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DYNAMIC POPULATION ENCODERS

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Dynamic population encoders author: Tofara moyo The human brain is an amazing machine. It is composed of billions of neurons , exchanging information (and adapting the ways they exchange information) in order to learn and process information that the individual receives. We know that neurons have a binary , on or off nature, and it is in the pattern of neurons firing and not firing that consciousness emerges. We assume that population codes are the way that the brain chooses to represent concepts rather than other theories such as grandmother neurons. The population code theory is that concepts are represented in the brain by groups of firing neurons, rather than individual neurons , which if it was individual neurons, would imply the hypothesis that grandmother cells are the way the brain encodes information. What this means is when you think of the concept “cat”. Each time you think it roughly the same group of neurons will fire in a specific part of the brain. Also when you perceiv...
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COVID 19 Detection through voice print

We have an algorithm that detects if someone has flue like symptoms is in the vicinity, just by the tone in his speech. We are not sure yet if we can accurately differentiate between Covid type flue like symptoms and those from another cause. Regardless it should be clear that the percentage of covid infected people is higher within the set of people with flu like symptoms than in the general public. So this should still serve useful. At its broadest then it protects the user from both flu and covid . This could be implemented in an android app that is constantly on on the smart phone, even in the users pocket. The moment it hears through the earpiece someone speak it will use adapted bio metric technology to check if they have flu like symptoms and set of an alarm or vibrate if they do. That gives you the chance to avoid close contact with the indicated person, and perhaps urge them to go for more thorough testing. This application can be used by the authorities to monitor everyone...
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Dynamic Weight Matrix Neural Network

I have a neural netwok that takes in a random noise vector and maps it explicitly to an image. If i was to do this with one training example , it would memorize the example. if i increase the number of examples, it starts to unlearn the previous examples in order to learn the new ones. Think of the equation of a straight line. y = mx + c where y is the image, x is the random noise vector, m is the weight matrix and c is the bias term so with the first example we can change m the weight matrix, through training, till we fit x to y if we took another training pair we would need to change m again and so unlearn the first example. we will have to do it another way thsi will involve having two weight matrices. W and a soft copy of W called W2 W will b the template weight matrix. and W2 will be made by swapping around the indeces without affecting their memory allocation as this is a soft copy of W What this will mean is that on the graph we have a family of straight lines, whose gradie...
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Patent pre publication

I would like to publish concepts concerning fMRI software to increase detection accuracy to give it a better temporl reolution as ell s a new type of imging technique We could have a neuron that leves a clear marker such as a magnetic pulse as soon as it fires as the basis of a new imaging technique..This would be done similar to the way the people at this site https://www.engadget.com/2018/05/10/ai-new-3d-model-predictive-human-cells-biology/?fbclid=IwAR1gLk5ANQBDT-h0rYqb3H2NovJEGfr0ianRqbu2hllhm56X9GcXZzIubHo edited the genes of cells to release phlourescent markers....also why would we not be able to have an fMRI based software that detects only up to a certain concentration of oxygen in an area ,also only when it occurs in an area that has the growth of the oxygen level that follows the pattern that the curve associated with that found in the build up to the neuron receiving a full supply of oxygen?
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Processing information with motifs

The axiom of identity states that For any "thing" (A = A) & (!A ! = A), which means that a thing is itself and not what its not. this is ubiquitous about nature and as we will see also expresses itself among the distribution of natural things as patterns. it is important for us in the field of AI because it introduces a complement to the hebbian factor which is that that things seemingly not related actually correlate. if A = A represents the hebbian factor (a thing is coincident with itself) then the other part would be !A != A (a thing is not coincident with what its not). But we see some more information than that last present in the complement. It fully contains all those elements found in A=A within its structure, i.e there are two A's in its expression as well as an equal sign. This should lead us to need to express it more appropriately as "a thing is coincident with those things that are not coincident with itself" . but that does not make full sens...
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Representing knowledge as a partitioning on a single set of words

Imagine a tessellation made from equilateral triangle shaped tiles. We could add more and more tiles besides each other till we reach infinity. Now we would like to model the following properties of language using a tessellation. We want for different shapes of tiles to be fitted together in our tessellation, where different tiles contain different information in the form of sentences.This will be useful in a number of ways. The process of communication will be equivalent to selecting a particular tile from this space.So could the process of acquiring commands to give to an agent responsible for making actions. To select a tile we may choose to select an Nth term in this tessellation according to some sorting algorithm that sorts this space of tessellations. We will also depart from having these tiles tessellate a Euclidean space, but have it be a non-Euclidean manifold, in order for the shapes to fit as we would like. In our tessellation procedure, we may begin with one sentence and a...
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Iterative Addressing in a Virtual Memory for Conceptualisation

If we could build a lens , where we sample from spaces itteratively in order to generate a particular, we may have a model that simplifies the process. And aids in keeping the selection process a series of linear transformations. For example, if we would like to generate an entire video, we could train an algorithm , perhaps with a VAE to selct the priinciple components of videos and then sample from this distribution. It will be up to the system to learn these components on its own, but the axes of this distribution are not objects in their own right. A point on a manifold is selected that is defined by the information it contains, which is a complete instance of a video. The system proposed described separates features and creates a manifold for each. Each new manifold will be linked by a parameter for selecting from that space. For this paper I present these manifolds as euclidean spaces that are positioned within a cascade. What this means is that , during the process of generation...
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