@BrainsAndTennis

I and @nicochristie ran the fly connectome and found the group of neurons (hΔH, hΔA, hΔI and hΔG) that could allow the fly to navigate using fast synaptic weight updates, not neural activations. This is fast-weight continual learning in a fly, something current LLMs don't do! > **News from Google @NewsFromGoogle** · 2026-09-03 > > For the first time, scientists have mapped the complete brain and central nervous system of an adult male fruit fly — a key model organism in science. 🪰 > > Working alongside HHMI Janelia Research Campus and the scientific community, @GoogleResearch scientists and researchers used > > [image: Vibrant 3D digital reconstruction of a fruit fly brain against a white background, displaying a dense, intricate network of multicolored neural fibers and cell bodies in shades of blue, cyan, orange, and purple.] [image: A complex 3D visualization of a fruit fly brain, featuring a dense, symmetrical network of thousands of tiny, multicolored neurons and fine, interconnected fibers in shades of blue, magenta, orange, and green against a solid white background.] --- The navigational system of the fruit fly is a crown jewel of systems neuroscience due to the work of some seminal neuroscientists (Larry Abbott, Gaby Maimon, Vivek Jayaraman, Barbara Webb), but it is an unfinished story. A fly that leaves a drop of food and wanders in the dark can always find its way back. To do that it has to keep a running sum of every step it has taken, a process called path integration. The neurons that report each step are known, but the neurons that add the steps up have never been found. There are two ways a brain can hold a sum like that. The usual answer is that some neurons holds it in activations and sustains these activations by exciting each other in a loop tuned so precisely that the signal neither fades nor blows up. Most models of navigation assumes this, and it is how RNNs and LLMs hold state too. The other answer is that nothing keeps firing at all. Each step is encoded into synaptic strengths (aka weights), and the sum of all synaptic strengths is the sum of the journey. A few papers have suggested the fly works this way but nobody has pointed to any candidate neurons, until now. Four neuron types, hΔH, hΔA, hΔI and hΔG, have no known functions, but we found that they have every ingredient option 2 needs. They receive input from neurons that report each step taken, they receive velocity-sensitive dopamine input that could gate memory writing, and they receive a reward-sensitive octopamine neuron that could reset the synaptic weights at food arrival. Simulations confirm this is a viable candidate for path integration. This is the key finding, but we have posted 3 other findings in links below. We tried to be exhaustive with published papers but may have very well missed some key published results, so inviting the cogniscenti to engage. Background, methods, experiments, results, as well as relevant citations: https://pwang724.github.io/fly-circuit-exploration/findings/index.html… Panoramic circuit view: https://pwang724.github.io/fly-circuit-exploration/circuit.html… GH repo: https://github.com/pwang724/fly-circuit-exploration… --- The same way levent gets to do better math because of ai, someone should be doing better neuroscience because of ai. I have been unplugged from systems neuroscience for the last two years and but have iterated closely with fable 5.1 over the course of two days. the good: fable 5.1 was creative and took a lot of scientific paths that were in high taste and most of the time found good sanity checks. it is really not emphasized enough how far along fable's research and scientific capabilities are compared to other models, probably because it can't be felt by making threejs games the bad: it needed a lot of pushing to go outside of what is currently known, and even then, it still clung on to existing hypotheses (storing spatial memories in synaptic weights have previously been hypothesized). also, getting it to communicate effectively to me, to layman, and to neuroscientists, was extremely triggering. it does not seem to understand a theory of mind over what people know and don't know, and how to properly define words that are used and staying on a certain level of abstraction throughout the discourse. lastly, claude code should compact at ~400k, not 1M. responses go haywire to a point of no return after this. --- ##### Comments > **nico @nicochristie** · [2026-09-14](https://x.com/nicochristie/status/2099564788737339662) > > 🍌 ++++++−−−−→→→ ·DA ∴ (2,3)∈{hΔH,hΔA,hΔI,hΔG} ; hΔM:(2,3)↦(−2,−3) ; −−←←←++ ∴ (0,0) ; 🍌 ; OA: w:=0 > **Ethan Shea @ethshea** · [2026-09-14](https://x.com/ethshea/status/2099581339800342559) > > So this is why a fly keeps returning to me even when I shoo it away? > > How can I get the fly to reset it's home so it doesn't bother me anymore > > > **Peter Wang @BrainsAndTennis** · [2026-09-14](https://x.com/BrainsAndTennis/status/2099591707545456792) > > > > gotta taze it bro > **Team Reagent @Reagent\_Systems** · [2026-09-14](https://x.com/Reagent_Systems/status/2099585123536585185) > > Isn't this what Ants do? > > > **Peter Wang @BrainsAndTennis** · [2026-09-14](https://x.com/BrainsAndTennis/status/2099591817025245476) > > > > yes! it is. classic experiments with ants on stilts > **Eli @elidolloff** · [2026-09-14](https://x.com/elidolloff/status/2099582798231453997) > > Peter very cool now lets put it in a drone > **macphee @macpheeeee** · [2026-09-14](https://x.com/macpheeeee/status/2099578863890416098) > > This is absolutely mind-boggling > **Espada @SantoshiEspada** · [2026-09-14](https://x.com/SantoshiEspada/status/2099583427670372696) > > are we so back then? > **Generic Working Class Citizen @GenericPlebeian** · [2026-09-14](https://x.com/GenericPlebeian/status/2099584335754006979) > > Low-key, fairly sure the fly will be harder to lock down and regulate, and might become more capable than most existing "ai" on a local level. Pandora's box might be open, and we're living in brief calm before the storm. A training-lag delay, as it were > **inwyrd i @inwyrdinfinitum** · [2026-09-14](https://x.com/inwyrdinfinitum/status/2099593992409141363) > > “Wow, how did nature figure that out”. > > It’s consciousness (unlimited intelligence) imagining itself as a “finite” being. > > [image] [image] [image] [image] > **LightSoldier @deathguardian90** · [2026-09-14](https://x.com/deathguardian90/status/2099592886333415742) > > so what now, scale by stack fruit fly brain? > **wolfie @wolfie\_** · [2026-09-14](https://x.com/wolfie_/status/2099575190892229085) > > @dylantirandaz > **Chuck Petras @Chuck\_Petras** · [2026-09-14](https://x.com/Chuck_Petras/status/2099587103546056919) > > @BrianRoemmele @GrandpaRoy2