Physics of Organs 2: Sensory Organs and Bioelectricity

Course manual 2017/2018

Course content

This course aims at explaining how the brain has evolved, by evolution, to process information efficiently. And indeed, efficient it is. The brain is 105 time more efficient that the most powerful computers. After an introduction to the basic principles of neuroscience, the focus will shift to the general “design” principles of brains.

Neuroscience research has exploded in the last decades. Due to the development of many new technologies, a large amount of new facts about synapses, neurons, and the brain as a whole have emerged. However, a principled framework to organize this knowledge is missing. After we have introduced the basics of how neurons and synapses work, we will discuss the book “Principles of Neural Design”. The authors of this book outline a set of organizing principles to explain the ways of neural design that allows the brain to compute so efficiently.

The course will not speculate about how the brain might work but will try to make sense of what is already known and discuss a coherent set of basic rules and apply them across various spatial and functional scales.

Study materials

Literature

• E.R. Kandel, J.H. Schwartz, T.M. Jessell, 'Principles of Neural Science', 4th Edition, McGraw-Hill, 2000, New York.

• Peter Sterling and Simon Laughlin, ‘Principles of Neural Design’, 1st Edition, The MIT Press, 2015, Cambridge

Objectives

After the course the student should be able to explain the basic principles of:

• Ion channels, membrane potentials, synaptic transmission, synaptic modulation.
• Electrophysiological recording techniques.
• Models for ion-channels, neurons and neuronal networks.
• The organization, function and neuronal coding mechanisms of the retina.
• Understanding of general "design principles" of the nervous system

After having finished this course successfully, the student should be able to understand and evaluate critically scientific papers dealing with electrical events in sensory organs and the brain, and should have a basic understanding of neural coding. 

Teaching methods

• Lecture
• Presentation/symposium
• Seminar
• Self-study

Lectures, working group, demonstrations.

Learning activities

Attendance

The programme does not have requirements concerning attendance (OER-B).

Additional requirements for this course:

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.uva.nl/plagiarism

Course structure

Timetable

The schedule for this course is published on DataNose.

Additional information

Recommended prior knowledge: a prerequisite for the course is knowledge and comprehension of:

• Mathematics:
  • Calculus (differentiation and integration of functions of more independent variables),
  • Vectorcalculus (inner- and outer product, gradient, divergence, rotation),
  • Solving linear differential equations,
  • Fourier series and transformation
• Physics: Basics in mechanics and electricity.

For students who do not have a Bsc Physics, a prerequisite for this course is that they have knowledge and comprehension of the appropriate chapters in Physics for Scientists & Engineers by D.C. Giancoli (In the 3rd edition: chapters 21 Electric Charge and Electric Field; ch. 22 Gauss’ Law; ch. 23 Electric Potential; ch. 24 Capacitance, Dielectrics, Electric Energy Storage; ch. 25 Electric Currents and Resistance; ch. 26 DC Circuits. In other editions the analog chapters).

Contact information

Coordinator

• M. Kamermans