Great blog post from @ItsNeuronal on the interpretation of Log-Likelihood Ratios for model comparison, including a (new to me!) result on how to obtain a significance threshold for declaring one model better than another:
https://t.co/lMCHaYXA5x
𝗦𝗽𝗮𝘁𝗶𝗮𝗹𝗹𝘆 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗼𝘂𝘀𝗲 𝗯𝗿𝗮𝗶𝗻
Leaving aside the more extreme discussions about "everything everywhere" and modularity this is a very cool paper. Data from 260 regions and 60K neurons.
https://t.co/uVZ77bpLIE
I did a podcast with Jon Stewart who has always been a hero of mine. It was a lot of fun. He really wanted to understand how AI works.
https://t.co/frLBndsW7g
A pair of papers on using holographic optogenetics and compressed sensing for connectomics
Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing
https://t.co/amA4i1BoGV
🔦 New @TrendsNeuro spotlight on our recent @NatureComms paper!
How do brains generalise spatial information across sensory modalities?
Our study identifies a region in the dorsal cortex of mice enabling them to transfer spatial knowledge between vision and touch!🖐🏻👀 🔗https://t.co/sz1meIv0It
My postdoc work "Unsupervised pretraining in biological neural networks" is out now, along with more than 400GB (milliions neurons) neural data. https://t.co/2uI5YAQ0vg. Figshare and Github links can be found in the paper.
Video prediction foundation models implicitly learn how objects move in videos. Can we learn how to extract these representations to accurately track objects in videos _without_ any supervision? Yes! 🧵
Work done with: @Rahul_Venkatesh, @SeKim1112, @jiajunwu_cs and @dyamins
We just finished up Winter quarter CS375: Large-Scale Neural Network Models for Neuroscience. Check out the publicly available Syllabus and lecture notes https://t.co/QUzfYRde8o
🚨Fully funded PhD opportunity at QMUL, London!🚨
Join us to explore optogenetic control of visual perception. Open to UK nationals & UK-ILR residents.
Please share! 👇
🔗 https://t.co/S72F0BHOCW
New study from our lab, in collab with @erlichlab
We developed novel frameworks to study multi-agent decision-making in mice.
Mice flexibly shift their value preference under social competition, by integrating real-time self and opponent information!
https://t.co/MFQNJ5IRnY
We always see that: 1) neural responses are very diverse 2) the shattering dimensionality is as high as it can be. Now also in an extensive analysis of the IBL dataset. Wonderful collaboration with @LorenzoPosani , Shuqi Wang, Samuel Muscinelli, Liam Paninski. Many new analyses
Temporal dynamics of energy-efficient coding in mouse primary visual cortex https://t.co/RFdEsdo3nH An efficient paper with few words but a lot of information. 😀
25 yrs ago, perception dominated neuroscience, and Raj's @RajeshPNRao predictive coding theory.
Now, it's all about action-perception loops across the hierarchy... "active predictive coding."
Also, augmenting our cognition with AI neural co-processors!
https://t.co/kpgZjfPwW3