Adam Safron

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Adam Safron

Adam Safron

@adamsafron

Katılım Ağustos 2010
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Adam Safron
Adam Safron@adamsafron·
Kenneth Hayworth@KennethHayworth

So, some people are asking me why this EON fly video doesn’t show real ‘uploading’ since it does simulate a real connectome. The most important reason is that the functional parameters that define the dynamic behavior of individual neuron and synapse types in the connectome are unknown. Instead, they used an existing model (nature.com/articles/s4158…) which substitutes these with guessed parameters and grossly simplified dynamics. As made clear in that older paper, these are not sufficient to recreate the activity patterns that would be seen in the real fly. The simplified dynamics would not, for example, be able to choreograph the timing of leg muscles during walking or grooming, or the dynamics of the compass neurons encoding the fly’s heading direction, or the myriad other neuronal dynamics that make up the fly ‘mind’. So not an ‘upload’ by any reasonable definition. In fact, the simplified dynamics they used have only been demonstrated to approximate gross correlations along major sensory-motor pathways for a handful of neurons. For example: activating a sugar sensing neuron causes gross downstream activation that elevates the activity of feeding neurons. It is this handful of very, very crude and basic correlations in the simulated connectome that are being used to drive the EON simulated fly. If they had said that from the start, then I would have had no issue. But instead, they made the bold claim that they had “uploaded a fly” and presented a video of said fly walking over a landscape with highly articulate legs, visually navigating through the terrain to a food source, grooming its antenna with eerily fly-like leg motions, etc. Any reasonable layperson would assume that these visually exciting articulations are the ones being controlled by the simulated brain’s dynamics instead of being faked by computational add-on routines. There are now many secondary reports of this on YouTube and all of them seem to make this reasonable assumption (e.g. youtube.com/shorts/Z7NNP1Z…). And who could blame them? Many neuroscientists also made that assumption before EON started to spell out what was really behind the video millions of views and over a day later. To make clearer just how misleading EON Systems’ video is and how outlandishly laughable their ‘uploading’ claim is, below is an imagined back-and-forth discussion between a [Reasonable Layperson] and a [Neuroscientist] trying to explain to them what is really behind the video: [Reasonable Layperson] “Look at the complicated leg motions as the fly walks… the timing of all those dozens of individual muscles being controlled by the dynamics of the simulated neurons… and they say that they used no reinforcement learning to tune parameters, just the connectome… that is really impressive!” [Neuroscientist] “Well actually no… those leg movements are actually coming from a program unrelated to the connectome. The connectome used didn’t even include the central pattern generator circuits in the ventral nerve cord responsible for controlling leg muscles.” [Reasonable Layperson] “Oh… so in what sense is the simulated connectome controlling walking?” [Neuroscientist] “It looks like they just found a few neurons in the brain connectome that are correlated with right/left/forward motion and used these to ‘steer’ the pretend walking routine.” [Reasonable Layperson] “Oh… But the activations of those ‘steering’ neurons are reflecting the complicated dynamics of tens of thousands of simulated neurons in the fly visual system as it moves through the virtual world, avoiding objects and heading toward its visual goal, right?” [Neuroscientist] “Well actually no … The visual system and virtual world are essentially ‘decoration’… the flashing dynamic neural responses as the fly moves through the virtual environment are designed to give the viewer the impression that the simulated fly is actually seeing the world and making walking decisions based on those visual responses. But, in fact, they could turn off the lights and the fly would behave identically.” [Reasonable Layperson] “Oh… so how does the fly walk toward the food then?” [Neuroscientist] “Well… it looks like they simply imposed an odor gradient in the virtual environment that is centered on the virtual food. The fly has two sets of odor receptors (right and left) that sense this gradient and the activation of these in the connectome is correlated with the activation of the ‘steering’ neurons. So if the left odor neuron activates more than the right then the fly steers left.” [Reasonable Layperson] “Oh… so it is like one of those toy cars that moves toward a light because it has right and left light sensors cross-connected to right and left motors… Gee, I thought a fly was more complicated than that.” [Neuroscientist] “Well actually a real fly is. Real flies have dozens of behavioral states that allow intelligent behavior in a complicated visual and sensory environment. In fact, a real fly contains a set of neurons which act as an internal compass updated by the visual environment and the fly’s walking.” [Reasonable Layperson] “Oh… and their connectome has those internal compass neurons?” [Neuroscientist] “Yes. They used the full brain connectome that contains those compass neurons.” [Reasonable Layperson] “...And their compass neuron activations are tracking the visual environment just like in the real fly?” [Neuroscientist] “Oh sweet summer child… those compass neurons exist in their connectome simulation, but no one knows enough about their functional parameters (synaptic weights, time constants, etc.) to simulate them accurately. They light up in pretty patterns totally unrelated to how they would in a real fly walking through that visual world.” [Reasonable Layperson] “Oh… and the complicated leg movements it shows during antenna grooming… is that also just a faked recording?” [Neuroscientist] “Yes. All the complicated leg motions shown during grooming are faked by a hard-coded program. But they turn that fake routine on or off by looking at some neurons in the connectome that are correlated with actual grooming behavior triggered by dust accumulation on the antenna… well really they fake the dust too by just activating a set of neurons after a delay.” [Reasonable Layperson] “And what did EON Systems do? Did they acquire the connectome? Did they determine the neurotransmitter types? Did they do the calcium imaging experiments to determine the steering and grooming neurons? Did they make the mechanical fly model?” [Neuroscientist] “No. Those were all done by real labs who were kind enough to carefully write up their results in open journals and to post their results and code openly online…. It looks like Eon Systems just took their code and put it together with a virtual environment designed specifically to trick viewers by triggering behaviors in misleading ways.”

