Fatemeh_Hadaeghi

BIG ANNOUNCEMENT📣: I haven’t been this excited to be part of something new in 15 years… Thrilled to reveal the passion project I’ve been working on for the past year and a half!🙀🥳 (thread 👇)

Spiking NN fans - the #SNUFA workshop (Nov 5-6) agenda is finalised and online now. Make sure to register (free) soon. (Note you can register for either day and come to both.)

Agenda: snufa.net/2025/
Registration: www.eventbrite.co.uk/e/snufa-2025...

Thanks to all who voted on abstracts!

🤖🧠🧪

🚨 New preprint!
“A Computational Perspective on the No-Strong-Loops Principle in Brain Networks”
www.biorxiv.org/content/10.1...

Over the past 3 years, we’ve been investigating why cortical networks avoid strong reciprocal loops — and what this means for computation.

A thought-provoking perspective from the visionary @giacomoi.bsky.social, calling for neuromorphic computing to return to its root: fundamental neuroscience; an inspiring vision for the future of NeuroAI 🤩

I would also like to thank prominent figures in the field, Sara Solla, Petra Vertes, @kenmiller.bsky.social, @bendfulcher.bsky.social, @jlizier.bsky.social, @danakarca.bsky.social, MarcusKaiser, Gorka Zamora-López, and Patrick Desrosiers, who provided feedback during lab visits and conferences.

This work has been in progress for 3+ years.
Grateful to my co-authors, Claus Hilgetag, @kayson.bsky.social, and Moein Khajehnejad for their invaluable contributions,

Implications:
🧠 Neuroscience — functional rationale for the evolutionary suppression of strong reciprocal motifs.
🤖 NeuroAI — reciprocity as a tunable design parameter in recurrent & neuromorphic networks.

So why does the brain avoid strong loops?
Because reciprocity systematically hurts computation.

Suppressing strong loops preserves:
working memory
representational diversity
stable but flexible dynamics

We validated this on empirical connectomes (macaque long-distance, macaque visual cortex, marmoset).

Result: the same pattern.
Strong reciprocity consistently undermines memory and representational richness.

Spectral analysis explains why:

- Higher reciprocity → larger spectral radius (instability risk).
- Narrower spectral gap → less dynamical diversity.
- Lower non-normality → weaker transient amplification.

Together → compressed dynamical range.

Interestingly, hierarchical modular networks consistently outperformed random counterparts, but only when reciprocity was low. However, the comparative advantages of network topologies shift with reciprocity, sparsity, and weight distribution

Findings (robust across sizes, densities, architectures):
- Increasing reciprocity (link as well as strength reciprocity) reduces memory capacity.
- Representation becomes less diverse (lower kernel rank).
- Effects are strongest in ultra-sparse networks.

Methods:
- Reservoir computing to isolate structure from learning.
- Networks of 64–256 nodes, in both ultra-sparse and sparse regimes.
- Topologies: small-world, hierarchical modular, core–periphery, hybrid, and nulls.
- Metrics: memory capacity, kernel rank, spectral analysis.

In earlier work (www.biorxiv.org/content/10.1...), we developed Network Reciprocity Control (NRC): algorithms that adjust reciprocity (link + strength) while preserving network structure.

In this study, we apply NRC to systematically test how reciprocity shapes computation in recurrent networks.

The no-strong-loops principle:
Across species (macaque, marmoset, mouse), strong reciprocal (symmetric) connections are rare.

This asymmetry is well known anatomically.
But what are its computational consequences?

🚨 New preprint!
“A Computational Perspective on the No-Strong-Loops Principle in Brain Networks”
www.biorxiv.org/content/10.1...

Over the past 3 years, we’ve been investigating why cortical networks avoid strong reciprocal loops — and what this means for computation.

Interested in Network hubs, cortical hierarchies, and gradients? Ever wonder where they come from? Check our latest review, where we cover different approaches to mapping hubs, models for their evolution, and mechanisms for how they develop:

osf.io/preprints/os...

