Q&A with Skild AI CEO Deepak Pathak on building a general-purpose brain for robots, standing out among big tech's robotics efforts, the path to AGI, and more (Alex Heath/Sources)
https://sources.news/p/skild-ai-ceo-robotics-brain-davos
A new study suggests that #dementia may be driven in part by faulty blood flow in the brain.
Researchers found that losing a key lipid causes blood vessels to become overactive,
disrupting circulation and starving brain tissue.
When the missing molecule was restored, normal blood flow returned.
This discovery opens the door to new treatments aimed at fixing vascular problems in …
cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted
How to improve memory, mitigate dementia & reverse brain aging:
1️⃣ Take a multivitamin daily.
2️⃣ Increase choline intake. (Eggs)
3️⃣ Eat more blueberries. (1 cup/daily)
4️⃣ Aerobic exercise (60% of max heart rate)
▶️ The Fastest Way to Reverse Brain Aging (Science-backed)
https://
budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 63448 edges.
Tags: Biolo…
An #earworm popping up in my brain lately. A poem I was made to memorize when I was 14. It re-emerges every couple years, for no reason I can figure. tbh, I usually only remember verbatim the first few lines or so, and have to look it up again. It’s one of those things, like advertising jingles from many years ago, that have lodged in my memory. 🎶You’ll wonder where the yellow went when you brush y…
RE: https://mastodon.xyz/@xkcd/116134642695834637
why does the compsci have a woolly hat? don’t they need a brain heatsink?
when i go out cycling in wintry subzero weather, i wear a fleecy band that stops my ears from getting frostbite, but still allows my…
RE: https://mastodon.social/@arstechnica/116132930980342790
It’s gonna be so funny when it turns out that Alzheimer’s is a post-viral syndrome of Varicella (brain Shingles!) and we turn out to have cured it inadvertently in recent decades by developing…
I like where this is going.
I think #ai has analyzed my brain 🧠
🧐🤣
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
"We're just going to run a physical simulation of a human brain to achieve AGI"
"Won't the brain die instantly if it's without a body and oxygen supply etc?"
"Well, we'll just also simulate a body."
"Won't the body die instantly if it's in a vacuum?
"Fine, we'll just simulate an atmosphere too."
"Won't the body die if it's without food and light and gravity and stimulation?"
"Fine, we'll just simulate all the physical processes on the Earth."
"Won't the Earth just freeze instantly without the Sun being there?"
"Fine, we'll just simulate the sun, too."
"Will the solar system work properly if there's only the sun? What about gravitational influences of other mass in the galaxy, what about cosmic rays?"
"Fine, we'll just simulate the whole universe, too."
What I mean by that is I doubt there is even one single truly “neurotypical” person on this Earth whose brain is actually average in every important dimension of brain variation.
2/
Brain implants: What's standing in the way of pivotal trials, FDA approval #BCI
Source: Sam Altman's Merge Labs, which seeks to read brain activity using ultrasound, is being spun out of LA-based nonprofit Forest Neurotech (Emily Mullin/Wired)
https://www.wired.com/story/sam-altman-brain-computer-interf…
“Brain Drain” or Temporary Wartime Realities? How the Number and Quality of Applicants to Ukrainian Higher Education Institutions Have Changed Since the Start of the Full-Scale Invasion: https://benborges.xyz/2026/02/19/brain-drain-or-temporary-wartime.html
I just scrolled through #LinkedIn and read my feed for the first time in 2 months. *shudders*
Enough internet for the day, I need a book and some tea to earn my brain cells back.
(In all seriousness I was shocked by just how homogenous everyone’s writing style is: every post had the same rhythm and the same uncanny, unnatural cadence. That’s not how anyone speaks! Or writes! Every sentence just reads like the author is blowing smoke up their own ass)
(Also - I didn’t see a single new idea that I hadn’t seen a year ago when I used to actually scroll through LinkedIn every day)
If you’ve never been into exercise or considered yourself “a gym person,” the idea of starting exercise in midlife can feel daunting, or even futile.
But here’s the good news:
from a scientific standpoint, your 40s may be one of the most powerful times to get started.
When it comes to bone, muscle, metabolism, heart health and even brain function, it is never too late to start exercising.
In fact, your body is remarkably responsive to change at this stage of life, and y…
A scientifically important fMRI study of a deceased salmon won an IgNobel Prize a while back. It’s still important.
