It Takes Two Neurons to Ride a Bicycle

mmalshe 4 days ago 21 commentsRead Article on fermatslibrary.com

DE version is available. Content is displayed in original English for accuracy.

⚡ Community Insights

Discussion Sentiment

89% Positive

Analyzed from 821 words in the discussion.

Discussion (21 Comments)Read Original on HackerNews

BiraIgnacio•11 minutes ago

> U-2200, a non-corporeal entity claiming to be the prehistoric Johorean god of forgetting how to ride a bicycle, engages Quinn in a conversation, suggesting she take a month off in Barbados, drink alcohol, or resign from the Organization.

- There Is No Antimemetics Division

ebhn•about 3 hours ago

Nice article, but the methods they used seem more like they just hand wrote a function for the task and called the function neurons based on how it was implemented. It is encouraging though that a simple network can be found for a complicated task like this, kind of like the Tiny Recursive Model that came out last year.

KolibriFly•23 minutes ago

I think that's basically right. The "neurons" framing feels a bit loose here; it's more like a very compact hand-designed controller expressed in neural-network-ish terms

actinium226•about 1 hour ago

This looks like they simply reinvented PID control. The inputs to the beyond are desired states minus actual states, which is basically how PID works.

KolibriFly•27 minutes ago

The useful insight is not "compare desired state to actual state"; it's deciding which state to control

mk_stjames•about 1 hour ago

It seems like it should say "It takes Two Neurons to Steer an already moving Bicycle".

The simulation is so simplified that I see no terms for the control of pedaling. Riding a real bicycle isn't just about steering and leaning a bit. You need to propel the bicycle a certain amount.

The paper buries this in the following:

  >Although the two-neuron network controller works well for a range of speeds, one thing the controller does not do is to try to dampen the instabilities that can arise when riding too slowly or in too sharp of a turn. (This would probably require a third neuron that isdedicated to this task.)

They say 'damping instabilities' but it is way more than that, because as anyone who has learned to ride a bike knows, the hard part is getting started at that zero point of forward velocity - how to apply torque to the crank at the same time as compensating with the steering to balance at such low momentum. It's not a trivial solution to 'damping instabilities' when getting going in the first place is the most difficult part (as any 5 year old child will demonstrate).

gpvos•about 1 hour ago

(2004)

Previously:

- https://news.ycombinator.com/item?id=19196664 (25 comments)

- https://news.ycombinator.com/item?id=16215130 (88 comments)

fintler•about 3 hours ago

I had fun reading this. Thanks for sharing.

With dendritic compartments, this seems like a waste of a perfectly good neuron that we could productively use elsewhere. ;)

Note that a SINGLE neuron can compute nonlinear functions like XOR.

Shameless plug: If anyone is interested, I did a post a while back on how neurons can act as logic gates:

https://blog.typeobject.com/posts/2025-neural-logic-gates/

This article builds on the first and creates a half adder out of neurons:

https://blog.typeobject.com/posts/2026-timing-is-the-bit/

shomp•about 2 hours ago

Research question: does it make sense to make a new family of logic gates using neurons? My intuition says there is a rich texture/fabric to uncover here. The best analogy on hand right now is legos: rather than 2-knotch legos [standard gates like NAND, XOR] what about some sort of new, irreducible gates that are bigger "legos"? Been a while since I played with logic gates but my intuition says there is something lurking below the surface. A new class of irreducible gates, maybe cross-connections? Like compacted multilayer gates? Think SHA-512, how certain bits feed into different layers of the "puzzle". Optimistic this thought-amalgam serves you in your continued research :)

fintler•about 2 hours ago

Yes!

I started going down the path of building a ripple carry adder already (which seems to work fine). Then I was going to try for a full on ALU, then some sort of ISA that sits on top of it all.

I have no idea what the end result will look like if it all comes together. Hopefully I'll find some weird primitives along the way. :D

It's very hand-wavy, but I'm kinda hoping I can somehow have a machine manually constructed out of neurons that can naturally interact with one built with looser hebbian learning rules.

shomp•about 1 hour ago

The ISA could be really cool, having lots of "combo" commands that might reduce program length dramatically. Think ADD and MULT and SHIFT all in one command, to give a simple analogy.

On the interaction, one system uses a clock signal / metronome and the other is all cascades. The clock signal is like a metronome calibrated to the duration of the longest cascade = "critical path." It seems clear that these can interact smoothly, as one simply has the training wheels of the clock, while the other is about progression-via-propagation.

hyperhello•about 3 hours ago

So can we have self-driving bicycles?

KolibriFly•19 minutes ago

Self-balancing bicycles, sure. Self-driving bicycles that navigate city streets safely are a much larger problem.

onesingleblast•about 2 hours ago

Yes and they'll have one of those wetware computers on board

soupspaces•about 2 hours ago

Recumbent bike with lidar and maps? Sign me up.

wrsh07•about 2 hours ago

> The output of the ﬁrst neuron is fed into the second neuron, whose outputis connected to an actuator which applies the speciﬁed amount of torque to the handlebars. As inputs to the network, we provide the desired heading θ_d, as well as the current heading θ and the degree to which the bicycle is currently leaning γ, along with their derivatives ˙θ and ˙γ.

It's somewhat important to consider the inputs, because if you want to make a classifier that can classify "inside circle vs outside circle" but the network needs to derive the nonlinearity itself, then you end up needing a more complex network

Eg on the playground^, see how many neurons you need to train a circle without using more than x1 and x2?

And yet, if you give the network x1^2 and x2^2, it can solve it with minimal additional neurons.

^ https://playground.tensorflow.org/#activation=tanh&batchSize...

shomp•about 2 hours ago

The instability ink-lines look like a flower blooming.

Observation: 2 neurons, 2 wheels. One for each?

KolibriFly•16 minutes ago

Sadly not quite one per wheel, though that would make the title even better

klas_segeljakt•about 2 hours ago

What about drawing a pelican riding a bicycle?

Razengan•about 2 hours ago

My neurons still don't get themselves: What kind of processing happens INSIDE neurons?

thatxliner•about 1 hour ago

now make this one-bit quantized