1/Our paper @NeuroCellPress "Interpreting the retinal code for natural scenes" develops explainable AI (#XAI) to derive a SOTA deep network model of the retina and *understand* how this net captures natural scenes plus 8 seminal experiments over >2 decades https://t.co/4Hy1tfNsHtsciencedirect.com/science/articl…
2/#XAI will become increasingly important in #neuroscience as deep learning allows us to derive highly accurate but complex models of biological circuits.But will we just be replacing something we don't understand-the brain-with something else we don't understand-our model of it?

1/ Our new paper lead by @AllanRaventos @mansiege , @FCHEN_AI asks when in-context learning of regression can solve fundamentally *new* problems *not* seen during pre-training, and reveals it as an emergent capability arising from a phase transition... arxiv.org/abs/2306.15063 2/ between two computational phases as one increases the diversity of the pre-training tasks. At low task diversity, transformers learn in-context like a Bayesian that memorizes only tasks seen during pre-training and cannot solve new tasks....

1/ Our new preprint biorxiv.org/content/10.110… on when grid cells appear in trained path integrators w/ Sorscher @meldefon @aran_nayebi @lisa_giocomo @dyamins critically assesses claims made in a #NeurIPS2022 paper described below. Several corrections in our thread ->

https://twitter.com/RylanSchaeffer/status/1587454396257796096

2/ Our prior theory authors.elsevier.com/c/1f~Ze3BtfH1Z… quantitatively explains why few hexagonal grid cells were found in the work; many choices were made which prior theory proved don’t lead to hexagonal grids; when 2 well understood choices are made grids appear robustly ~100% of the time

1/Is scale all you need for AGI?(unlikely).But our new paper "Beyond neural scaling laws:beating power law scaling via data pruning" shows how to achieve much superior exponential decay of error with dataset size rather than slow power law neural scaling arxiv.org/abs/2206.14486 2/ In joint work @MetaAI w/Ben Sorscher, Robert Geirhos, Shashank Shekhar & @arimorcos we show both in theory (via statistical mechanics) and practice how to achieve exponential scaling by only learning on selected data subsets of difficult nonredundant examples(defined properly)

1/ Our new work: "How many degrees of freedom do we need to train deep networks: a loss landscape perspective." arxiv.org/abs/2107.05802 We present a geometric theory that connects to lottery tickets and a new method: lottery subspaces. w/ @_BrettLarsen @caenopy @stanislavfort 2/ Many methods can train to low loss using very few degrees of freedom (DoF). But why? We show that to train to a small loss L using a small number of random DoF, the number of DoF + the Gaussian width of the loss sublevel set projected onto a sphere around initialization...

1/ Super excited to share our work with @drfeifei and @silviocinguetta, lead by the mastermind @agrimgupta92 on Deep Evolutionary Reinforcement Learning (DERL): arxiv.org/abs/2102.02202 which leverages large scale simulations of evolution and learning to...

https://twitter.com/agrimgupta92/status/1357147957502513153

2/ generate diverse morphologies with embodied intelligence that can exploit the passive physical dynamics of agent environment interactions to rapidly learn complex tasks in an energy efficient manner

1/ New paper in @Nature : “Fundamental bounds on the fidelity of sensory cortical coding” with amazing colleagues: Oleg Rumyantsev, Jérôme Lecoq, Oscar Hernandez, Yanping Zhang, Joan Savall, Radosław Chrapkiewicz, Jane Li, Hongkui Zheng, Mark Schnitzer: nature.com/articles/s4158… 2/ See also here for a free version: rdcu.be/b26wp and tweeprint below ->

1/ New in @sciencemagazine w/ @KarlDeisseroth lab: science.sciencemag.org/content/early/…: new opsin + multi-photon holography to image ~4000 cells in 3D volumes over 5 cortical layers while also stimulating ~50 neurons to directly drive visual percepts; data analysis and theory reveal… 2/ that visual cortex operates in a highly sensitive critically excitable regime in which stimulating a tiny subset of ~20 cells with similar orientation tuning is sufficient to both selectively recruit a large fraction of similarly responding cells and drive a specific percept

1/ Our new #neuroscience paper, "Emergent elasticity in the neural code for space" just appeared in @PNASNews: pnas.org/content/early/… Awesome work lead by @SamOcko, with @kiahhardcastle and @lisa_giocomo . Take home messages... 2/ We ask: how do we learn where we are? two info sources are needed: 1) our recent history of velocity; 2) what landmarks we have encountered. How can neurons/synapses fuse these two sources to build a consistent spatial map as we explore a new place we have never seen before?

1/ New #deeplearning paper at the intersection of #AI #mathematics #psychology and #neuroscience: A mathematical theory of semantic development in deep neural networks: arxiv.org/abs/1810.10531 Thanks to awesome collaborators Andrew Saxe and Jay McClelland! 2/ We study how many phenomena in human semantic cognition arise in deep neural networks, and how these phenomena can be understood analytically in a simple deep linear network. Such phenomena include…