Bennett et al. "Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction" Journal of Serendipitous and Unexpected Results, 2010
The image just popped into my head of a PCBA manager in a Smokey Bear hat taking his blue-coated SMT technicians out for a morning run around the factory grounds singing cadence at them
I don't know but it's been said
Vintage solder's made of lead
Some say RoHS is a pain
But I kinda like my brain
Aoyue irons make me sad
But my Metcal ain't half bad
Four fast, weird songs on this EP from 2025 via Buffalo's synth punk freaks HAVANA SYNDROME. Love the guy's voice, he sounds totally unhinged. I love singers that sound unhinged. 😂
https://swimmingfaithrecords.bandcamp.com/album/kill-your-brain
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Kunyu Zhang, Yanwu Yang, Jing Zhang, Xiangjie Shi, Shujian Yu
https://arxiv.org/abs/2602.21046 https://arxiv.org/pdf/2602.21046 https://arxiv.org/html/2602.21046
arXiv:2602.21046v1 Announce Type: new
Abstract: Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle with noisy interactions, and conventional post-hoc attribution methods may lack reliability, potentially highlighting dataset-specific artifacts. To address these challenges, we introduce PIME, an interpretable framework that bridges intrinsic interpretability with minimal-sufficient subgraph optimization by integrating prototype-based classification and consistency training with structural perturbations during learning. This encourages a structured latent space and enables Monte Carlo Tree Search (MCTS) under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training. Experiments on three benchmark fMRI datasets demonstrate that PIME achieves state-of-the-art performance. Furthermore, by constraining the search space via learned prototypes, PIME identifies critical brain regions that are consistent with established neuroimaging findings. Stability analysis shows 90% reproducibility and consistent explanations across atlases.
toXiv_bot_toot
South Carolina doesn’t require hospitals to report when they admit patients with measles-related illnesses.
Available data shows that only 2% of the state’s measles cases have resulted in hospitalizations. Some infectious disease experts fear significant underreporting.
Some doctors say they lack information about the severity of measles complications as it spreads around them.
New Virus
There is a new virus going around called WORK. If you receive any sort
of WORK, whether via e-mail, Internet, or simply handed to you by a
colleague, do not open it. Those who have opened WORK have found that
their social life is deleted and their brain ceases to function
properly.
If you do encounter WORK via e-mail or are faced with any WORK at all,
purge the virus by sending an e-mail to your boss with the words 'This
is too much for…
In der Juristerei wird das Aufkommen der LLMs begeistert gefeiert. Man versucht sich zu profilieren. Man ist vielleicht besorgt, dass die Stundensätze herunter gehen könnten. Aber sonst? Und dann diese Studie, die zeigt, dass bei Benutzung von LLMs die cognitive Kapazität und damit auch die Qualität dauernd nach unten zeigt. Kurz: Ein LLM-Anwalt bietet teure 0815-Soße, die man auch ohne Anwalt haben kann.
fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata
I want to emphasize: the graphic details of what ICE did this morning are •extremely• disturbing. It is important to know what is happening, but that does not necessarily mean having every traumatic detail burned into your brain. Please take care of yourself, and be mindful of how you absorb details as they emerge.
Boston-based Neurable, which builds an OS for brain-computer interfaces, raised a $35M Series A led by Spectrum Moonshot Fund, bringing its total raised to $65M (Brock E.W. Turner/Axios)
https://www.axios.com/pro/health-tech-deals/2025/12/19/neurable-r…
Unhinged doesn't even cut it anymore... This speech & rhetoric needs a superlative to describe it... sheer brain rot! And yet, instead of a group walk out, a standing ovation at the end... unbelievable!
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Did you know that I have a brain?
Yes.
No.
Pic or didn't happen.
My brain looked at this log and merged the last two plugin names into "yiffing".
I've clearly spent too much time on the internet.
