Year: 2024
Type: article
Abstract: In this paper, the nervous system is composed of a combination of common structure components, indicating that the hierarchical structure may lead to a great evolution to cognitive function extending eating behavior. The common component (basic unit) is a set that combines neural circuits to cylindrical forms, and the basic unit has an affinity with the structure of pillars, barrels, or blobs in neuroscience. Use multiple input and output to achieve a variable value function. By connecting a basic unit in a hierarchically, it can be done more complex time -series data process and can be add almost unlimited. Time series data is saved by repeating judgment and action. Save data includes not only food and other objects, but also surrounding situations. If the new situation resembles the elements of the time that is already recorded, the approximation element becomes active and the activation spreads throughout the past time series data. Time series data is constantly updated with processing. As example, reality turns into a new cause, and prediction turns into a new reality. If the two areas are often active at the same time, even if only one area becomes active, the corresponding areas will also change. This learning function corresponds to Hebb's law in neuroscience. This feature is the basis of imitation and conditional learning, enabling people to communicate with fellows and enabling collective actions. This ability is an indispensable ability to build a society and is the beginning of a language. Languages can express events not only in the past or future events, but also in places where sensory organs cannot reach. It has been inherited for generations as a culture. In this paper, a new layer is proposed at the top of the neural network that has evolved from eating behavior. The processing of the new layer is asynchronous with the lower layer, but it performs a complementary process and enables event communication with friends. This ability is the basics of language. The process of forming common knowledge exchanging questions and teachings between two neural networks is shown a simple example.
Author: Seisuke Yanagawa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405008986