Doctoral Project: Mice Interaction Modeling from Multi-View Video
Description of the research project
In this project, we investigate the interaction between learning and social perception. If there is an interplay between emotions and cognition in the service of efficient information processing and communication, emotional behavior may represent a shortcut to bypass more costly cognitive processes in the reduction of dimensionality.
The mouse serves as an animal model in the proposed project in order to gain an understanding of how social responsiveness is facilitating learning and how it is modulated by priors as well as emotions. On the analytic side, the facial expressions of emotions will be closely analyzed in mice and their effects on learning behavior as well as the emotional state of conspecifics will be investigated. Moreover, we will examine learning strategies in socially housed mice and how emotional signaling is involved in social learning.
On the synthetic side, we aim to automatically monitor learning behavior in social interaction of group-housed mice and to model their behavior. Reinforcement learners that should show the same behavior as the mice are developed and we will investigate whether the reinforcement learners can exchange learned behavior at certain periods in time during the solution of the task at hand.
The data derived in this project will serve as a basis for an in-depth analysis of if and how our model organism exhibits intelligent behavior. By comparing mice to other animal species, human subjects, and artificial agents we will gain a better understanding on intelligence and especially on different grades of intelligent behavior.
Description of the doctoral project
In this project, the learning behavior of mice will be observed using a network of synchronous rgb-d video cameras. Video analysis will result in instantiation of a social interaction model for a group of mice. In close cooperation with behavioral biology, interaction models will be used as prior knowledge in video interpretation, and video analysis will be used to refine interaction models, both in an iterative process. The learning behavior of the animals in the social setting will be analyzed and synthesized using reinforcement learning methods.
The focus will be on three classes of social learning: imitation learning, learning by observation and curriculum learning all of them involving rewards.
- Conducting experimental research in computer vision
- Analysis of video data to generate algorithms for computer vision
- Automated evaluation of behavior
- Modeling of behavior using reinforcement learning
- Interaction within the SCIoI cluster of excellence
- Compilation of the results for presentations, project reports, and publications
Applications should include: motivation letter, curriculum vitae, transcripts of records (for both BSc and MSc + doctoral degree if applicaple), copies of degree certificates (BSc, MSc), abstracts of Bachelor-, Master-thesis, e.g. doctoral thesis, list of publications and one selected manuscript (if applicable), two names of qualified persons who are willing to provide references, and any documents candidates feel may help us assess their competence.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. Applications are also expressly welcomed from suitably qualified persons seeking to be entered as “gender diverse” in the public register. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.
Please send copies only. Original documents will not be returned.
Applicants must hold a Diploma/Master’s degree in a highly quantitative field (e.g., mathematics, physics, computer science, engineering). The ideal candidate has a background in machine learning, with expertise in both reinforcements learning and computer vision.
- Excellent mathematical skills
- In depth programming skills (C/C++, Python, Matlab)
- Very good command of English, both written and spoken
- Strong interest in visual perception and machine learning
- A keen interest in understanding intelligence and the strong communicative skills required for interdisciplinary research
- Conscientious work approach, flexibility, good time management, and ability to work in a team