ChatGPT about a Neuralink Blindsight brain implant for people born blind: "For early-blind people, a Neuralink-style cortical visual prosthesis is very unlikely to be meaningfully available in the 2020s or 2030s." https://chatgpt.com/share/694e5e8a-ee78-8004-91fc-9ed48e261…
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Functional Continuous Decomposition
Teymur Aghayev
https://arxiv.org/abs/2602.20857 https://mastoxiv.page/@arXiv_eessSP_bot/116130499236089653
- SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models
Xie, Zhang, Shan, Zhu, Tang, Wei, Song, Wan, Song
https://arxiv.org/abs/2602.20901 https://mastoxiv.page/@arXiv_csCV_bot/116130845273808954
- Some Simple Economics of AGI
Christian Catalini, Xiang Hui, Jane Wu
https://arxiv.org/abs/2602.20946 https://mastoxiv.page/@arXiv_econGN_bot/116130470423837005
- Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
Yubin Ge, Yongsong Huang, Xiaofeng Liu
https://arxiv.org/abs/2602.20994 https://mastoxiv.page/@arXiv_eessIV_bot/116130212832138624
- MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Tianhao Fu, Yucheng Chen
https://arxiv.org/abs/2602.21033 https://mastoxiv.page/@arXiv_csCV_bot/116130864279556063
- Empirically Calibrated Conditional Independence Tests
Milleno Pan, Antoine de Mathelin, Wesley Tansey
https://arxiv.org/abs/2602.21036 https://mastoxiv.page/@arXiv_statME_bot/116130690605113562
- Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
https://arxiv.org/abs/2602.21039 https://mastoxiv.page/@arXiv_statML_bot/116130572661848449
- Position-Aware Sequential Attention for Accurate Next Item Recommendations
Timur Nabiev, Evgeny Frolov
https://arxiv.org/abs/2602.21052 https://mastoxiv.page/@arXiv_csIR_bot/116130263323086316
- Motivation is Something You Need
Mehdi Acheli, Walid Gaaloul
https://arxiv.org/abs/2602.21064 https://mastoxiv.page/@arXiv_csAI_bot/116130680774678580
- An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Impr...
Natalia da Silva, Dianne Cook, Eun-Kyung Lee
https://arxiv.org/abs/2602.21130 https://mastoxiv.page/@arXiv_statML_bot/116130610674573081
- Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank
Kimon Fountoulakis, David Mart\'inez-Rubio
https://arxiv.org/abs/2602.21138 https://mastoxiv.page/@arXiv_mathOC_bot/116130547076073836
- LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
Jiang, Yang, Nath, Parida, Kulkarni, Xu, Xu, Anwar, Roth, Linguraru
https://arxiv.org/abs/2602.21142 https://mastoxiv.page/@arXiv_csCV_bot/116130871488694585
- A Benchmark for Deep Information Synthesis
Debjit Paul, et al.
https://arxiv.org/abs/2602.21143 https://mastoxiv.page/@arXiv_csAI_bot/116130692571594706
- Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
Anurag Dutt, Nimit Shah, Hazem Masarani, Anshul Gandhi
https://arxiv.org/abs/2602.21144 https://mastoxiv.page/@arXiv_csDC_bot/116130520888343997
- Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Mame Diarra Toure, David A. Stephens
https://arxiv.org/abs/2602.21160 https://mastoxiv.page/@arXiv_statML_bot/116130618512594211
- Aletheia tackles FirstProof autonomously
Tony Feng, et al.
https://arxiv.org/abs/2602.21201 https://mastoxiv.page/@arXiv_csAI_bot/116130705679345625
- Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Abdulaziz Almuzairee, Henrik I. Christensen
https://arxiv.org/abs/2602.21203 https://mastoxiv.page/@arXiv_csRO_bot/116130765974498223
toXiv_bot_toot
“How can one mutation cause such different effects?”
Pera explained. “It comes down to genetic background.
Each strain has a unique genetic makeup that can either protect against or magnify the impact of that mutation.”
To confirm these findings in living organisms, Pera introduced the same mutations into live mice from the same eight strains.
Remarkably, the neurons in the brains of these mice phenotypically matched what he had seen in the petri dish,
providin…
Thus far about the only known advantage of a visual cortical prosthesis (brain implant for restoring vision) over visual-to-auditory sensory substitution is that you get to see blobs of light (phosphenes) if late-blind https://www.artificialvision.com/neuralink.htm
Sources: Dell, Lenovo, and other PC makers are working with Nvidia on laptops using the Arm-based Nvidia-MediaTek system-on-a-chip, which could come in H1 2026 (Yang Jie/Wall Street Journal)
https://www.wsj.com/tech/nvidia-wants-to-be-the-brain-of-co…
I once again have the feeling that Randall Monroe can see directly into my brain (especially the mouseover text):
https://xkcd.com/3210/
What does Neuralink want — to help people with paralysis, or prepare for a war with AI? https://archive.ph/c96dg Conflicting rhetoric from Elon Musk's company impedes brain implants' ability to gain traction as medical devices, competitors say";
Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
Chuqin Geng, Li Zhang, Mark Zhang, Haolin Ye, Ziyu Zhao, Xujie Si
https://arxiv.org/abs/2602.16954 https://mastoxiv.page/@arXiv_csLG_bot/116102434757760085
- Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Fres...
