GLLN: Difference between revisions
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<big>'''G'''ativus '''L'''ow '''L'''evel '''N'''etwork</big> | |||
==General== | |||
Gativus Theory of Mind outlines '''GLLN''' - three low-level networks (associated with neural networks term), comprising of: | |||
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Some values, such as dendrites weights, can be defined as a content of internal containers, which would be changed or memorized as a process of learning. As soon as Gativus already introduced value containers, these learning data can be stored in those containers. However, there is no v-relation, but new capability of the cell. | Some values, such as dendrites weights, can be defined as a content of internal containers, which would be changed or memorized as a process of learning. As soon as Gativus already introduced value containers, these learning data can be stored in those containers. However, there is no v-relation, but new capability of the cell. | ||
Latest revision as of 00:32, 25 November 2024
Gativus Low Level Network
General
Gativus Theory of Mind outlines GLLN - three low-level networks (associated with neural networks term), comprising of:
| Cell information, actually coded by DNA, propagated and further creating cellular network; | Spikes between axons and dendrites, creating another network above cellular one; | Learning parameters, which would modify behavior of the cell. |
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Only spike network have the association of electrical conductivity of signals, and would be seemed as something similar to electrical networks relaying signals through the network. As soon as spikes signify the value of the cell’s function – they are considered as of the value and defined as v-relations in Gativus terms.
But at the same time, structural code – even though is not conducted in spike-like way, but still propagates the network. Therefore, structure data or S-relation, in Gativus terms, is considered to be a virtual delivery of structure construction data to the hosting environment, allowing growth virtual (digital) cell.
Some values, such as dendrites weights, can be defined as a content of internal containers, which would be changed or memorized as a process of learning. As soon as Gativus already introduced value containers, these learning data can be stored in those containers. However, there is no v-relation, but new capability of the cell.
