X-Message-Number: 25890 From: Date: Sun, 27 Mar 2005 02:19:02 EST Subject: Uploading technology (2.ii.0). Uploading technology (2.ii.0). Memory ask for some proteins produced in the body cell of neurons, a neuron model must so take into accout the gene regulation system responsible for that process. In the synaptic area, here are very complex biochemical reactions in a limited space with a limited number of molecules. To create a model for these phenomenon is somewhat tricky. Not all synapses are active, a dendrite spine may have no active receptor for fast ionic signals. Even more: It can shift from active to inactive and back in some hours. It may be created or disappear in the same time frame. Many people reject the uploading concept because they want a building back of the brain atom by atom. They don't realize that many synapses in their brain are created and destroyed every day. When dealing with such complex process with very many elements, there are 3 possible models: The boolean, the differential and the stochastic. Two more could be taken into account in the future: The positional and the quantum. The Boolean model assume that each process is either on or off, 0 or 1 and define a binary variable. The full system is then a boolean function readily implemented on a computer. This is interesting for the researcher to get a first look at interactions of very many variable. It is far too crude for any use in uploading. The next tool is the differential equation: Each chemical reaction is defined by the concentration of its reactants and products. The time derivative of these concentration define the reaction speed. The space derivative gives the concentration gradient from place to place. Differential models are continuous and address the point made by some uploading critics that computer models are discrete and don't take into account the continuous nature of biochemical reality. Unfortunately, in the real world, continuity is not always a good thing. Very often, a differential equation system predict more than one stable states. If the system get to one of them it remains here. In the dendrite spine yet, the number of molecules of a given species is often very limited, in the 100's range. Simple random fluctuation of this number may then dislodge the system of a stable state and send it to another. These models display so more stability than the real thing. The third model tool is the stochastic one, here each molecule of biochemical interest is counted and a chemical reaction is watched molecule by molecule. This take fully into account the random fluctuation of the molecule number. The problem is that the reactants concentration is assumed to be everywhere the same, there is no gradient in space and no or badly taken into account reaction speed with concentration. The last two systems are a model of the position of each atom and one computing from first quantum mechanical principles. They are so much computing hungry that there is no practical interest in them now. This could evolve if here was very large quantum computers someday. There are some intermediate models between boolean and differential and between differential and stochastic ones. For uploading purposes, the last are of particular interest. Starting from the differential model, the first idea is to add some random function so that here is no over stability when the number of molecules is in the 10 - 1000 range. This is the so called Lancevin's equation. Starting from the stochastic side, some gradient terms can be added, this is the Fokker-Planck equation. It may be demonstrated that Langevin's and Fokker-Planck are equivalent in modeling power. Depending on the case, it is possible to use one or another, the predicting power is the same. The most advanced system is the 1/(omega) model from van Kampen (*). Here, random effects are defined by a variable called omega. The omega function is expanded and all terms with the same power are summed up. When they are ordered in growing power, they form a Taylor serie for the omega function. That serie can be cut at any power in omega. At the zeroth power there is the differential and stochastic models. At first power there are the Langevin and Fokker-Planck equations. Higher powers give more precise models at the cost of more mathematical complexity and computing power. It seems the general model frame must be cast in that kind of system. Depending on each case and technology at hand, the expansion in omega will be cut at one or another power. In a first generation system, it will not be possible to go beyond Langevin and Fokker-Planck in most case. Because there has been many works on the differential side and far fever on the stochastic one, it seems best to start from the differential coast. The Langevin's equation is then better than the Fokker-Planck one for merly practical purposes. (*) van Kampen N.G. (1992) Stochastic Processes in Physics and Chemistry. North-Holland, Amsterdam. Yvan Bozzonetti. Content-Type: text/html; charset="US-ASCII" [ AUTOMATICALLY SKIPPING HTML ENCODING! ] Rate This Message: http://www.cryonet.org/cgi-bin/rate.cgi?msg=25890