Ziliang Zhao, et al.
https://arxiv.org/abs/2602.17050 https://mastoxiv.page/@arXiv_csLG_bot/116102517335590034
- MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sam...
Fu, Lin, Fang, Zheng, Hu, Shao, Qin, Pan, Zeng, Cai
https://arxiv.org/abs/2602.17550 https://mastoxiv.page/@arXiv_csLG_bot/116102581561441103
- A Theoretical Framework for Modular Learning of Robust Generative Models
Corinna Cortes, Mehryar Mohri, Yutao Zhong
https://arxiv.org/abs/2602.17554 https://mastoxiv.page/@arXiv_csLG_bot/116102582216715527
- Multi-Round Human-AI Collaboration with User-Specified Requirements
Sima Noorani, Shayan Kiyani, Hamed Hassani, George Pappas
https://arxiv.org/abs/2602.17646 https://mastoxiv.page/@arXiv_csLG_bot/116102592047544971
- NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting i...
Rampunit Kumar, Aditya Maheshwari
https://arxiv.org/abs/2602.19654 https://mastoxiv.page/@arXiv_csLG_bot/116125610403473755
- Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task
Mina Ghashami, Soumya Smruti Mishra
https://arxiv.org/abs/2405.10385 https://mastoxiv.page/@arXiv_csCL_bot/112472190479013167
- Watermarking Language Models with Error Correcting Codes
Patrick Chao, Yan Sun, Edgar Dobriban, Hamed Hassani
https://arxiv.org/abs/2406.10281 https://mastoxiv.page/@arXiv_csCR_bot/112636307340218522
- Learning to Control Unknown Strongly Monotone Games
Siddharth Chandak, Ilai Bistritz, Nicholas Bambos
https://arxiv.org/abs/2407.00575 https://mastoxiv.page/@arXiv_csMA_bot/112715733875586837
- Classification and reconstruction for single-pixel imaging with classical and quantum neural netw...
Sofya Manko, Dmitry Frolovtsev
https://arxiv.org/abs/2407.12506 https://mastoxiv.page/@arXiv_quantph_bot/112806295477530195
- Statistical Inference for Temporal Difference Learning with Linear Function Approximation
Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo
https://arxiv.org/abs/2410.16106 https://mastoxiv.page/@arXiv_statML_bot/113350611306532443
- Big data approach to Kazhdan-Lusztig polynomials
Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz
https://arxiv.org/abs/2412.01283 https://mastoxiv.page/@arXiv_mathRT_bot/113587812663608119
- MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition
Mehran Shabanpour, Kasra Rad, Sadaf Khademi, Arash Mohammadi
https://arxiv.org/abs/2502.17457 https://mastoxiv.page/@arXiv_eessSP_bot/114069047434302054
- Tightening Optimality gap with confidence through conformal prediction
Miao Li, Michael Klamkin, Russell Bent, Pascal Van Hentenryck
https://arxiv.org/abs/2503.04071 https://mastoxiv.page/@arXiv_statML_bot/114120074927291283
- SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
Juhyeon Park, Peter Yongho Kim, Jiook Cha, Shinjae Yoo, Taesup Moon
https://arxiv.org/abs/2503.06437 https://mastoxiv.page/@arXiv_csCV_bot/114142690988862508
- How much does context affect the accuracy of AI health advice?
Prashant Garg, Thiemo Fetzer
https://arxiv.org/abs/2504.18310 https://mastoxiv.page/@arXiv_econGN_bot/114414380916957986
- Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
Daniel J. Strick, Carlos Garcia, Anthony Huang, Thomas Gardos
https://arxiv.org/abs/2505.06646 https://mastoxiv.page/@arXiv_eessIV_bot/114499319986528625
- Sharp Gaussian approximations for Decentralized Federated Learning
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
https://arxiv.org/abs/2505.08125 https://mastoxiv.page/@arXiv_statML_bot/114505047719395949
- HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning
Chuhao Zhou, Jianfei Yang
https://arxiv.org/abs/2505.17645 https://mastoxiv.page/@arXiv_csCV_bot/114572928659057348
- A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine ...
