Welcome to UCLA Marcus! π We are thrilled Marcus is joining our neuroscience and computational community. I encourage students and postdocs to check out the exciting opportunities in his lab.
Posts by Mario Dipoppa
We are recruiting PhD students and postdocs at UIUC ECE interested in learning dynamics, spiking networks, and dynamical systems. Details:
rainerengelken.github.io/join/
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 π§΅π! 1/13
Congratulations Franck! π π
Great opportunity in NeuroAI! π
Dario Ringach gave a really interesting talk at the National University of Singapore today about results with Elaine Tring and @mariodipoppa.bsky.social adaptation of population responses in mouse visual cortex. Remarkably data was well described by log(r(x)/r(y) ~ log(p(y)/p(x) π
Congratulations, Matteo! π
Our paper on the statistical mechanics of transfer learning is now published in PRL. Franz-Parisi meets Kernel Renormalization in this nice collaboration with friends in Bologna (F. Gerace) and Parma (P. Rodondo, R. Pacelli).
journals.aps.org/prl/abstract...
Very happy to see our work finally in print!
www.pnas.org/doi/10.1073/...
TLDR: Tilt illusion is not a bug, but a feature of a well-designed visual system that maximizes information capacity adaptively based on spatial context. (1/6)
Congratulations and thank you for the mention! We really enjoyed your paper!
The Grossman Center at UChicago is hiring Center Postdocs! Great scientific environment in a great city. Competitive salaries and freedom to work with any of the center PIs. The deadline is May 1st. DM me if you have any questions. neuroscience.uchicago.edu/grossmancent...
In previous work with Dario Ringach (Tring et al. 2023), we discovered a universal power law of visual adaptation. With @matteomariani.bsky.social, we now show with a computational model that this power law can be explained by efficient coding! We will present this at #Cosyne2025, Thu. poster 1-031.
Iβve been overwhelmed trying to keep up with everything thatβs happening at the NIH. I wondered if others were likewise overwhelmed so I am going to start regularly posting videos with what I know. Pls feel free to leave suggestions and comments. youtu.be/MvgNHWSJtH0
Picture of neural manifolds for the two choices in a decision-making task, depicted in 3D and in 2D
New preprint: "The geometry of the neural state space of decisions", work by Mauro Monsalve-Mercado, buff.ly/42wVHD5. Surprising results & predictions! (Thread) We analyze neuropixel population recordings in macaque area LIP during a reaction time, random-dot motion 1/
Please retweet! Open rank faculty search in the basic sciences at UCLA David School of Medicine. Multiple positions are available - the application deadline is 19th January. Please apply here:
recruit.apo.ucla.edu/JPF10062
New job alert!
My lab (saleemlab.com) has a new postdoc / senior postdoc opening. They will be part of an exciting research supported by ERC & UKRI, studying vision during navigation. Projects range from purely computational to performing new physiological recordings.
Our work on how visual adaptation changes the geometry of neural representations in V1 is now on bioRxiv:
Hello world, my first post in Bluesky! Adaptation to a frequent stimulus reduces neuronal activity but increases discriminability in V1. These two effects can be observed as well in the geometry of representations and they are reproduced in an ANN with metabolic constraints.
The geometry of adaptation! My first excursion in the V1 territory. Great collaboration with @mariodipoppa.bsky.social @matteocarandini.bsky.social and many others
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
Iβve just joined and looking forward to connect with others from computational and systems neuroscience community and beyond!