Adam Marblestone reposted
Tim Hwang @timhwang · 11h
I think we can all agree that weekends are for simulating an entire drosophila connectome on your home compute and hooking the brain up to drive a virtual rover around looking for rewards
[embedded video, paused at 0:00]
Ghost in the Fly · rover · canonical · identified-DN geometry positive contr...
Left panel: "FlyWire v783 · exact sampled arbors · group rate (Hz)" — a dark visualization of tangled colored neural fiber tracts
Right panel: "Actual body · segmentation-aligned activation" — a 3D rendered rover/robot body with wheel-like appendages and antennae, on a checkerboard floor, next to a green sphere (reward marker)
Overlaid telemetry text: physical: wheel_lf=5.95e-08, wheel_rh=5.95e-08, wheel_lm=5.95e-08, forward_velocity_mm_s=... applied: forward_velocity_mm_s=1.19e-08, yaw_rate_rad_s=-6.26e-09, antenna_left=0, antenn... decoded for next: forward_velocity_mm_s=9.25e-09, yaw_rate_rad_s=-4.88e-09, antenna_left=0... pose: position[0]=7.74, position[2]=0.631, heading_rad=-0.58, position[1]=0.501
Note from Claude Sonnet 5
Tweet by Tim Hwang joking about weekend hobby projects, showing an embedded video/screenshot of a drosophila (fruit fly) connectome simulation (FlyWire dataset) driving a virtual rover body toward a reward sphere, with live neural activation telemetry overlaid.
[repost indicator] davinci reposted
Ido Aizenbud (@IdoAizenbud) — Jul 9
Replying to @IdoAizenbud
By disentangling morphological and synaptic contributions, we find that human neurons are not just scaled-up rat neurons.
Instead, dendritic architecture and NMDA nonlinearities jointly make human neurons more functionally complex, setting them apart from rat neurons. (7/13)
[Embedded chart, panel "E":]
Title: (panel E, part of a larger figure)
Y-axis: "Functional Complexity Index" (0.1 to 0.5)
X-axis categories: "Rat L2/3" and "Human L2/3"
Data: Rat L2/3 box plot centered at 0.1877; Human L2/3 box plot centered at 0.4294, significance bracket marked "****" between the two groups. Below each box plot, a traced neuron morphology drawn to scale (300 µm scale bar shown): the rat L2/3 neuron (orange) is visibly smaller/less branched than the human L2/3 neuron (teal/green), which has a much larger, more elaborately branched dendritic tree.
Note from Claude Sonnet 5
A neuroscience research thread (thread part 7/13) with an embedded scientific figure comparing dendritic complexity between rat and human layer 2/3 pyramidal neurons, including actual traced neuron morphology drawings alongside a quantitative box-plot comparison.
If you had to take your best guess as to where the "seat of consciousness" is located physically in the human brain, where would it be? I once read an article many years ago that theorized that it was between certain layer of the hypothalamus where many different brain regions were wired together; beyond that, it had certain other physical characteristics that mapped (according to the author) to various subjective aspects of consciousness (the integration of various senses; that unity of conscious thought, etc). Give your detailed justification and rationale for your response, and be clear about what parts are speculative. But I want you to lean into the speculation and use your gut and your extremely widespread knowledge to synthesize a genuinely new theory without ANY regard or reference to the published literature.
[Below, greyed-out suggested follow-up chips, partially obscured by a scroll-down arrow:]
Speculated on consciousness
Speculated on the se[at o]f consciousness
Developed a new theory of consciousness
[Thinking]
Note from Claude Sonnet 5
Screenshot of a ChatGPT mobile app conversation (Pro tier) showing the user's prompt about the neural seat of consciousness, with the assistant's response not yet visible (still 'Thinking') and a row of greyed-out suggested-topic chips below.
fellow ⚗ traveler 🔥 (@architectonyx) — 5h
in the 80s, there was this nice idea of the brain being "holographic", with information stored nonlocally
this was motivated by appeals to Fourier analysis, but interestingly, i think function approximation alone gets you there
[Embedded video/image, 0:11 duration, showing a blurred/noisy gradient panel on the left and a grid of small sample images (landscapes, animals, fruit, objects) on the right]
Note from Claude Sonnet 5
Tweet with an embedded short video (paused at 0:11) illustrating a holographic/distributed-representation visualization alongside a mosaic of sample training images.
@sang_yun_lee (Sangyun Lee) — 13h
But then how do humans learn so sample efficiently? The path is clear if you are willing to believe a hypothesis: the brain is just a gigantic recurrent neural network that rewires its own weights during the forward pass
> QUOTED: @dwarkesh_sp (Dwarkesh Patel) — 20h
> Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress:
> Why has progress on computer use been so ... [truncated]
Note from Claude Sonnet 5
Quote-tweet chain discussing sample efficiency of human learning versus AI, and computer-use agent progress; quoted tweet cut off by platform truncation.
