The goal of our group is to examine and compare the learning processes of biological and artificial systems. Learning is an essential condition for the proper environmental adaptation of biological systems, and at the same time it is also one of the main pillars of human cognition. The learning of artificial systems, the so-called “machine learning”, is currently experiencing a renaissance and can greatly facilitate technical development and the processing and interpretation of the results of various research areas. In our experiments, we examine the learning processes of biological systems in traditional behavioral paradigms and with the help of electrophysiological tools, while the examination of artificial systems takes place in a computer environment. We process the data obtained during the examination of biological systems with the tools of higher mathematics and statistics, and with the help of the obtained results we develop mathematical models, which, when implemented in a computer environment, can themselves implement an artificial learning algorithm. We consider it important that the observations made during the examination of biological and artificial systems and the relationships discovered can mutually promote the development of these two areas.
leader: Dr. Péczely László
Prof. Dr. Lénárd László (professor emeritus, advisor)
Dr. Kenyeresné Dr. Kállai Veronika
Marosné Dr. Berta Beáta
Petykó Zoltán
Szabó Ádám
Godova Gábor
Takács Tícián
Tóth Dominik
Fogasi Pálma
Dr. Dusa Daniella Anna (external collaborator)
Kisander Zsolt (external collaborator)