Modeling of stochastic brain function in artificial intelligence

Аннотация

Objectives – research of stochastic brain function in respect to creation of artificial intelligence. 

Material and methods. Mathematical modeling principles were used for simulation of brain functioning in a stochastic mode.

Results. Two types of brain activity were considered: determinated type, usually modeled using the perceptron, and stochastic type. It is shown, that stochastic brain function modeling is the necessary condition for AI to become capable of creativity, generation of new knowledge. Mathematical modeling of a neural network of the cerebral cortex, consisting of the set of the cyclic neuronal circuits (memory units), was performed for the stochastic mode of brain functioning. Models of "two-dimensional" and "one-dimensional" brain were analyzed. The pattern of excitation in memory units was calculated in the "one-dimensional" brain model.

Conclusion. Relying on the knowledge of the stochastic mode of brain function, a way of creation of AI can be offered. -rhythm of a patient is a recommended focus of the therapist's attention in diagnostics and treatment of brain disorders. It was noted, that the alpha wave amplitude and frequency could indicate the cognitive, creative and intuitive abilities of a person.

Conflict of Interest: nothing to disclose.

Список литературы

1. Guyton AC, Hall JE. Textbook of medical physiology. Elsevier Inc., New York, USA, 2006.

2. Volobuev AN, Romanchuk PI, Bulgakova SV. Alzheimer’s disease as the cerebral cortex disorder. Science & Innovations in Medicine. 2019;4(2):16–20. (In Russ.) doi: 10.35693/2500-1388-2019-4-2-16-20.

3. Rosenblatt F. The perceptron, a probabilistic model for information storage and organization in the brain. Psych. Rev. 1958;65:386–408.

4. Grechko LG, Sugakov VI, Tomasevich OF, Fedorchenko AM. Problems in theoretical physics. M., 1972. (InRuss.).

5. Levitov LS, Shitov AV. Green's function. Problems and solutions. M.: FIZMATLIT, 2003. (In Russ.).

6. Skopenkov MB, Pakharev AA, Ustinov AV. Through resistivity network. Matematicheskoe prosveshchenie.2014;3(18):33–65. (In Russ.).

7. Spitzer F. Principles of Random Walk. Princeton, New Jersey, 1964.

8. Fikhtengolts GM. Rate differential and integral calculus M.: Nauka, 1966. (In Russ.).

Для цитирования

Volobuev AN, Pyatin VF, Romanchuk NP, Romanchuk PI, Bulgakova SV. Modeling of stochastic brain function in artificial intelligenceScience & Innovations in Medicine. 2019;4(3): 8-14. doi: 10.35693/2500-1388-2019-4-3-8-14

Request

Send an online application form to the publication

Send