BCP), a plasticity framework grounded in adaptive control theory, which simplifies to a Hebbian-like learning rule modulated by recurrent inhibition. This learning rule is both biologically plausible yet powerful.

3/6 We propose feedback signals may target interneurons to transiently disrupt E/I balance. These controlled deviations can efficiently encode local error signals, allowing to guide plasticity without the need for special dendritic morphology.

2/6 Existing theories of bio-plausible learning and credit assignment often rely on segregated dendrites to encode neuronal errors.

However, not all neurons have morphologically well-separated dendrites while data-driven plasticity models seem at odds with such error modulated learning.

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

Nice. This looks interesting. I am curious. What do you mean by surrogates are noisy? This surely depends on the surrogate, right? And what makes you say that batchnorm is brittle?

*** Come join us !! We are recruiting !! ***
How does the brain generate beliefs that go beyond direct experience? How do these mechanisms go awry in psychosis?

The Barron lab tinyurl.com/bdf4drjf @MRCBNDU @NDCNOxford are recruiting a new postdoc tinyurl.com/ut4vbwt4 to work in mouse models

Your opportunity to apply for the #LakeConference on Neural Coding & Dynamics is going to set sail soon! 🛥️

Apply by May 23 to join us for this intimate, interactive conference.

📆 Sep. 28 to Oct. 2
📍 Seattle, USA
🧠📈 All career stages welcomed

More info: alleninstitute.org/events/lake-...

That is very sad. I am sorry. I still remember when he showed me his counting skills.

Filter? Do they not make apertures any more?

A prospective code for value in the serotonin system - Nature Merging ideas from reinforcement learning theory with recent insights into the filtering properties of the dorsal raphe nucleus, a unifying perspective is found explaining why serotonin neurons are ac...

I'm excited to share that the last chapter of my PhD thesis is now published in Nature! 🍾

What drives serotonin neurons? We think it's the expectation of future reward and --- critically --- how fast this expectation is increasing. 📈

doi.org/10.1038/s415...

1/6

Uncertainty estimation with prediction-error circuits - Nature Communications How the brain integrates sensory input and predictions to adapt to change is not fully understood. Here authors build a neural network model to show how prediction-error neurons compute uncertainty of...

How should you combine sensory inputs with your internal predictions? Well, use uncertainty directly computed by your prediction-error circuits. Cool? Check out our Nat. Com. paper with Loreen Hertäg and @k47h4.bsky.social . www.nature.com/articles/s41...

Congrats to Julian Rossbroich on his PhD defense! 🎉 Julian's theoretical research suggests that inhibitory microcircuits help the brain balance excitation and inhibition for more efficient learning 🧠

The Clopath Lab has a fully funded PhD position open for Oct in Computational Neuroscience at Imperial College London. If you are interested, just send us an informal e-mail!

ZAPBench ZAPBench evaluates how well different models can predict the activity of over 70,000 neurons in a novel larval zebrafish dataset.

⚡️ Excited to introduce ZAPBench, our #ICLR2025 spotlight: The Zebrafish Activity Prediction Benchmark measures progress in predicting neural activity within an entire vertebrate brain (70k+ neurons!)

Explore interactive visualizations, datasets, code + paper: google-research.github.io/zapbench

🧠🧪

Just in case you - as we did - were wondering whether humans would have any of these pesky visuomotor mismatch responses certain people have seen in mice...

Breaking the barrier between theorists and experimentalists Many neuroscience students are steeped in an experiment-first style of thinking. Let’s not forget how theory can guide experiments.

Sam Gershman writes beautifully about how theory-free neuroscience prevents the field from reaching its promise. Beautiful and true. Most folks do not test hypotheses. Running a NHST does not a hypothesis make. www.thetransmitter.org/theoretical-...