GitHub - dionnecargy/melbourne: A package love letter to Melbourne A package love letter to Melbourne. Contribute to dionnecargy/melbourne development by creating an account on GitHub.

Have you ever been transfixed by the colour palette of the Melbourne tram network?

Well I have... so I made a package called {melbourne}! So far it includes a colour palette called "melb_trams()". More to come, stay tuned!

🔗 Check it out: github.com/dionnecargy/...

#RStats #DataScience #Rcoding

Good morning folks. If you’re around #OCNS2025, maybe come by today for a chat about optimal communication in brain networks? ✨

Wonderful work by Shrey and Kayson in developing an XAI method for exploring the contribution of any computational unit (e.g., nodes, experts, communities, filters) within neural networks. It enables analysis of both their influence on each other and their overall impact on task performance.

Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units Neural networks now generate text, images, and speech with billions of parameters, producing a need to know how each neural unit contributes to these high-dimensional outputs. Existing explainable-AI ...

🚨Preprint Alert: Who Does What in Deep Learning? Multidimensional Contribution of Neural Units using Game Theory: arxiv.org/abs/2506.19732
The result of my MSc thesis is out with @kayson.bsky.social @fatemehhadaeghi.bsky.social @patrickmineault.bsky.social @kordinglab.bsky.social, Claus C. Hilgetag

Expectation violations as an effective alternative to complex mentalizing in novel communication Neuroscience; Systems neuroscience; Social sciences

✨Excited to share that our new paper is now out in iScience!✨

🧠 We show that people can coordinate surprisingly well in novel interactions by violating others' expectations - without requiring deep, recursive reasoning about others’ beliefs.

📄 Read the full paper here: www.cell.com/iscience/ful...

How should #multisensory signals be combined when they are structured in time?

To explore this, we:
* Introduce a new multisensory task
* Compare several models

TLDR:
* Prior models perform suboptimally
* Our new model performs ≈ an RNN, while using less than 1/10th the number of parameters.

The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear. We p...

1/ I am very excited to announce that our paper "The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training" is available on arXiv 💜

arxiv.org/abs/2502.10927

How is information encoded in self-attention matrices?How to interpret it?

⬇️

Doctoral fellow

PhD fellowship to work with me and Benedetta Franceschiello on the analysis and modelling of fast sampled fMRI data!

www.ugent.be/en/work/scie...

Integrating multimodal data to understand cortical circuit architecture and function - Nature Neuroscience This paper discusses how experimental and computational studies integrating multimodal data, such as RNA expression, connectivity and neural activity, are advancing our understanding of the architectu...

A Perspective on integrating multimodal data to understand cortical circuit architecture and function

@alleninstitute.bsky.social

www.nature.com/articles/s41...

Excited to share our latest preprint on connectivity modulation with dual-site tACS! We show that in-phase tACS at 20 Hz can disrupt fMRI connectivity between the primary motor cortices, but also affects connectivity with other motor regions.

www.biorxiv.org/content/10.1...

wodyetia bifurcata tree

wodyetia bifurcata tree

wodyetia bifurcata Tree

Found a #bifurcation (bifurcated ?) #tree outside the Royal Botanic garden, #Sydney 😬

A neuromorphic multi-scale approach for real-time heart rate and state detection - npj Unconventional Computing npj Unconventional Computing - A neuromorphic multi-scale approach for real-time heart rate and state detection

🌟 Paper out in npj Unconventional Computing!
www.nature.com/articles/s44...

A system built with just a few neurons, yet able to solve a complex task — not by stacking layers or going deeper, but by embracing unconventional thinking.

This is neuromorphic to me!

Applications are now open! 3-week courses: Comp Neuro and Deep Learning. 2-week courses: NeuroAI and Comp Tools for Climate Science.

🚨 Neuromatch Academy Course Applications are OPEN for 2025!! 🚨

Get your application in early to be a student or teaching assistant for this year’s courses!

Applications are due Sunday, March 23.

Apply & learn more: neuromatch.io/courses/

#mlsky #compneurosky #ai #climatesolutions #ScienceEdu 🧪