Course leader: Sadegh Nabavi
Graduate school: Faculty of Health
Graduate program: BIO
Course fee: 3,600.00 DKK
Status: Course is finished
Semester: Fall 2023
Application deadline: 22/10/2023
Cancellation deadline: 06/11/2023
Course type: Classroom teaching
Start date: 20/11/2023
Administrator: Anne Fabricius
The course B322/01 Principles of Neural Organization is being offered by the Graduate School of Health, Aarhus University, 2023.
Criteria for participation: University degree in medicine, dentistry, nursing, or Master’s degree in other fields and/or postgraduate research fellows (PhD students and research-year medical students).
Requirements for participation: A grasp of the materials covered in the course Neurobiology 555152U006 should be sufficient. This is equivalent to a 10 ECTS in basic neurobiology at the level provided by basic textbooks such as Principles of Neurobiology by Liqun Luo; From Neuron to Brain by John G. Nicholls et al.; Neuroscience- Exploring the Brain by Mark Bear et al.; Principles of Neural Science by Eric Kandel et al.
Aim: This course is interested in the following question: How can the brain be far smarter than a supercomputer yet consumes 100,000-fold less space and energy? Both brain and computer obey mathematical and physical laws and both deal with receiving, sending, processing, storing, and retrieving information.
First, we will discuss why animals need brain. To provide the perspective, we discuss how brainless organisms such as bacteria and protozoa solve their problems in a constantly changing environments, what limitation they face which can be solved only by a brain.
Then we ask why larger brains have been favored by the evolution. We discuss the advantages that organisms with larger brains have over worms and flies.
For the rest of the lecture, we will discuss what constrains a large brain faces and how those constrains shape the neural design. We will argue that the (for now) unmatched computational power and efficiency of the brain lies in the principles of neural designs.
To understand the efficiency of the brain, first we will introduce students to the basic principles of information theory, so they can appreciate how the brain cleverly exploits these principles to compute most efficiently given limited in space and energy.
We will use the visual system as the example of a clever design for achieving the highest computational power within the limits of space and energy imposed by the laws of physics.
Finally, we will show that learning is a deep principle of neural design. As such, it is subject to the same constraints discussed previously. Regarding the encoding of new information and memories, we will discuss how synapses serve to conserve space, time, and energy and what are the molecular designs of short-term and long-term memories. We will conclude the section by pointing out to the downsides to design for learning, including addiction (in its broadest definition), depression, …
The course is supplemented with an introduction to the techniques that are (mainly) exclusive to neuroscience. This includes optogenetics, in vitro and in vivo electrophysiology, calcium imaging from behaving animals, and animal behavior. Students will visit the labs performing these experiments and will see these techniques in action.
Learning outcomes: By the end of the course, the student should be able to:
- Have a good grasp of the limitations and advantages of small and large nervous systems.
- Know the constrains that nervous systems face and how they evolved within these constrains.
- Perceive brain design and function as an information processing entity.
- Describe the basic principles that the brain uses to achieve a superior computational power while keeping the energy consumption in check.
- Identify some fundamental principles shared by all systems and circuits within the brain.
- Analyze, review and constructively criticize papers in the relevant fields.
Workload: The full workload of the course is expected to be 50 hours. This includes a total of 15 hours of teaching materials, 10 hours of take-home assignment and exam, and 10 hours or preparation for the lectures.
- Lives of the brainless: A microbe’ s memory and its limitation; Protozoa: bigger and faster but still brainless.
- Worm with tiny brain: Finding warmth, food, and mates; Some design aspects of this tiny brain.
- How bigger brains are organized: Design constraints; Three principles of neural design.
- Sending out and receiving signals: Wireless and with wire; Processing and storage of information; Correcting errors.
- Principles of information theory: What is information; Measuring neural information. Pricing neural information.
- How protein molecules transmit and process information: Computation by the kinetics of chemical binding.
- What makes a protein circuit efficient: Energy efficiency of protein devices.
- Information processing by electrical circuits: Constraints on information processing by circuits of ion channels.
- Synapses and local computation; Dendrites expand a neuron’s information capacity.
- How visual system optimize the capture, transmission, and processing of visual information.
- How general are the circuits for cortical processing?
- Efficient wiring at large scales: “Law” of conservation of metabolic energy.
- Principles for the design of learning: Cellular design for efficient information storage; How the design of memory couples to the design of learning; Designs to optimize choice and information storage; Downsides to the design for learning.
Instructors: Sadegh Nabavi, Nathalie Van Den Berge, Caroline Cristiano Real Gregório, Andrea Moreno, Noemie Mermet-Jore
Venue: Aarhus University, Aarhus (other)
Participation in the course is without cost for:
- PhD students, Health Research Year students from Aarhus University
- PhD students enrolled at partner universities of the Nordoc collaboration
- PhD students from other institutions in the open market agreement for PhD courses
- 20 November 2023 09:00 - 12:00
- 21 November 2023 09:00 - 12:00
- 22 November 2023 09:00 - 12:00
- 23 November 2023 13:00 - 16:00
- 24 November 2023 09:00 - 12:00