I read other research recently that showed that real synapsis have additional dimensions of data that are stored than previously thought. Whereas before it was thought they were a simple binary fire/no fire, they are also transferring data through width and height of the firing. Because of this neural networks designs may be missing a key feature.
That has never been thought by anyone but computer scientists who never looked at a biology textbook.
To begin approximating what a lone spherical synapse would actually do you'd need to solve 2^n coupled second order differential equations where n is the number of ions used.
That is before you throw in things like neuro transmitters and the physical volume of a cell. Simulating a single neuron accurately is beyond any super computer today. The question is how inaccurately can we simulate one and still get meaningful answers.
We are way to stupid to solve this riddle but I'm rather optimistic we could build something that solves it or at least build something that can build something that solves it.
I'm looking forwards to all the "easy" things it will figure out and stick us in a loop of "why didn't anyone think of that?" Something like the nth generation ML offspring solving the building of viable neurons at scale by breeding some single cell organism.
We didn't solve flight by building a bird. We solved it by building a plane. The problems we care about might not be solved by neurons at all. But right now using ANNs as a model of the brain is like saying that a bunch of kites have the same behavior as a flock of birds.
Seems to me, a bunch of asynchronously moving kites would be a more efficient approach to modeling a flock of birds than, say, iterating all those birds' positions frame by frame in a synchronous loop.
Flocks are best modeled as aggregates of extremely simple agents who want to avoid collisions, avoid complete separation and move with the center of mass of the flock.
You're willfully missing the point. I'm not talking about the behavior of a kite (the kite could be designed to act autonomously and do whatever). I'm saying asynchronous analog modeling is by definition going to be both more efficient and capture a smoother gradient of concurrent states than a giant for/next loop (essentially what all neural networks do today) where each agent is checked and modified sequentially.
It's not exactly a "new" finding that neurons communicate signals to each other by means other than purely electrical signalling. The existence of well over 100 different neurotransmitters has been known for some time, and these are used to create unique signal cascades within the receiving neuron depending on the exact concentration sent. There is nothing equivalent to this in artificial neural networks. Artificial neural networks are not necessarily using binary neurons. Each activation unit may receive and pass on a continuous signal, but this tends to be a single floating-point number, usually normalized to be between -1 and 1 or between 0 and 1. Biological neurons are sending at minimum hundreds of these continuous-valued signals to each other. Likely, the closest way to build an equivalent artificial neural network would to have each neuron have hundreds of connections to all the other neurons it is connected to, rather than just one, but even that isn't necessarily equivalent, as what actually happens internal to the cells in response to these signal cascades isn't all that well-understood, but it involves quite a bit more than just determining what sort of signal to pass on to the next synapse.
