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Antonino Greco

@agreco

Computational Cognitive Scientist πŸ§ πŸ€– β€’ NeuroAI, Predictive Coding, RL & Deep Learning, Complex Systems β€’ Postdoc at @siegellab.bsky.social, @unituebingen.bsky.social β€’ Husband & Dad πŸŽ“ https://scholar.google.com/citations?hl=en&user=k5eR8_oAAAAJ

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01.10.2023
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Latest posts by Antonino Greco @agreco

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Ig Nobels ceremony moves to Europe over security concerns Marc Abraham: β€œDuring the past year, it has become unsafe for our guests to visit the country."

The IgNobel Prizes, usually bestowed at a ceremony in Cambridge, Mass, are moving to Europe this year due to security concerns for honorees and journalists coming to the US from abroad.

I bet I know who's going to win the IgNobel Peace Prize this year.

arstechnica.com/science/2026...

11.03.2026 14:20 πŸ‘ 91 πŸ” 16 πŸ’¬ 7 πŸ“Œ 2

The actual audience when you deliver a talk

πŸ™‚πŸ™‚πŸ˜—πŸ˜ŠπŸ§πŸ€“πŸ™‚
πŸ™‚πŸ€”πŸ₯±πŸ€”πŸ™‚πŸ€¨πŸ™‚
β˜ΊοΈπŸ™‚πŸ˜ŠπŸ™‚β˜ΊοΈπŸ˜ŒπŸ§

What you see when you deliver a talk

🀨πŸ₯±πŸ₯±πŸ€¨πŸ€¨πŸ₯±πŸ˜‘
πŸ₯±πŸ€¨πŸ€¨πŸ₯±πŸ˜‘πŸ˜πŸ˜
🀨πŸ₯±πŸ₯±πŸ€¨πŸ€¨πŸ₯±πŸ˜‘

11.03.2026 15:55 πŸ‘ 22 πŸ” 1 πŸ’¬ 5 πŸ“Œ 0
Dissecting Spectral Granger Causality through Partial Information Decomposition
Luca Faes, Gorana Mijatovic, Riccardo Pernice, Daniele Marinazzo, Sebastiano Stramaglia, Yuri Antonacci
Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The spectral PDGC was tested in physiological networks probed by measuring the variability series of arterial pressure, heart period, respiration and cerebral blood velocity in patients prone to neurally-mediated syncope compared to healthy controls. This application revealed unprecedented modes of physiological interaction, related to the sympathetic control of low-frequency cardiovascular and cerebrovascular oscillations, characterizing distinctive patterns of autonomic dysfunction. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.

Dissecting Spectral Granger Causality through Partial Information Decomposition Luca Faes, Gorana Mijatovic, Riccardo Pernice, Daniele Marinazzo, Sebastiano Stramaglia, Yuri Antonacci Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The spectral PDGC was tested in physiological networks probed by measuring the variability series of arterial pressure, heart period, respiration and cerebral blood velocity in patients prone to neurally-mediated syncope compared to healthy controls. This application revealed unprecedented modes of physiological interaction, related to the sympathetic control of low-frequency cardiovascular and cerebrovascular oscillations, characterizing distinctive patterns of autonomic dysfunction. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.

Dissecting Spectral Granger Causality through Partial Information Decomposition

arxiv.org/abs/2603.07634

10.03.2026 09:10 πŸ‘ 14 πŸ” 3 πŸ’¬ 0 πŸ“Œ 0
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Going to Cosyne? There will be a "Biologically-inspired Artificial Intelligence: Challenges and Opportunities" workshop on Monday 16th! 🧠

Exciting lineup of speakers alongside the opportunity to present your #NeuroAI poster, more info on program & poster signup below!

#compneuro #neuroscience

09.03.2026 12:16 πŸ‘ 43 πŸ” 7 πŸ’¬ 2 πŸ“Œ 0
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Compact deep neural network models of the visual cortex Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

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

26.02.2026 22:32 πŸ‘ 124 πŸ” 46 πŸ’¬ 3 πŸ“Œ 4
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πŸ“’ PhD position in the NeuroAI of Language

Why can LLMs predict brain activity so well? We're hiring a PhD student to find out -- AI interpretability meets neuroimaging
Deadline March 20
Please RT πŸ™
πŸ‘‡
mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-neuroai-language

05.03.2026 13:34 πŸ‘ 48 πŸ” 39 πŸ’¬ 2 πŸ“Œ 1
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I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs:
www.biorxiv.org/content/10.6...

