How does the brain extract relevant visual features from the rich, dynamic visual input that typifies active exploration, and how does the neural representation of these features support visual navigation?
Amirali Vahid is a Postdoctoral Researcher at UC Santa Barbara. He received his PhD in Psychology from Technische Universität Dresden, Germany, as well as a BS and MS in Electrical Engineering from the University of Tehran, Iran.
He is interested in applying machine learning models and computer vision to biological and biomedical applications. In particular, his research focuses on determining how the mouse brain extracts relevant visual features from the rich, dynamic visual input that typifies active exploration, and to investigate how the neural representations of these features can support visual navigation. For this, he is performing data-driven statistical analyses and developing (deep) predictive models of brain activity based on visual input and several behavioral variables.
PhD in Psychology, 2021
TU Dresden, Germany
How does the brain extract relevant visual features from the rich, dynamic visual input that typifies active exploration, and how does the neural representation of these features support visual navigation?