- Lecturer: Felix Effenberger
- Date: Tuesday 13.30 - 15.00
- Room: MPI MiS A02
- Language: English
- Target audience: MSc students, PhD students, Postdocs
- Content (Keywords): spiking neuron models, simulation software, neural networks, synaptic plasticity, neural coding, information theory
- Prerequisites: Basic knowledge in analysis and linear algebra, probability and statistics
This lecture will discuss methods commonly used for the simulation and mathematical analysis of networks of spiking neurons, the basic building block of most nervous systems. No previous knowledge in neuroscience will be assumed, so this lecture is well-suited for the neuroscientifically interested but not yet well-adept.
We will take a look at the mathematical properties of single cell models and networks thereof as well as software suited for the simulation of single cells and neuronal networks in silico (NEST, NEURON, BRIAN).
Furthermore, we investigate upon learning in such networks (by means of dynamic synapses) and develop mathematical tools for the qualitative and quantitative analysis of information coding and transfer in such networks.
A preliminary table of contents is the following:
- Elements of neuronal networks: neurons and synapses
- Single cell models: from Hodgkin-Huxley to integrate and fire
- Population models
- Neuronal dynamics & coding
- Synaptic plasticity
- Information theoretic analysis of spiking neural networks