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Adam Safron
Adam Safron@adamsafron·
“Generating a coherent video depends on simulating a coherent world—if the laws of reality change between frames, the output would be nonsensical. Such rudimentary world models can fill in details of the world beyond what they have been fed: give one a picture of a maze and it will be able to draw a route through it; present it with a photo of hands holding a jar and it will accurately model the movements required to open it." This depends on what we mean by “simulating a coherent world.” It is quite possible that models can learn to generate sequences that are highly consistent with physics by detecting patterns such as spatial continuity/contiguity of features (e.g. contact forces and material properties), or that objects tend not to suddenly start levitating or teleporting, but this does not mean that they are running simulations in their latent spaces that would correspond to causal world models with understanding of physical laws. I believe their ongoing “hallucinations”/inconsistencies/anomalies are consistent with this somewhat deflationary view. “Dr Li’s startup, World Labs, has built a world model called Marble that can create digital versions of 3D worlds which are internally consistent and complete… What’s more, spaces are not hallucinated afresh each time the user looks around; instead, they are created in their entirety from the off.” While such a spatial world model could be useful for AI applications, I’m not sure this is an accurate description of the spatial intelligence of animal brains. “But what if these complicated approaches are superfluous? If existing generative AI systems can already do useful things in the real world, then maybe they already contain some kind of world model within them. That’s the view of Ilya Sutskever, an OpenAI cofounder, and many of his former colleagues still at the lab. Training a large language model is, he said in 2023, no more than “learning a world model”. Compressing all the information contained on the internet down into a few hundred gigabytes of numbers is possible only if a system “learns” the underlying principles behind that information.” It depends what we mean by learning the “underlying principles.” Does Ilya still believe this? In more recent comments it seems he largely abandoned this version of the scaling hypothesis and believes we need to create new kinds of (potentially biologically-inspired) architectures. “There is some evidence he may be right. In 2023 a language model trained on a list of moves in the game Othello was shown to have reflected the board state within its own neural network—even though it had never seen an Othello board nor been taught the rules of the game. It was a detailed enough representation that the researchers could identify specific parts of the neural network that stored the colour of individual pieces.” In an upcoming special issue of Royal Society on “world models” that I recently co-guest edited, Krakauer, Krakauer, and Mitchell take a closer look at OthelloGPT and describe how it’s more like a bag of heuristics than an integrated world model: arxiv.org/abs/2506.11135 1drv.ms/w/c/bfa212aa22… “Bigger language models are likely to have more complex world models inside—if only researchers could find them… That suggests the systems aren’t simply stringing words together: they have a consistent understanding of physical features in the real world, which they draw on to answer questions. It sounds suspiciously like what you would expect from an internal world model.” While we should expect bigger models to have more complex modeling capacities, that doesn’t mean we should expect them to learn to represent the causal structure of the world via scaling, and feature-interpretability/controllability doesn’t necessarily imply causal modeling. “Not everyone agrees. LLMs, Dr Li argues, are just “wordsmiths in the dark”. Being able to use language to describe the world, she says, does not mean they have a grounded understanding of it. Like a student who has only read about a foreign country, there’s a missing piece of knowledge that can’t be patched with books, she says.” Thank you.
The Economist@TheEconomist

