SC8 Neural Biophysical Properties and Neural Computation


The remarkable abilities of our brain rely, at the core, on the coordinated activity of neurons in local networks. It is commonly assumed that functional computations are shaped by the way neurons are connected in networks. Properties of the neurons themselves, however, can vary widely and are specialised to contribute to computational function. In this course we will look at basics of neuronal processing from a combined biological-mathematical perspective. We will introduce how electrical activity in neurons is generated and how biophysical processes modulate activity. Cell-intrinsic properties determine which information is passed on by a cell. Cell-intrinsic properties also influence how neurons synchronise in a network. Moreover, not only functional considerations, but also evolutionary constraints determine neural design. Variable temperatures, limited energetic resources, restricted space - all these parameters may disturb physiological processes and interfere with neural computation. At the end of the course you will hopefully be more aware of the fact that our brain is not only a fascinating organ, but is also made of individual components that have been carefully designed in evolution to ensure robust neuronal function.


Get familiar with basic principles of action potential generation and synaptic transmission and their mathematical description. Raise your awareness for the fact that intrinsic neuronal properties do matter in neural computation as well as for the fact that evolutionary constraints most likely had a say in the design of nervous systems.


Christoph Koch (1999), Biophysics of Computation: Information processing in single neurons. Oxford University Press. (Textbook)

Peter Sterling and Simon Laughlin (2015), Principles of Neural Design. The MIT Press. (Textbook)

Eugene M. Izhikevich (2007), Dynamical systems in neuroscience: The geometry of excitability and bursting. The MIT Press. (Textbook, first chapters only)

Course location

Lecture Room 2

Course requirements


Instructor information.

Instructor's name

Susanne Schreiber


cf. website


Susanne Schreiber heads the research group of Computational Neurophysiology at Humboldt-Universität zu Berlin. Coming from biophysics, she entered the field of computational neuroscience during her Diploma project, which she did at the University of Cambridge, UK, in the lab of Simon Laughlin. Susanne spent the first half of her PhD as a Sloan-Swartz Fellow at the Salk Institute for Biological Studies in California in the lab of Terrence Sejnowski, to then return to Berlin to the lab of Andreas Herz. The Bernstein Award 2008 allowed her to start her own junior research group at Humboldt Universität, where she meanwhile is a full professor. Susanne’s interests are centered around the properties of individual neurons and their influence on neural coding as well as the question how neural systems were designed to achieve robustness in view of evolutionary constraints.