04.03.2026 17:12 πŸ‘ 85 πŸ” 32 πŸ’¬ 2 πŸ“Œ 3

Delighted to share this paper led by @hannahmcdermott.bsky.social . We examined dynamics of prediction effects (sharpening vs dampening) and found they’re completely different across time scales!

04.03.2026 08:29 πŸ‘ 18 πŸ” 4 πŸ’¬ 0 πŸ“Œ 0

Absolutely agree with those principles, thanks for pointing them out! I believe improving scientific culture in this regard could have a profound impact, perhaps even comparable to major discoveries that have changed the course of science

04.03.2026 12:38 πŸ‘ 1 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

"how can we make this paper better?" assumes the authors sent a version of the work that need to be improved, while most of the times this is not the case. Peer-review should be only about correctness and impact, improvement should be only suggested and not central to the evaluation at all.

04.03.2026 09:01 πŸ‘ 1 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0

Peer review would be easier if we stopped treating it like combat with the authors. Results want to be free, and perfect is the enemy of good. Do the data support the conclusions? If so, that’s enough. A paper isn’t a blank slate for projecting your own ideas.

02.03.2026 17:56 πŸ‘ 110 πŸ” 24 πŸ’¬ 4 πŸ“Œ 1
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Lack of reviewers threatens robustness of neuroscience literature Simple math suggests that small groups of scientists can significantly bias peer review.

Broad peer review is crucial for a healthy scientific literature, but neuroscientists turn down review requests too often. Simple math suggests that small groups of scientists can significantly bias the literature, writes @jvoigts.bsky.social.

#neuroskyence

www.thetransmitter.org/publishing/l...

02.03.2026 15:04 πŸ‘ 56 πŸ” 23 πŸ’¬ 2 πŸ“Œ 7
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Raccoons optimally forage for information: exploration–exploitation trade-offs in innovation Animals in rapidly changing environments, such as cities, should optimize information gathering to learn about and efficiently forage in these heterog…

Raccoon information seeking! Very cool work on raccoons optimizing information gain in puzzle solving from @sarahba.bsky.social and lab:
www.sciencedirect.com/science/arti...

03.03.2026 18:12 πŸ‘ 28 πŸ” 7 πŸ’¬ 0 πŸ“Œ 1
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Orbitofrontal cortex drives predictive filtering of sensory responses - Nature Neuroscience Top-down projections from the orbitofrontal cortex carry predictive signals that grow with sound experience and suppress the auditory cortex via inhibitory circuits, revealing a predictive mechanism f...

Orbitofrontal cortex drives predictive filtering of sensory responses

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

#neuroskyence

02.03.2026 15:41 πŸ‘ 75 πŸ” 28 πŸ’¬ 0 πŸ“Œ 2

Why are tactile sensations suppressed during movements? In our new preprint, we explain this as optimal integration of sensory signals with an internal model.

Work led by @fatatai.bsky.social with Dimitris Voudouris, Katja Fiehler and @c-rothkopf.bsky.social

www.biorxiv.org/content/10.6...

27.02.2026 11:37 πŸ‘ 38 πŸ” 10 πŸ’¬ 1 πŸ“Œ 1

After several years of work, my lab is starting to put out our first papers on learning in a unicellular organism (Stentor coeruleus).

Here we show evidence for a form of associative learning in Stentor:
www.biorxiv.org/content/10.6...

26.02.2026 11:39 πŸ‘ 176 πŸ” 57 πŸ’¬ 5 πŸ“Œ 7
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Vectorized instructive signals in cortical dendrites - Nature Mice learning a neurofeedback brain–computer interface task show neuron-specific teaching signals in cortical dendrites, consistent with a vectorized solution for credit assignment in the brain.

This paper on how the brain may do gradient descent is very cool: www.nature.com/articles/s41...

26.02.2026 03:02 πŸ‘ 148 πŸ” 46 πŸ’¬ 3 πŸ“Œ 2
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Renormalization in the brain?
With @andreasantoro.bsky.social and @jesseba.bsky.social we're organizing a workshop at @cosynemeeting.bsky.social (Lisbon, March 16) on how coarse-graining principles from physics can explain neural computation across scales. nplresearch.github.io/neurorenorm2...

