Márton graduated as an M.D. at Semmelweis University (SE), and with an M.Sc. in Physics at Eötvös Loránd University (ELTE), Budapest, where he also received his PhD.

His undergraduate work was about experimental measurements and analysis of complex spatial and temporal patterns in the brain microcirculation at the András Eke Lab, Institute of Clinical Experimental Research, SE. At ELTE, he started working in the Neural Information Processing Group (NIPG) of András Lőrincz, on information capacity of recurrent neural networks. He was also research project leader for a large epidemiology study of cancers in low dose of environmental radiation at the RAD Lab, and OKK OSKI.

His PhD research at NIPG continued on information exchange and capacity of neurons and microcircuits, with models ranging from molecular through biophysical to functional scope. He later worked with Krisztina Szalisznyó at the HUN-REN Wigner Research Centre for Physics of the Hungarian Academy of Sciences (Wigner RCP) on understanding the combined effect of dopamine and acetylcholine in the striatal principal cells, while he also visited several Berstein Center for Computational Neuroscience groups in this topic in Berlin and Freiburg.

He joined CSNL of Gergő Orbán in 2018, in Wigner RCP, and mentored several undergraduate students there. He has worked on understanding population code in the visual system of mice and monkeys with experimental partners from the Peyman Golshani Lab, UCLA, Los Angeles, and Wolf Singer Lab, ESI, Frankfurt. He developed analytical, bayesian probabilistic, and recurrent neural network modelling tools for topics of representational geometry, context integration with mutual inhibition, continual learning, and hierarchical computations.

Márton also has a 20 years of experience in applied research and development in various fields, such as clinical diagnosis, radiology, soil ecology, machine learning and statistics with partners in hospitals, firms, universities, environmental organizations around the globe.