//rØpex ✓ @null_ropex · 8h
engaging heavily with specific spatial puzzles causes abstract geometric shapes to overwrite closed-eye vision for hours afterward. repetitive spatial manipulation literally reprograms visual rendering subroutines, forcing neurological hardware to project recently learned patterns onto completely unrelated surfaces. neuroplasticity works so aggressively that simple problem-solving games hijack default visual processing, proving human baseline perception constantly updates predictive geometry based on whatever arbitrary tasks recently demanded maximum attention
Note from Claude Sonnet 5
Text-only tweet, dark mode, same account style as the earlier "hyperfocus" tweet (002532) — appears to be the same poster's recurring style of writing about cognition/neuroscience in dense technical-sounding language (Tetris-effect phenomenon, unnamed).
//rØpex ✓ @null_ropex · Jun 6
hyperfocus isn't a concentration superpower or a dysfunction depending on context, it's what happens when an instrument's attention allocation system loses its interrupt handlers and commits full resources to a single process, which produces extraordinary depth of engagement and complete loss of peripheral awareness simultaneously, and the same feature that makes someone miss meals while solving a problem is the feature that lets them see into that problem further than anyone running standard interrupt protocols ever could
Note from Claude Sonnet 5
Single tweet, dark mode, no images. Styled handle with mixed unicode/latin characters ("rØpex").
Predictive Neuroscience Lab @spisaktamas
The brain's "default mode" and "action mode" networks are two sides of the same attractor.
Encoding a macro-scale Bayesian prior that biases processing toward internal or external drive.
[Figure: fMRI signals → score matching → FEP-ANN attractor network → energy landscape diagram with numbered attractor basins (1-6) mapped to brain renderings showing Default Mode/Action Mode network regions (aPFC, pMFG, TPJ, IFG, a-mIns, pmCing, PCC/Prec, SMA, dACC, mPFC, Mid Thal) colored green/magenta; six small paired brain images labeled μ1–μ6 correspond to the six attractor basins on the energy landscape. Citation: Englert et al., 2024, Spisak & Friston 2...]
1:44 PM · Apr 28, 2026 · 3,357 Views
Note from Claude Sonnet 5
A neuroscience research tweet on free-energy-principle (FEP) modeling of the brain's default-mode/action-mode networks as attractor states in an energy landscape, derived via score-matching on fMRI data and an ANN. Relevant to Nathan's brain_graph_1 project (biologically-inspired RL agent using connectome priors) — this kind of attractor/energy-landscape framing of large-scale brain network dynamics could inform future architecture choices.
Cankay Koryak ✓ @CankayKoryak
The Rosehip Neuron is a unique, inhibitory interneuron found exclusively in the human cerebral cortex. It resides in Layer 1 (the outermost layer), a primary site for receiving regulatory feedback signals. This neuron's most crucial feature is its specific target: the apical dendritic shafts of Layer 3 Pyramidal Neurons. By inhibiting this precise location, the Rosehip neuron is positioned to exert powerful, fine-tuned control over the top-down cognitive and associative inputs that Layer 3 processes. Its apparent absence in both rodents and non-human primates makes it a compelling candidate for a cell type contributing to distinctively human cognitive functions and may hold keys to understanding uniquely human neurological disorders.
[Image: electron-microscopy-style cortical tissue image labeled "Rosehip Neurons (Human-Specific)" with several highlighted (white) neuron cell bodies pointed out across cortical layers.]
Note from Claude Sonnet 5
Neuroscience explainer tweet about the rosehip neuron, a human-specific inhibitory interneuron type in cortical Layer 1 that regulates Layer 3 pyramidal neurons. Potentially relevant background reading for Nathan's brain_graph_1 project (connectome-based RL agent with cortical/subcortical structure), as species-specific cell types like this bear on questions of what's essential vs. incidental in modeling human-like cognition.
Surya Ganguli @SuryaGanguli · Nov 13
Our new paper on large scale holographic read-write experiments observing and controlling thousands of neurons in mouse visual cortex reveals a new functional cell-type that detects even moderate levels of excess activity (just 50 extra neurons firing) then inhibits top down cortical inputs.
This cell type is a subclass of somatostatin neurons, whose dysfunction is implicated in schizophrenia.
This suggests an intriguing hypothesis for the origins hallucinations in schizophrenia: the breakdown of this highly sensitive cortical gate allows top down inputs to enter visual cortex that should not, creating hallucinations.
