The aim of this course is to give an overview of the field of Behavioral Neuroscience and introduce the major experimental techniques and animal model systems in Behavioral Neuroscience. Students will apply modern measurement and data-analysis methods to quantify behaviour of animals and the nervous system.
The course gives an academic basis for studying animal behaviour, biorobotics and behavioural neuroscience, both in animal experiments and using modelling approaches.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- Provide competences to apply quantitative analysis of complex data.
- Provide the competence to plan and control work and development of projects, to initiate and succeed in professional collaboration and to be responsible for own professional development and specialization
- Provide skills in planning and conducting independent research projects using techniques in behavioral neuroscience research, critical review of the literature, written and oral communication of biological research.
- Provide knowledge and understanding of fundamental and recent topics within the field of behavioural neuroscience.
The following main topics are contained in the course:
- The invertebrate and vertebrate nervous system
- The function and evolution of the brain
- Elemental functions of real neural networks function, e.g. Central Pattern Generators
- How the brain and body interact to drive behavior
- Sensorimotor integration and decision making
- Effect of drugs on behavior. Psychopharmacology.
- Quantitative analysis of complex data.
- Experimental labs that will be presented as a poster session
Entry requirements. The curriculum from a bachelor in biology, psychology, engineering or similar must be known.
The learning objective of the course is that the student demonstrates the ability to:
- Design and conduct (electrophysiological and) behavioral experiments.
- Be able to describe the experiments and results in a satisfactory academic language.
- Analyze activity in real and simulated neural networks
- Understand basic mechanisms underlying sensorimotor learning and decision making.
- Plan, conduct and report an independent project containing laboratory work.