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Part of the project: Understanding learning of mice in social interaction

Lead PIs: ​Lars Lewejohann, Christa Thöne-Reineke, Olaf Hellwich, Henning Sprekeler

Description:

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.

Duties:

  • 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

 

Requirements:

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

Application deadline: 29 November 2019 23:59 CET