Benucci Lab

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

[Anil Seth @anilkseth]

Is it a duck, or is it a rabbit? (How) do multimodal large language models (MLLMs) report bistable images? Terrific new work led by @SussexCentre PhD student @rtakatsky - to be presented at #EMNLP2026. Paper and explainer video below 👇🏽

[Benucci Lab @BenucciLab]

https://t.co/xU2t31WtZz

[Kenneth D Harris @kennethd_harris]

Watch this.

Seriously. Whatever you are doing, stop doing that and watch this instead.

https://t.co/7HwLJLMPrK

[Kanaka Rajan @KanakaRajanPhD]

Our approach for untangling brain-wide interactions is out today in @NeuroCellPress!

Massive congrats to @mattperich for leading this incredible effort since 2020, and a huge thank you to all of our collaborators.

https://t.co/aIjzMDpQnE

[Dr. Chunyu Ann Duan @AnnDuan2]

New preprint from the lab!
Behavioral and neural mechanisms for stochastic choices in mixed-strategy games: https://t.co/48OlCsCwiW

We use game theoretic tasks, cross species comparisons, modelling strategies, and mesoscale imaging to probe how we can escape predictability!

[Anthropic @AnthropicAI]

New Anthropic research: A global workspace in language models.

Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with.

We found a strikingly similar divide inside Claude.

[Jonathan Pillow @jpillowtime]

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:

https://t.co/lMCHaYXA5x

[Benucci Lab @BenucciLab]

How psychedelics push your brain to dream while awake – new study https://t.co/tsnnsSRaPl via @ConversationUK

[nature @Nature]

Nature research paper: Evidence accumulation from experience and observation in the cingulate cortex

https://t.co/OXk7uCmNyO

[Luiz Pessoa @PessoaBrain]

𝗦𝗽𝗮𝘁𝗶𝗮𝗹𝗹𝘆 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗼𝘂𝘀𝗲 𝗯𝗿𝗮𝗶𝗻
Leaving aside the more extreme discussions about "everything everywhere" and modularity this is a very cool paper. Data from 260 regions and 60K neurons.
https://t.co/uVZ77bpLIE

[Anil Seth @anilkseth]

1/🧵 Out today in @FrontiersIn @FrontScience: “Consciousness science: where are we, where are we going, and what if we get there?" By @axelcleeremans, @Liad_Mudrik, & me (a @CIFAR_News & @ERC_Research production🧠). https://t.co/HNzCaDPsF8

[Geoffrey Hinton @geoffreyhinton]

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.
https://t.co/frLBndsW7g

[Nature Neuroscience @NatureNeuro]

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

https://t.co/amA4i1BoGV

[Giulio Matteucci @giulio_matt]

🔦 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!🖐🏻👀 🔗https://t.co/sz1meIv0It

[David Sussillo @SussilloDavid]

https://t.co/iFlXhLumIq

[Lin Zhong @Lin_Zhong_ion]

My postdoc work "Unsupervised pretraining in biological neural networks" is out now, along with more than 400GB (milliions neurons) neural data. https://t.co/2uI5YAQ0vg. Figshare and Github links can be found in the paper.

[RIKEN @riken_en]

Press Release -- New research shows how brain circuitry produces inferred emotions, allowing a better understanding of anxiety. #Emotions #BrainScience #RIKEN #MentalHealth @RIKEN_CBS @JoJoLab5 https://t.co/yZ2FuwhmqG https://t.co/i0T6nMGr3u

[Benucci Lab @BenucciLab]

@TJRyan_77 @JoJoLab5 @Andrea_MZamora @Akaron0884 @tcddublin @lynchielydia @DrChristineAnnD @RyanLabTCD @Nature Wonderful work. "Engram cells can be defined as neuronal ensembles [...] the reactivation of which leads to the retrieval of the target memory". Could replacing "memory" with "computation" relate engrams to, for example, sparse coding in neural nets?

[Stefan Stojanov @sstj389]

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! 🧵

Work done with: @Rahul_Venkatesh, @SeKim1112, @jiajunwu_cs and @dyamins

[Daniel Yamins @dyamins]

We just finished up Winter quarter CS375: Large-Scale Neural Network Models for Neuroscience. Check out the publicly available Syllabus and lecture notes https://t.co/QUzfYRde8o