Postdoc in the Center for Theoretical Neuroscience at Columbia, previously at the University of Chicago
he/they
wj2.github.io
Jeff Johnston
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We also now include a generalization of our approach to a large set of novel task types, and show that our main result generalizes as well! (5/7)
We now spend more time introducing our framework, as well as the main benefit of modularity: It provides a factorized solution to complex tasks. (3/7)
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)
We’ve also rewritten large parts of the manuscript for clarity, as well as further developed some of our experimental predictions. I think the paper is much improved and I encourage you to check it out even if you read the original! Here’s the link again: doi.org/10.1101/2024... (6/7)
The core result is still the same: Whether or not modularity emerges depends on both the format – or, representational geometry – of the input to the network and the structure of the task it is trained to perform. (2/7)
By the way, if you’re interested in working together on problems like this, I’m starting my lab at UCSF this summer. Get in touch if you’re interested in doing a postdoc! More info here: wj2.github.io/postdoc_ad (7/7)
Though, as before, this benefit can emerge with specialized sub-populations of units (i.e., explicit modularity) or specialized subspaces at the population level (i.e., implicit modularity). We characterize the emergence of both forms of modularity! (4/7)
Experimental and theoretical work has argued both for and against the existence of specialized sub-populations of neurons (modules) within single brain regions. By studying artificial neural networks,...