Giving the ability to understand how the world works to AI systems was a promising area of research before large language models sucked away the world’s attention. Now that attention is back econ.st/4rBsC37 Illustration: Sandro Rybak

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Adam Safron
Adam Safron@adamsafron·
If either/both of you were interested in writing position papers on criteria for causal world models for an upcoming special issue of Royal Society (Phil Trans A), please backchannel me (in the next few weeks) and I can include you as featured submissions with the journal. adamsafron.com/agency
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Judea Pearl
Judea Pearl@yudapearl·
Models specified by "action conditioned" knowledge, as in classical control theory, are surely "causal" but are not "world models" in the causal sense. The former requires specifying the effect of every action or combination of actions; the latter permits the derivation of such effects from a compact description of (causal relationships in) the world, not of actions. To appreciate the difference, see "Does Obesity Shorten Life? Or is it the Soda? ucla.in/2EpxcNU Another way to appreciate the difference is to consider a city road-map, as an example of world model, and contrast it with GPS instructions, "Go right, left or straight", which contain the same information about approaching the destination, but not about handling unforeseen road blocks.
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Judea Pearl
Judea Pearl@yudapearl·
We are notified of a unique event in the history of AI-investment: Yann LeCun's AMI Labs launches with $1.03 Billion to build AI "that understand the world". frenchtechjournal.com/yann-lecuns-am… Comment: There is no "understanding the world" without causal modeling of the world and, strangely, LeCun has not shown any interest in causal modeling in the past. I do not know what to make of it except to repeat my comments when the WSJ article came out: archive.is/2025.11.16-234…. I said: "evidently, LeCun has just discovered "world models", I hope to see lots of funding pouring into CI soon."
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Satpreet (Sat) Singh
Satpreet (Sat) Singh@tweetsatpreet·
@adamsafron We're running a workshop on such models at the upcoming CoSyNe conference. Check out our lineup and last year's talks here: x.com/tweetsatpreet/…
Satpreet (Sat) Singh@tweetsatpreet

📣 Excited to announce the 2nd edition of our workshop “Agent-Based Models in Neuroscience: Theory, Autonomy, Embodiment & Environment” at @CosyneMeeting #CoSyNe2026! 🧠🤖🌍🪰🐟🐭💪🧘🏃 🗓️ March 17, 2026 📍 Cascais, Portugal 🔗 Speakers and schedule: neuro-agent-models.github.io

