Steve,
STEVE> Neurons appear to adjust their operation based at least in part on feedback - has their output been crucial to downstream neurons doing what they do? Are other neurons using the output at all? Indeed, my glaucoma theory is based on parts of the visual field NOT being useful enough to even bother monitoring - thereby causing circulation to shrink, possibly to the point of destruction, though I was in time to save my own right eye. I strongly suspect that certain events, e.g. downstream neurons sending back "messages" that they just barely made a particular decision, go upstream to reinforce or abandon neuron functionality. Now, suppose that a particular "disconnected" synapse "noticed" that its presynaptic neuron usually changed just before feedback arrived, so the presynaptic neuron probably conveys information needed to improve that feedback. Of course, whether the synapse should facilitate or inhibit operation would be based on what the neuron did to receive the feedback, whether the presynaptic neuron was "in phase" as needed, etc. SERGIO> I have a problem with this. If neurons time their response to short intervals, then all the information related to the event that caused the feedforward in the first place, would be gone by the time the feedback arrives. The neuron would have to have a memory of its own to determine which event that feedback corresponds to. Unless the feedback comes back soon enough that the original event is still there. That's why I was asking which feedback you were talking about. STEVE> A neuron's output represents (as nearly as is practical) something at a particular relative moment in time, e.g. 1/2 second ago, 2 seconds projected into the future, etc. To accomplish this, they must adjust the delays of their inputs so that everything comes together for the SAME moment in time. SERGIO> But how does a neuron "know" that? If a neuron receives two inputs, and both arrive at the same time slot, they can still correspond to different events. Conversely, if two inputs arrive at different times, they may still correspond to the same event. The neuron can't tell. Sergio From: Steve Richfield [mailto:[email protected]] Sent: Wednesday, August 15, 2012 3:32 PM To: AGI Subject: Re: [agi] A wrong presumption? Sergio, On Wed, Aug 15, 2012 at 12:33 PM, Sergio Pissanetzky <[email protected]> wrote: STEVE> I suspect that (neurons) need the correct delay as needed to preserve time coherence. A neuron's output represents (as nearly as is practical) something at a particular relative moment in time, e.g. 1/2 second ago, 2 seconds projected into the future, etc. To accomplish this, they must adjust the delays of their inputs so that everything comes together for the SAME moment in time. SERGIO> We are on to something on this. This provides an additional equation for the problem of understanding how the connections work. One equation is that they must be of minimum length to satisfy entropy, the other, they must adjust for synchronization. Min length could also be obtained if the neurons were able to move in response to "pull" from active connections, but neuroscientists don't like that idea. The two equations make for a stronger system of constraints to guess what they *should* do to satisfy both constraints, and also their biology. Note that this sort of time adjustment in effect makes relative time to be just another dimension, with operands being selected by finding the appropriate connections. STEVE> Note that time coherence would be AUTOMATICALLY adjusted if neurons selected inputs that changed just BEFORE feedback information arrived, as these inputs would contain properly timed information to produce correct outputs. SERGIO> I don't understand this, could you explain some more? What feedback? Neurons appear to adjust their operation based at least in part on feedback - has their output been crucial to downstream neurons doing what they do? Are other neurons using the output at all? Indeed, my glaucoma theory is based on parts of the visual field NOT being useful enough to even bother monitoring - thereby causing circulation to shrink, possibly to the point of destruction, though I was in time to save my own right eye. I strongly suspect that certain events, e.g. downstream neurons sending back "messages" that they just barely made a particular decision, go upstream to reinforce or abandon neuron functionality. Now, suppose that a particular "disconnected" synapse "noticed" that its presynaptic neuron usually changed just before feedback arrived, so the presynaptic neuron probably conveys information needed to improve that feedback. Of course, whether the synapse should facilitate or inhibit operation would be based on what the neuron did to receive the feedback, whether the presynaptic neuron was "in phase" as needed, etc. STEVE> I suspect that those numbers came from Calvin's own observations and calculations. I could probably track him down and ask him. SERGIO> If you would please. The buzz number I hear for the count of synapses per neuron is 10,000, but I never heard that only 200 of them were active. If you talk to him, would you also please ask him for an update on all he knows about connections in general. Maybe he published something. OK, I'll see what I can do. STEVE> ...instead of referring to a location for an operand, the location pointed anywhere in memory to the operand. SERGIO> That can be done in software using references, references to references, etc. Still, those references had to refer to SOMETHING, and if you don't like the structures available in your choice of languages, then you are pretty much screwed. Steve STEVE> I suspect that people think in "layers" and "columns" in part because they correspond to programming structures like arrays, which are in turn an artifact of the crude processors