Why a computer scientist can't build a brain—and what that reveals
Could a computer scientist build a brain?
A computer scientist handed a brain's wiring diagram and asked to write a genome-sized program that builds it hits a wall: the genome is too small to store per-synapse wiring, and blind axon search would take too long. This thought experiment shows that only strategies resembling real developmental biology—using cell types, molecular cues, and local rules—can scale from 302 neurons in C. elegans to 10^10 in humans. The convergence suggests brain wiring is a computational necessity, not just evolutionary contingency.
The genome is too small to store per-synapse wiring, and axons searching blindly for targets would take too long.
- sim04ful
Imo, there isn't anything fundamentally special about the brain as a cognitive substrate. Even a pool of water could have cognition (reservoir computing), i feel that it's a general property that can emerge in any sufficiently high-dimensional physical system with enough internal degrees of freedom to explore many configurations, and enough coupling and stability to remain coherent, and enough plasticity for experience to leave persistent changes in its future dynamics.
And subjecting such a system to sustained external objective pressure...where some internal organisations perform better than others and it would progressively accumulate some useful structure about its environment.
The brain is just a really old artefact of billions of years of blind environmental weathering of matter being repeatedly shaped by the requirement that its host continue to exist as a coherent, persistent entity.
In short the brain is not fundamentally special in the principles that make cognition feasible.
What i'm trying to say in response to this article is that he's working of a far higher level of abstraction than is required. It's putting software before physics.
- jekude
I think the question and article is fascinating. I do think that one should not only look at the genetic representation, and the algorithm a cell runs, but also the formalized environment that runs the simulation.
I’ve been thinking about this for a few years and I truly believe these cells need to be able to “move” in either a 3d lattice similar to a cellular automata or become nodes in some cyclic graph (or maybe both).
For the genetic representation, I had an insight that DNA/RNA looks strangely similar to SKI calculus (combinators) and have been using a Church encoding to translate the SKI program to an “action” per turn (move, connect, spike, divide, etc.). It has a nice property that the cell’s “program” and the spikes between them, and the input/output to the simulation itself can be the same thing (just a string of combinators).
Either way the author is spot on when it comes to this sort of thing having the properties 1) “always on” (no separate training mode), 2) local interactions, 3) everything stems from a genome, 4) each cell is its own little mini-program with the same genetics as everyone else.
All I can say is that evolutionary program is hard and takes either massive compute or large timescales to run the simulations. Fascinating stuff though
- ppsreejith
> The program must fit in a genome-sized instruction set of about 1 gigabyte
True but this can be a lot larger, maybe even a 1 exabyte instruction size since it depends on he programming language used to express the program. And even accounting for the invariance of runtimes at scale*, the constant factor might be gigantic since we have to build up a lot of cellular machinery first. Reminds me of the Carl Sagan quote: To make a sandwich,you must first construct the universe!
* For example, to translate a program from language A to language B, you can dedicate a constant size to write a language A to B translator. Thus at large program sizes , the kolmogorov complexity (i.e instruction set size) is fairly similar between programs since the size of the program dominates the size of the translator program which is constant. But the constant factor for cellular machinery might be gigantic**
** Or not if we only need a rough simulation
- oezi
One aspect missing from TFA is that in biological brains a lot of the training is performed during query time. If we assume 1 GB of DNA is enough to encode the brain's overall structure we still do need training data (e.g. visual/auditorial/tactile) to build out the strength of the synaptic connections.
- hoppp
You'll need a simulated environment because brain development depends a lot on the environment, it's not purely genetic.