Seeing red

[emjay]

@emjay, If brain is comp...then no, if all components of a comp system yield the same state simultaneously that's critical failure.  It can no longer comp (and it can't reboot -itself- as that would be a signal change as well).  If you were designing a board with this problem in mind, for whatever reason, say..in this case, you were going to use it around alot of ambient power discharges that might interfere with the circuit.....what you -could do- is run an output from some (or all, theoretically) gates in process and if, and only if all gates are on simultaneously, that trips a nand wired directly to the PS, shutting the system down (ofc you'll have just engineered a single point failure criteria for your whole system..if that nand malfunctions so too does the entire board).  You'll actually find error checking arrays like this in your home PC, but you'll find even more exotic arrays in boards meant for exotic purposes.  I know that probably doesn't answer your question outright, but it gives you a little more context for the question.  That's a mechanical problem which does exist in comp systems, for which a solution exists...but.....it's the kind of solution which invokes near instant suspicion in the case of an evolved computer.  Thankfully, we don't seem to be subject to this problem.  I picked up on something earlier, that I wanted to comment upon.  There's an upper limit to how fast neurons can fire, and there's also an upper limit to how quickly those signals can move as well.  Presumably, there's time for error checking in transit, and there must be some sort of frontloading or backloading of information when the amount of work being done exceeds the ability of the system to transmit the data as a packet to the relevant centers.  This is the sort of thing that answers a question benny had awhile back as well..."what do I gain from considering mind material or considering mind comp" - well, you gain insight into how existing comp systems work around problems we would perceive or describe as cognitive problems.  The great and wonderful "well, you could try to solve for x like so".  That gives us a hell of a working start to exploring how the brain does it.  We can say.."were going to engineer "problem x" in the brain, and then see if it's doing anything that resembles this solution in response to that".  For example, present each eye with a different image isolated from the other.  What do we see the brain doing, how does it resolve conflicting inputs?  I mention this because, mechanically, your situation above where all neurons are in the same state, is the penultimate expression of the conflicting inputs problem.

Also, in your convo with Jorg you mentioned that you didn;t see the need for programming, that the hardware would handle and explain all function.  That's always the case..even when there -is- programming.  Programming is just a set of instructions for the hardware to be in a certain state.  It's -always- about the hardware..your nn weighting process...is writing a program, constantly.  That's the point at which "classical" comp mind fell flat on it's face and things like NN and hueristics took over as practical descriptions.

I'm not quite sure what you mean by 'frontloading' and 'backloading' but there are feedforward and feedback connections which might be close to what you're talking about. Say you've got four layers L1-4 connected bidirectionally with each other, like my earlier example but all on top of each other this time rather that two on a middle 'level', then if L1 not only projects to L2 but also projects to L4... but still say that L4 only projects back down to L3, not L1 as well. Then what would happen is L1 would activate L2 and L4 at the same time, and L4 would start sending feedback down to L3, biasing it, and the same from L3 down to L2, and thus the activation going up from L1 and coming down from L4 would meet in the middle. I don't know if that's what you mean by frontloading but it allows the network to act according to expectation.

Also, using chemical synapses slows down transmission, as does the opening and closing of all these channels, but there are other types of transmission (which I don't know that much about... sorry) which are purely electrical and don't use neurotransmitters, so if you're looking for fast connections that may be where to look

And yep, that's what I'm about as well... you do it with comp mind, I do it with neural networks... kind of reverse engineering the brain based on first principles, proposing a problem and a solution and seeing how well it matches up with what is observed.

When I was talking about the all-on/all-off problem I was primarily concerned with the effects on the extra-cellular fluid and whether it would even be able to function as a whole network. But if you're talking about that as a precursor to a conflicting inputs problem, just to say, just in case this was what you meant, that the brain is excellent at selecting between two equally valid patterns of input. Picture if you will a household radiator with its sticky-out bits... if you look at that it's an optical illusion where you can see either the sticky-out bits or the indented bits as being in front. There are better examples of this illusion I'm sure but this is the one that comes to mind right now. Anyway your mind flips between seeing it one way and seeing it the other and it has two ways of resolving the conflict. The first is inhibition, which in practice will force one 'winner' out of many neurons in a layer... shutting down others so that only one comes out on top... and all it needs is a slight fluctuation in activation for one of them to gain the upper hand. So say we've got all of our neurons on as per this edge case, then inhibition will still cause the network to settle into one state (where the neurons in the layer with inhibition in this case are binding neurons for a whole context below). If it really is tight and nothing can get the upper hand, that's where neuron fatigue comes in... a neuron can't fire indefinitely so when it turns off it can tip the balance.

As for the programming, yep I see what you mean. The programming in this case probably comes about not just from general neural network dynamics but also from the specific connectivity patterns... different connectivity patterns will create different network dynamics... as for instance is the case with the bidirectional connectivity of the cortex... that creates completely different (and really cool ) network dynamics than areas without that sort of connectivity.