RT @Meaningness
https://metarationality.com/rationality
https://metarationality.com/rationality
JohnMark Taylor
- 34 Followers
- 117 Following
- 81 Posts
Came for the beautiful visuals, stayed for the cheeky name
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RT @abhi_aggarwal1
iGluSnFR3 is published! Check it out⬇️
https://www.nature.com/articles/s41592-023-01863-6
We made a new glutamate indicator with improved sensitivity, kinetics & localization for imaging synaptic transmission.
This is #iGluSnFR3 in action detecting minis w/ TTX.
@PodgorskiLab @HHMIJanelia @AllenInstitute
https://twitter.com/abhi_aggarwal1/status/1654141541215084545
Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission - Nature Methods
iGluSnFR variants with improved signal-to-noise ratios and targeting to postsynaptic sites have been developed, enabling the analysis of glutamatergic neurotransmission in vivo as illustrated in the mouse visual and somatosensory cortex.
Dendritic dynamics as fireworks show 🎆🤯
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RT @abhi_aggarwal1
iGluSnFR3 is published! Check it out⬇️
https://www.nature.com/articles/s41592-023-01863-6
We made a new glutamate indicator with improved sensitivity, kinetics & localization for imaging synaptic transmission.
This is #iGluSnFR3 in action detecting minis w/ TTX.
@PodgorskiLab @HHMIJanelia @AllenInstitute
https://twitter.com/abhi_aggarwal1/status/1654141541215084545
Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission - Nature Methods
iGluSnFR variants with improved signal-to-noise ratios and targeting to postsynaptic sites have been developed, enabling the analysis of glutamatergic neurotransmission in vivo as illustrated in the mouse visual and somatosensory cortex.
RT @thejazzestate
Duke Ellington on dreaming.
Duke Ellington on dreaming.
Do you consciously perceive a half moon as a 2D semicircle, as a 3D sphere (partially lit), or can you flip your perception back and forth between 2D and 3D?
Do you perceive a half moon as a 2D semicircle, as a 3D sphere, or can you flip your conscious perception back and forth between the two?
RT @MIT
We did not subscribe to Twitter Blue.
We did not subscribe to Twitter Blue.
RT @jxbz
We are thrilled to announce "automatic gradient descent"---a neural network optimiser without hyperparameters. AGD trains out-of-the-box and at ImageNet scale.
paper: https://arxiv.org/abs/2304.05187
PyTorch: https://github.com/jxbz/agd
1/5
Automatic Gradient Descent: Deep Learning without Hyperparameters
The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation frameworks neglect this information in favour of implicit architectural information (e.g. second-order methods) or architecture-agnostic distance functions (e.g. mirror descent). Meanwhile, the most popular optimiser in practice, Adam, is based on heuristics. This paper builds a new framework for deriving optimisation algorithms that explicitly leverage neural architecture. The theory extends mirror descent to non-convex composite objective functions: the idea is to transform a Bregman divergence to account for the non-linear structure of neural architecture. Working through the details for deep fully-connected networks yields automatic gradient descent: a first-order optimiser without any hyperparameters. Automatic gradient descent trains both fully-connected and convolutional networks out-of-the-box and at ImageNet scale. A PyTorch implementation is available at https://github.com/jxbz/agd and also in Appendix B. Overall, the paper supplies a rigorous theoretical foundation for a next-generation of architecture-dependent optimisers that work automatically and without hyperparameters.