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@stefanofusi.bsky.social

12 Posts891 Follower225 Folgt
@stefanofusi.bsky.social · 2mo ago

Specialized readouts are also possible when most neurons are not specialized, if you have the right representational geometry. Another great work with D. Salzman's lab: led by Pia O'Neill and @lorenzoposani.com, who did a wonderful job

Lorenzo Posani@lorenzoposani.com · 2mo ago

Fin/ check out the paper at www.nature.com/articles/s41...! Huge thanks to Pia O'Neill (new assistant prof at Dartmouth sites.dartmouth.edu/oneill-lab/) for leading this project, and to @stefanofusi.bsky.social & Daniel Salzman for the fantastic collaboration! Cover artwork matteofarinella.com/

@stefanofusi.bsky.social · 9mo ago

The geometry of biased decisions! A great collaboration with @rnogueiraneuro.bsky.social , Saleh Esteki and @roozbehkiani.bsky.social

Ramon Nogueira@rnogueiraneuro.bsky.social · 9mo ago

We are very excited to announce that our new preprint with Saleh Esteki, @stefanofusi.bsky.social, and @roozbehkiani.bsky.social is now available on biorxiv! www.biorxiv.org/content/10.6.... We investigated how reward context is learned, represented, and updated to bias decisions. Thread 🧵👇! 1/13

@stefanofusi.bsky.social · 9mo ago

Explicit and implicit modularity that emerges in simple neural network models even in the absence of anatomical constraints. Whether modularity emerges or not strongly depends on the geometry of the inputs and other factors. Extensively revised article with many new results. With @wjj.bsky.social

Jeff Johnston@wjj.bsky.social · 9mo ago

When and why do modular representations emerge in neural networks? @stefanofusi.bsky.social and I posted a preprint answering this question last year, and now it has been extensively revised, refocused, and generalized. Read more here: doi.org/10.1101/2024... (1/7)

Nicholas Menghi@nichome.bsky.social · 10mo ago

Our new paper, now published in @natcomms.nature.com , asks a simple question: when two tasks share a common structure, does the brain learn them more efficiently? Surprisingly, this was not the case. Thread below (1/7) rdcu.be/eSwvU

The effects of task similarity during representation learning in brains and neural networks

Nature Communications - Here, the authors show learning tasks with similar structures can initially cause interference and slow down learning, but both the brain and artificial networks gradually...

rdcu.be

The Transmitter @thetransmitter.bsky.social · 10mo ago

“I will die on the hill that population coding is the relevant level of encoding information in the brain.” In the latest “This paper changed my life,” Nancy Padilla-Coreano discusses a paper on mixed selectivity neurons. #neuroskyence www.thetransmitter.org/this-paper-c...

This paper changed my life: Nancy Padilla-Coreano on learning the value of population coding

The 2013 Nature paper by Mattia Rigotti and his colleagues revealed how mixed selectivity neurons—cells that are not selectively tuned to a stimulus—play a key role in cognition.

thetransmitter.org

Fanny Cazettes@fannycazettes.bsky.social · 10mo ago

When @natrevneuro.nature.com asked me to highlight a paper that influenced both my field and my own research, I immediately thought of the work on mixed selectivity by @matrig.net, @stefanofusi.bsky.social, and colleagues. More on this journal club: rdcu.be/eRKLk

Mixed selectivity: when neurons stopped looking like specialists

Nature Reviews Neuroscience - In this Journal Club, Fanny Cazettes highlights a 2013 paper that demonstrated the importance of mixed selectivity for cortical computations.

rdcu.be

@stefanofusi.bsky.social · 2y ago

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.com, Shuqi Wang, Samuel Muscinelli, Liam Paninski. Many new analyses in this new version

Lorenzo Posani@lorenzoposani.com · 2y ago

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 🧱 and dimensionality 📐 of the neural code across 40+ cortical regions. doi.org/10.1101/202... 👇 1/n

@stefanofusi.bsky.social · 2y ago

The geometry of adaptation! My first excursion in the V1 territory. Great collaboration with @mariodipoppa.bsky.social @matteocarandini.bsky.social and many others

Mario Dipoppa@mariodipoppa.bsky.social · 2y ago

New results! Visual adaptation changes the geometry of V1 population activity: frequent stimuli elicit smaller responses but become more discriminable. Similar results are seen in ANNs trained with metabolic constraints, suggesting these changes emerge from efficient coding. bit.ly/3VJHXRn