Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning


  • Aleksejs Zorins Rezeknes Augstskola (LV)
  • Peteris Grabusts Rezeknes Augstskola (LV)



Artificial Neural Networks, Brain Networks, Artificial Neural Network Learning Algorithms


There are numerous applications of Artificial Neural Networks (ANN) at the present time and there are different learning algorithms, topologies, hybrid methods etc. It is strongly believed that ANN is built using human brain’s functioning principles but still ANN is very primitive and tricky way for real problem solving. In the recent years modern neurophysiology advanced to a big extent in understanding human brain functions and structure, however, there is a lack of this knowledge application to real ANN learning algorithms. Each learning algorithm and each network topology should be carefully developed to solve more or less complex problem in real life. One may say that almost each serious application requires its own network topology, algorithm and data pre-processing. This article presents a survey of several ways to improve ANN learning possibilities according to human brain structure and functioning, especially one example of this concept – neuroplasticity – automatic adaptation of ANN topology to problem domain.

Supporting Agencies
Artificial Neural Networks, Brain Networks, Artificial Neural Network Learning Algorithms


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How to Cite

A. Zorins and P. Grabusts, “Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning”, ETR, vol. 3, pp. 228–231, Jun. 2015, doi: 10.17770/etr2015vol3.165.