Agnideep Aich, Md Monzur Murshed, Sameera Hewage, Amanda Mayeaux
https://arxiv.org/abs/2505.22554 https://mastoxiv.page/@arXiv_statML_bot/114589983451462525
- Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian F\"urrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2506.01666 https://mastoxiv.page/@arXiv_quantph_bot/114618420761346125
toXiv_bot_toot
budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 121755 edges.
Tags: Biol…
The unusual electronic and optical properties of #perovskites have long been touted as useful for improving solar cells and television screens,
but these materials have never quite hit the big time.
Existing approaches have hoovered up all the investment and attention, and perovskites remain confined to specialist applications.
Now researchers at Empa, ETH Zurich and the Politecnico…
Daniels was looking at just 10 easily quantifiable body measurements. How many important dimensions of variations are there in a human mind? How hard are they to measure? How likely is it that even one single “average” mind exists on Earth?? The odds are vanishingly small.
[Napkin sketch: assume there are a paltry 20 dimensions of brain variation. (Surely that’s low.) Assume there’s a 1 in 5 change of being completely “normal” in each. (Surely that’s high.) Even that absurd hypothetical gives a 1 in 11,490 chance that a •single• completely average mind exists in a population of 8.3 billion.]
5/
budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 121755 edges.
Tags: Biol…
Donald Trump sounded like a fascist dictator
suffering from a brain bleed
during his speech yesterday
at Davos.
It was a national embarrassment
even by the lowly standards of modern American politics.
The president’s showing in Switzerland was so shabby that I feel compelled to share some thoughts about it.
First of all, despite what cable news chyrons would have you believe,
Trump’s screed was chillingly aggressive.
He began the Gree…
Fortunately my brain is quite slow, so as long as I'm healthy I won't need an invasive BCI (brain implant) to output faster than I can type or talk (with or without AI), and I'll use a headset or smartglasses for AR/XR.
cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
Doctors made a custom CRISPR therapy to treat an infant with a rare genetic disease
A baby, named KJ, was born with a deficiency in carbamoyl phosphate synthetase 1 (CPS1).
This deficiency means his liver couldn’t convert ammonia
– which is made naturally when the body breaks down proteins
– into urea, damaging his brain and liver.
Babies born with CPS1 deficiency often end up staying in the hospital until they’re eligible for a liver transplant.
They h…
Postdoc position in brain plasticity (blindness/deafness) at Georgetown University, Washington DC #neuroscience
fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata
"A high acuity is trivially obtained at the expense of field of view through suitable enlarging optics, so whenever a high acuity is claimed for a visual prosthesis one must also check if the corresponding field of view is better than tunnel vision." https://www.artificialvision.com/neuralink
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 71604 edges.
Tags: Biolo…
fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata
fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…
fly_larva — Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`, `'ad'`, `'da'`, `'dd'`), and synapse count.
The market opportunities for invasive visual prostheses such as the much hyped Neuralink Blindsight brain implant are slowly eroding, in favor of smart glasses with AI scene description, talking OCR and visual-to-auditory sensory substitution.
Toward real-time fMRI? Exploring the sensitivity limits of neuronal current imaging with MRI and MEG in the human brain https://www.biorxiv.org/content/10.64898/2026.02.17.706369v1 Local magnetic field amplitudes of ∼0.07 nT still below detection threshold of 0.2 nT at 3T; …
Dynamic reversal of IT-PFC information flow orchestrates visual categorization under perceptual uncertainty https://www.biorxiv.org/content/10.64898/2025.12.17.695044v1 Quite a mouthful to say that "the brain actually reverses its information flow when things get blurr…
(YouTube) China's first brain-computer cluster in Shanghai sees vision implants and ultrasound brain therapy https://www.youtube.com/watch?v=QGcXdkqNbek "Mindtrix BCI founder Liu Bing is developing an implant and smart glasses that could help blind patients “see”. Clinical trials may start…
Perhaps the good thing about The vOICe vision BCI is that it is not easy but hard, preventing AI brain rot #AI