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
The geometry of biased decisions! A great collaboration with @rnogueiraneuro.bsky.social , Saleh Esteki and @roozbehkiani.bsky.social
“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...
Great collaboration with the Doeller lab, and hopefully the first of a long series
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.
The deadline for applying to the Methods in Computational Neuroscience summer course at MBL in Woods Hole is approaching (March 16)! An exciting course with an amazing lineup of lecturers in a beautiful location www.mbl.edu/education/ad...
A fantastic new laboratory at UCSF! Strongly recommended!
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 Transmitter
Huge thanks to the anonymous reviewers for their feedback and to all collaborators. @mpicbs.bsky.social, @doellerlab.bsky.social, @stefanofusi.bsky.social, @wjj.bsky.social, @vigano.bsky.social, @burkhardmaess.bsky.social and Max Hinrichs.