25.02.2026 08:11 πŸ‘ 10 πŸ” 6 πŸ’¬ 0 πŸ“Œ 2
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Screening, sorting, and the feedback cycles that imperil peer review The process of peer review is vital to contemporary science, but is also under enormous strain. This study uses mathematical models to dissect the threats to the long-term viability of peer review, su...

1. Kevin Gross and I have a new paper out today PLOS Biology.

We used economic models based around screening games and the market for unpaid labor to highlight a meltdown cycle threatening peer review.

24.02.2026 20:54 πŸ‘ 324 πŸ” 132 πŸ’¬ 8 πŸ“Œ 17
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Our bilingualism paper is now published in PNAS. We used fMRI to compare semantic brain representations in English-Chinese bilinguals. Semantic representations are largely shared across languages, but finer-grained differences modulate how meaning is represented.
www.pnas.org/doi/10.1073/...

24.02.2026 19:02 πŸ‘ 28 πŸ” 4 πŸ’¬ 0 πŸ“Œ 0

TBH the thing that is most sad about the loss of ~2015-2021 era science twitter is the loss of a public square where grad students can see, and participate in, the diversity of expert opinions in the field. It really shaped how I see science.

23.02.2026 14:41 πŸ‘ 63 πŸ” 16 πŸ’¬ 8 πŸ“Œ 1
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We've posted a new fMRI study of semantic relations (has-part, is-a, made-of, etc.), a key aspect of language. We find that relations are represented in the same brain regions as are other semantic concepts, though voxels tend to be selective for only one relation or another.
doi.org/10.64898/202...

23.02.2026 21:06 πŸ‘ 58 πŸ” 23 πŸ’¬ 1 πŸ“Œ 2
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Forward and backward prediction in learning and perception Predictive processing frameworks have emphasized the role of forward prediction as a critical ingredient for learning and perceptual inference. We ant…

These findings align well with what @clarepress.bsky.social and I discuss in our "Forward and backward prediction" paper, see www.sciencedirect.com/science/arti...

23.02.2026 12:54 πŸ‘ 6 πŸ” 2 πŸ’¬ 0 πŸ“Œ 0
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What type of relationship is learned during visual statistical learning? Statistical learning enables observers to extract regularities from their environment, but what statistical regularity is extracted remains debated. While previous research has mainly focused on condi...

Which "statistic" governs statistical learning? Find out in this new article of our lab: journals.plos.org/plosone/arti...

23.02.2026 12:53 πŸ‘ 18 πŸ” 4 πŸ’¬ 1 πŸ“Œ 1
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The strongest version of this illusion I’ve seen! Absolute head-wrecker!

21.02.2026 16:36 πŸ‘ 385 πŸ” 123 πŸ’¬ 24 πŸ“Œ 29

underappreciated concept πŸ™‚

for more dipoles: neighboring dipoles have different orientations & can have different stimulus preferences. so even though topographies look similar, subtle differences remain. maybe that's why it is possible to decode information from relatively few sensors?

20.02.2026 11:46 πŸ‘ 20 πŸ” 8 πŸ’¬ 1 πŸ“Œ 0

Recent work has shown how vulnerable online survey research is to LLMs. Motivated by this, we examined our online Posner cueing data from Prolific. It's concerning. We now must carefully consider when (or whether?) online behavioral data can be trusted.
see our comment:
www.pnas.org/doi/10.1073/...

19.02.2026 12:00 πŸ‘ 76 πŸ” 34 πŸ’¬ 6 πŸ“Œ 4
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Hiring now!
πŸ“’ The CRC 1233 Robust Vision consortium at @unituebingen.bsky.social is looking for a Research Coordinator to lead science management, support project execution, and help steer our interdisciplinary research in neuroscience, machine learning and computer vision.

19.02.2026 14:03 πŸ‘ 7 πŸ” 4 πŸ’¬ 1 πŸ“Œ 0
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Man I really need to go back and watch this show again

19.02.2026 13:45 πŸ‘ 57 πŸ” 17 πŸ’¬ 3 πŸ“Œ 4

I completely agree. I think accurately predicting future sensory input is rewarding per se!

This is because it’s one of the brain’s most fundamental objectives, arguably second only to survival itself

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

bsky.app/profile/agre...

16.02.2026 15:25 πŸ‘ 4 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0