Laboratory for Neural Circuit and Behavior, School of Biological and Behavioural Sciences, Queen Mary University of London

Joined March 2015
9 Photos and videos
Benucci Lab retweeted
Great blog post from @ItsNeuronal on the interpretation of Log-Likelihood Ratios for model comparison, including a (new to me!) result on how to obtain a significance threshold for declaring one model better than another: neurostatsblog.github.io/202โ€ฆ
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Benucci Lab retweeted
๐—ฆ๐—ฝ๐—ฎ๐˜๐—ถ๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐˜€๐˜๐—ฟ๐—ถ๐—ฏ๐˜‚๐˜๐—ฒ๐—ฑ ๐—ฝ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐˜€๐˜€๐—ถ๐—ป๐—ด ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜‚๐˜€๐—ฒ ๐—ฏ๐—ฟ๐—ฎ๐—ถ๐—ป Leaving aside the more extreme discussions about "everything everywhere" and modularity this is a very cool paper. Data from 260 regions and 60K neurons. doi.org/10.1101/2025.07.30.6โ€ฆ
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Benucci Lab retweeted
30 Oct 2025
1/๐Ÿงต Out today in @FrontiersIn @FrontScience: โ€œConsciousness science: where are we, where are we going, and what if we get there?" By @axelcleeremans, @Liad_Mudrik, & me (a @CIFAR_News & @ERC_Research production๐Ÿง ). frontiersin.org/journals/sciโ€ฆ
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Benucci Lab retweeted
I did a podcast with Jon Stewart who has always been a hero of mine. It was a lot of fun. He really wanted to understand how AI works. youtube.com/watch?v=jrK3PsD3โ€ฆ
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Benucci Lab retweeted
A pair of papers on using holographic optogenetics and compressed sensing for connectomics Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing nature.com/articles/s41593-0โ€ฆ
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Benucci Lab retweeted
๐Ÿ”ฆ New @TrendsNeuro spotlight on our recent @NatureComms paper! How do brains generalise spatial information across sensory modalities? Our study identifies a region in the dorsal cortex of mice enabling them to transfer spatial knowledge between vision and touch!๐Ÿ–๐Ÿป๐Ÿ‘€ ๐Ÿ”—cell.com/trends/neuroscienceโ€ฆ
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Benucci Lab retweeted
My postdoc work "Unsupervised pretraining in biological neural networks" is out now, along with more than 400GB (milliions neurons) neural data. nature.com/articles/s41586-0โ€ฆ. Figshare and Github links can be found in the paper.
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Benucci Lab retweeted
Video prediction foundation models implicitly learn how objects move in videos. Can we learn how to extract these representations to accurately track objects in videos _without_ any supervision? Yes! ๐Ÿงต Work done with: @Rahul_Venkatesh, @SeKim1112, @jiajunwu_cs and @dyamins
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Benucci Lab retweeted
25 Mar 2025
We just finished up Winter quarter CS375: Large-Scale Neural Network Models for Neuroscience. Check out the publicly available Syllabus and lecture notes cs375.stanford.edu/course-caโ€ฆ

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๐ŸšจFully funded PhD opportunity at QMUL, London!๐Ÿšจ Join us to explore optogenetic control of visual perception. Open to UK nationals & UK-ILR residents. Please share! ๐Ÿ‘‡ ๐Ÿ”— shorturl.at/77f6U
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Benucci Lab retweeted
New study from our lab, in collab with @erlichlab We developed novel frameworks to study multi-agent decision-making in mice. Mice flexibly shift their value preference under social competition, by integrating real-time self and opponent information! biorxiv.org/content/10.1101/โ€ฆ
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Benucci Lab retweeted
We always see that: 1) neural responses are very diverse 2) the shattering dimensionality is as high as it can be. Now also in an extensive analysis of the IBL dataset. Wonderful collaboration with @LorenzoPosani , Shuqi Wang, Samuel Muscinelli, Liam Paninski. Many new analyses
Long-overdue thread on our latest work using the IBL data to reveal the shared organizational principles of the neural code in the cortex. A systematic analysis of categoricality ๐Ÿงฑ and dimensionality ๐Ÿ“ of the neural code across 40 regions. doi.org/10.1101/2024.11.15.6โ€ฆ ๐Ÿ‘‡ 1/n
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Benucci Lab retweeted
25 yrs ago, perception dominated neuroscience, and Raj's @RajeshPNRao predictive coding theory. Now, it's all about action-perception loops across the hierarchy... "active predictive coding." Also, augmenting our cognition with AI neural co-processors! braininspired.co/podcast/201โ€ฆ
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Benucci Lab retweeted
New results! Visual adaptation changes the geometry of V1 population activity: frequent stimuli elicit smaller responses but become more discriminable, consistent with our efficient coding model. You can find me on the "new neurotwitter" at mariodipoppa. biorxiv.org/content/10.1101/โ€ฆ
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