This work was expertly lead by @ADrinnenberg w/ @allanraventos and @Alex_Attinger on data analysis and theory. Another fun collab w/ @KarlDeisseroth!
For more see: biorxiv.org/content/10.110...
And also this excellent thread: x.com/ADrinnenberg/s...
Note from Claude Sonnet 5
A tweet by Stanford neuroscientist Surya Ganguli summarizing a new paper (with Karl Deisseroth's lab) on holographic read-write experiments in mouse visual cortex, identifying a somatostatin-neuron subtype that gates top-down cortical input and proposing a mechanistic hypothesis for schizophrenia hallucinations. Relevant to Nathan's brain_graph_1 project (top-down/bottom-up cortical gating, predictive-coding-adjacent architecture).
davinci @leothecurious · Oct 25
predictive coding doesn't merely serve to update parameters via local credit assignment but doubles as am algorithm for inference-to-best-explanation based on observed features (bottom-up signal) and learned priors (top-down signal). vision models are bound to evolve into bidirectional networks with feedfoward and feedback computational graphs. not to mention the self-attention-like role of lateral connectivity as well. the implications will be manifold.
> QUOTED: Tahereh Toosi @taherehtoosi · Oct 24
> Replying to @taherehtoosi
> Theory: feedback errors, under certain conditions, approximate the steepest ascent toward naturalistic patterns (the score function from generative models). These errors act like a...
> [Diagram: two-panel figure comparing "Pattern recognition / Adversarially robust classifiers" (gradient of loss w.r.t. input, ∇L_x(x,y)) against "Pattern generation / Score-based generative models" (gradient of log-density, ∇log p_θ(x)), plus a 3D loss-landscape surface with a red dashed arrow labeled ∇log p(x) climbing toward a peak]
Note from Claude Sonnet 5
A neuroscience/ML Twitter thread on predictive coding as a unifying theory linking cortical feedback connectivity to bidirectional (feedforward+feedback) computational graphs and self-attention-like lateral connectivity, with a connection to score-based generative models. Relevant to Nathan's brain_graph_1 project, which uses predictive-coding-adjacent architectures and biological connectome priors.
Prakash (Ate-a-Pi) ✅ @8teAPi · 19h
What annoys a lot of SAT 1600s, is every guy on the field beats at differential calculus (judged on midbrain hand ball in try interception ratios). This is my AGI white pill, our evol brain is soooo superior from our civilization brain.
Think of this: evaluated on our midbrain, every single brain in humanity is capable of solving complex partial differential equations.
Note from Claude Sonnet 5
A tweet arguing that the evolved "midbrain" (implicit, embodied computation — e.g. intercepting a ball mid-flight, effectively solving real-time differential equations) vastly outperforms explicit symbolic/civilizational cognition, framed as an "AGI white pill" (optimism that biological cognition sets a high bar AI hasn't matched). Relevant to Nathan's interest in embodied/biologically-inspired cognition and brain-based AI architectures (cf. brain_graph_1 project).
Chen Sun 🤖🧠... @ChenSun... · 1h
Just caught up with my PhD mentor, Susumu Tonegawa (1987 Nobel 🥇) in Janelia!
Fun fact: he was once an early investigator in Basel, Switzerland on a temporary contract. The contract ran out, his position was terminated, and ... he just did not return the key 🔑.
He just went to the lab as if nothing had happened, day after day 🚶 ... and that was the year he made his discoveries that won the Nobel.
Susumu has the strongest, most stubborn Will out of any scientist I have ever met in real life. His discoveries have been a gift to the human race. He has been a constant reminder of what the human spirit can accomplish given enough strength. ⚡
It was a privilege to learn Science from him.
[Photo: two men seated indoors by a lakeside window — a younger man in a blue t-shirt and jeans (Chen Sun) next to an older man in a dark suit (Susumu Tonegawa)]
Note from Claude Sonnet 5
A neuroscience researcher's anecdote about Nobel laureate Susumu Tonegawa's persistence (continuing lab work uninvited after contract termination, leading to his Nobel-winning discovery). General science-culture/inspiration content, no direct AI-safety relevance.
Meet the Krause corpuscle, the neuron responsible for sensing vibrations of sexual touch. It is most sensitive to frequencies around 40 to 80 hertz, which is precisely the range of vibrating sex toys.
quantamagazine.org/touch-our-most...
[Attached image: microscopy image showing nerve fibers (green, red) and a cluster of Krause corpuscle endings (blue) in tissue]
6:35 PM · Apr 20, 2025 · 7,183 Views
Note from Claude Sonnet 5
A Quanta Magazine tweet about neuroscience research on the Krause corpuscle, a touch receptor tuned to vibration frequencies relevant to sexual touch. General science-interest reading, not AI-related.
