Thesis supervisor: Gergő Orbán
Location of studies (in Hungarian): BME KTT Abbreviation of location of studies: BME
Description of the research topic:
Novel recording techniques provide novel opportunities for neuroscience: in contrast to traditional approaches, which attempted to discover the properties of the neural code through the characterization of the responses single neurons, recordings relying on dozens provide opportunities to understand the synergies between neurons. The complex, high-dimensional data provided by the experiments, however, require novel technologies for analysis. In this context, the tools of machine learning and artificial intelligence turn out to be helpful: exploiting the synergies between neurons, we can establish links between neuron population activity and thedecision of the animal. Building on collaborations with our partners at UCLA and Frankfurt we can investigate in awake behaving mice and monkeys how neural activity in visual cortices lead to decisions.The research topic yields training in cutting-edge methods in machine learning, including latent variable models, Bayesian inference, information theory. The trainee will learn the most advanced methodologies in computational neuroscience and and will access cutting-edge recordings in
28neuroscience (electrophysiology, optogenetics) through collaboration with world-leading laboratories. The project is part of a high-prestige HFSP collaboration.
Required language skills: English Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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