================================== One can read this information processed in the evolving brain directly from electrical patterns that occur within neurons with a very simple technique http://dx.doi.org./10.1016/j.jneumeth.2005.05.006, Since all this information is electrically integrated in the brain see also http://www.sciencedirect.com/science/article/pii/S0165027012001021?v=s5 this method should be more than enough Indeed, we can complicate the problem, study synapses, the path of propagation......... Why would you like to do that when we have a different goal? Dorian =================
On Wed, Sep 25, 2013 at 10:29 AM, Steve Richfield <[email protected] > wrote: > Dorian, > > On Wed, Sep 25, 2013 at 5:44 AM, Dorian Aur <[email protected]> wrote: > >> We need to use their power of computation directly, a hybrid model >> (evolving brain - digital computer) will solve many issues regarding in >> vivo monitoring >> > > I'm not sure what you are saying here. All "monitoring" already goes > directly into a digital computer, if for no other reason than to clean up > the signal and compute things like dV/dt and peak values that may have been > obscured by limited bandwidth and sampling rate. > > However, there is more that remains invisible than there is that can be > seen. The ONLY thing that can now be seen is the instantaneous voltage, and > to my knowledge there are NO proposals to monitor anything else. > > This is what detoured neural networks - the belief that the ONLY thing > being transmitted is a single value, the "output". Now, most AGI projects > seek to perpetuate this detour, believing that everything can be contained > in a "probability". > > Everyone seems to believe in SOME sort of back propagation, and the > inability to make any sort of "simple" back propagation learn anything but > orders of magnitude slower than we learn suggests that there is something > more complex happening, like maybe more than one thing being propagated > back. > > There may be other signals going forward, like some indication of > plasticity to help operate the complex triage of determining WHICH neuron > should change when an apparently impossible set of signals arrives > somewhere. > > My own suspicions are that some of this may work by impedance, where some > outputs are "stiffer" than others. A plastic neuron might have a higher > output impedance, to be more easily affected by downstream loading. > However, it still seems that whatever happens at a synapse must travel > backwards to the inputs to be able to make an entire system self-organize. > > Until we can see and affect more, I don't see any prospect for useful > hybrid computation. This present inability to interact (other than voltage) > with a living neuron that is carrying multiple signals in various > directions seems to me to be an even bigger technical barrier than > scanning out entire connetomes, uploading and downloading, etc. > > The AGI folks here wave all this off, but until SOMEONE here builds > SOMETHING that is capable of the same sorts of instantaneous self-adapting > learning that we and every other living thing can do, AGI has no possible > future. I can't see any way around this barrier, and in the years that I > have been on this forum, apparently no one else has seen any way around > this barrier. > > For those who haven't immersed themselves in this sort of code, the > challenge is that learning must take place with unreliable inputs that are > themselves learning and adapting. What they "learn" is often superstitious > learning based on some "feature" (bug) in a signal that learning later > "corrects", only to help some downstream processes while hurting others. > Multiply this problem by ALL of the computations being performed doing > this, and nothing works UNLESS you introduce some sort of low-pass > "dampening" to keep single miss-learned computations from undoing the > entire process, which destroys the instantaneous learning that is sought - > or alternatively, downstream processes fall into a learning hole where they > fail to learn anything that is useful. It is hard to fully appreciate this > phenomenon until you have been there. At first this appears to be a simple > algorithmic malfunction, until you realize that the problem presently lacks > solution. > > From thousands of miles away and reading postings here, people appear to > be in various phases of "hacking" the above mentioned challenge. I suspect > that Ben is starting to realize that there are more fundamental issues. It > seems pretty obvious to me that this is from a shortfall in theory, and NOT > the result of any shortfall in "hacking". > > I have attempted to dissect this issue and suggest possible approaches on > past postings, but got no reasoned responses. It appears that to be active > in AGI that it is absolutely necessary to live in a sort of La-La land of > denial of this VERY basic issue. > > It seems to me that everyone here should *STOP* and deal with this > "little detail", because AGI has nowhere to go without it. > > Steve > ===================== > >> On Tue, Sep 24, 2013 at 10:29 PM, Steve Richfield < >> [email protected]> wrote: >> >>> Back around 1970 I heard about a researcher who had a couple hundred >>> pipette electrodes, all in parallel and affixed to a square plate, that was >>> pushed into a brain to read a couple hundred extracellular points in the >>> brain. Unfortunately, I don't remember enough about the reference to >>> exhibit it here. I remember hearing about this from William Calvin. >>> >>> Note that the equipment didn't then exist to record and analyze this >>> many parallel real-time inputs, so researchers had to switch their limited >>> monitoring equipment between electrodes. >>> >>> This would establish the rate of progress at approximately zero, and the >>> time to monitor the entire brain as infinite. >>> >>> Note that none of the past or present approaches to monitoring monitor >>> anything but voltage. There are various ions being bidirectionally moved >>> around to compute far more than can be seen by voltage alone, and these >>> remain beyond our "modern" technology to observe. >>> >>> Further, all present approaches to monitoring KILL some percentage of >>> the neurons that they seek to monitor. To scale, looking at an axon is a >>> lot like monitoring YOU by stabbing you with a telephone pole sized >>> electrode. Axons survive this better than people, but often all you monitor >>> is the last seconds of the death of the neuron. >>> >>> No, for these and other reasons I reject the idea that we are making ANY >>> significant progress in this area. >>> >>> Steve >>> ===================== >>> >>> On Tue, Sep 24, 2013 at 11:57 AM, tintner michael < >>> [email protected]> wrote: >>> >>>> "Out of a human brain's 100 billion neurons, researchers can presently >>>> monitor only about 200 at a time. This is sort of like trying to predict a >>>> presidential election by polling three people. 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