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Adam Safron retweetledi
Jorge Bravo Abad
Jorge Bravo Abad@bravo_abad·
Flattening convolutional neural networks to understand how the brain hears Convolutional neural networks (CNNs) can predict what a neuron in auditory cortex will do better than any classical model—but their multilayer complexity makes it nearly impossible to say why. That gap between prediction accuracy and interpretability is one of the central tensions in using deep learning to model biological systems. Jereme Wingert and coauthors tackle this problem by taking a trained CNN and compressing it into a low-dimensional subspace model that is both accurate and interpretable. Microelectrode arrays record thousands of single neurons across cortical layers in the auditory cortex of awake ferrets listening to up to 56,000 unique natural sound segments. A four-layer population CNN is trained to predict each neuron's firing rate from the sound spectrogram, substantially outperforming the classical linear–nonlinear STRF model. The key move: for each neuron, they compute the gradient of the CNN output with respect to the input at every time point, producing a large collection of locally linear filters. PCA extracts a compact set of spectrotemporal filters (3–13 dimensions) defining the neuron's tuning subspace. A small network then learns the nonlinear mapping from stimulus projections in that subspace to firing rate—a subspace receptive field (SSRF) that works as a compact lookup table for neural responses. This flattened model accounts for 95.4% of the variance explained by the full CNN, yet is simple enough to visualize directly. The biological findings are remarkable. Neurons in the same cortical column share a similar tuning subspace, but their SSRFs sparsely tile it—at any moment, only a small fraction respond strongly, consistent with efficient coding via recurrent inhibition. Putative inhibitory neurons have larger SSRFs with upward-facing nonlinearities (responding broadly to high-contrast stimuli), while excitatory neurons show smaller, selective SSRFs with downward-facing nonlinearities. These properties also vary with cortical depth, linking specific computations to distinct circuit elements. The message: gradient-based linearization plus dimensionality reduction can bridge the gap between deep network complexity and interpretable encoding models. The approach is general and demonstrates that predictive power and mechanistic insight need not be at odds. Paper: nature.com/articles/s4159…
Jorge Bravo Abad tweet media
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Adam Safron
Adam Safron@adamsafron·
Looking forward to reading this.
Jorge Bravo Abad@bravo_abad

A neural network that forms, understands, and communicates concepts like the human brain Humans compress rich sensory experiences into compact mental concepts—the word "dinner" can evoke a full scene of flavors, sounds, and images—and use those concepts to communicate meaning without the original stimuli present. Building artificial systems that replicate this process remains a major challenge. Standard vision models entangle knowledge across millions of parameters, making it impossible to extract or transfer a single "concept." Multimodal LLMs rely on pre-existing language symbols rather than forming concepts from sensory experience. Liangxuan Guo and coauthors propose CATS Net, a dual-module framework that bridges this gap. A concept-abstraction (CA) module compresses visual information into 20-dimensional concept vectors, while a task-solving (TS) module performs image classification under hierarchical gating control from those vectors. The CA module transforms each concept vector into layer-wise multiplicative signals that dynamically reconfigure how the TS module processes features—turning one network into many classifiers depending on which concept is provided. The results go well beyond classification. The emergent concept spaces align significantly with two human semantic models—Binder65 and SPOSE49—despite training purely on visual categorization, even capturing non-visual dimensions like spatial, temporal, and emotional features. The communication experiments are particularly compelling. Two independently trained CATS Nets develop separate concept spaces, yet share significant structural similarity. A translation module aligns them, allowing a "teacher" to transmit a novel concept to a "student" as a simple vector. The student, never having seen images of that category, classifies them well above chance—through pure symbolic exchange, without updating any network parameters. Using fMRI data from 26 participants, the concept layer correlates with human ventral occipitotemporal cortex activity, while the CA module's gating aligns specifically with the brain's semantic-control network. Models that spontaneously converge on similar representations show even stronger brain alignment—echoing the idea that biological and artificial systems under similar constraints find shared solutions. A concrete framework where concept formation and understanding coexist, compatible with human cognition and pointing toward AI systems that build grounded representations independently and align them through shared symbolic interfaces—much as humans do. Paper: nature.com/articles/s4358…

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