we now use. I suspect that we need to shed such baggage and stop being thought-constrained by the CPUs we now use. SERGIO> Of course. LAST MINUTE> In case you don't read the blog, I was just pointed to theoretical neuroscientist Karl Friston <http://www.fil.ion.ucl.ac.uk/%7Ekarl/#_Free-energy_principle> . See also Wikipedia. This is exactly what we need. He seems to know a great deal about neural connections, and he's saying many of the same things you and I are saying. Sergio From: Steve Richfield [mailto:[email protected]] Sent: Monday, August 13, 2012 4:15 PM To: AGI Subject: Re: [agi] A wrong presumption? Sergio, On Mon, Aug 13, 2012 at 1:24 PM, Sergio Pissanetzky <[email protected]> wrote: Any thoughts? You have flooded me with thoughts! That was my goal - to kick people's thinking out of their present ruts. I have been thinking for months now, why is it that neurons need so many synapses? And I know that neurons need to establish short connections in order to minimize energy used in the storage of information, which in turn results in entropy decrease and self-organization of the information (more on this in my Schroedinger's cat post). I suspect that they need the correct delay as needed to preserve time coherence. A neuron's output represents (as nearly as is practical) something at a particular relative moment in time, e.g. 1/2 second ago, 2 seconds projected into the future, etc. To accomplish this, they must adjust the delays of their inputs so that everything comes together for the SAME moment in time. This is very parallel to the techniques used in "pipelined" supercomputers, where delays are carefully adjusted. When CRAY computers first came out, everyone noticed the many excessively long wires in them. Their lengths corresponded to the number of clock cycles it took signals to travel from one end to the other, and each wire had to be the correct length or the computer wouldn't work. I suspect that neurons are much the same. Note that time coherence would be AUTOMATICALLY adjusted if neurons selected inputs that changed just BEFORE feedback information arrived, as these inputs would contain properly timed information to produce correct outputs. But to make short connections, the neurons need some way to compare them, so they have to make many connections, test them by sending a signal and kill the ones that are too slow (which would go very well with Hebbian learning). That's why they start from 50,000 (it keeps growing, I thought it was 10,000) and end up with 200. What do you think? Where did you get those numbers? Would you please have a reference to a publication? This is important information for my work. I got those numbers from William Calvin during discussions when I worked for him ~40 years ago at the U.W. Department of Neurological Surgery, before he became a famous neuroscience author. I suspect that those numbers came from Calvin's own observations and calculations. I could probably track him down and ask him. Note that MOST of what is "known" in the neurosciences does NOT appear in print!!! They have their own strange sort of "ethics" that is almost the exact opposite of Physics. Physics is a battle of competing models, while the neurosciences punish those who advance models before they are "proven", hence, no models. However, when you get one of these guys into a long off-the-record conversation and start talking about what they have actually seen but can't prove (remember, neuroscience IS the study of irreproducable results, because you can never exactly repeat an experiment), you start to realize that things are NOTHING like you read in the literature. My own view of this is: Without models you can't advance potentially useful hypotheses, and without these hypotheses you can't practice the Scientific Method. Hence, the neurosciences are not (yet) a "science". My advice to funding agencies both public and private is not to fund ANYTHING that lacks a comprehensive tentative model that was used to form the hypothesis being tested. Selforg and learning are different. Learning is acquiring info. Selforg is removing uncertainty from info you already have acquired. However, in another sense, selforg is indeed learning because if derives new facts - the self-organized structures - from known facts - that what you have just learned. This is inference, and that's why I call it Emergent Inference, or it could also be Self-organizing Inference. You may also note that an entity that can represent knowledge and has an inference is known as a mathematical logic, so I am proposing EI as a new math logic. "Modern" mathematical notation and computer languages have lose some of their history. Earlier computers with architectures far more advanced than PCs (there were LOTS of these) had "indirect addressing", where instead of referring to a location for an operand, the location pointed anywhere in memory to the operand. Then, some computers like the GE/Honeywell 600/6000 series mainframes allowed the memory addresses to themselves contain a flag to perform additional levels of indirect addressing, so an instruction might go from one location to another in search of its operand. With these sorts of architectures, operands did NOT have to be constrained to particular structures or arrays. This was one of the powerful pieces at the heart of the early MULTICS and other systems. I suspect that people think in "layers" and "columns" in part because they correspond to programming structures like arrays, which are in turn an artifact of the crude processors we now use. I suspect that we need to shed such baggage and stop being thought-constrained by the CPUs we now use. Our mathematical notation and our CPUs need to be able to refer to ANYTHING that provides useful and timely input. 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