Computational Neuroscience
Teaching hours and weekly schedule
This is a 2nd semester elective course of about 2.5 weeks that corresponds to 4 ECTs and 31 total hours of teaching including student presentations.
Co-ordinators
- Efstratios K. Kosmidis
Assistant Professor of Neurophysiology, Laboratory of Physiology, Department of Medicine, Aristotle University of Thessaloniki
- Vassilis Cutsuridis
Affiliated Researcher, FORTH, Senior Lecturer, School of Computer Science, University of Lincoln, UK
Description:
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing neural function from the single neuron to the neural network level. Specific topics will cover synaptic plasticity, learning and memory in the brain. We will make use of C++/Matlab/NEURON demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is aimed to students of all ages eager to learn how the brain processes information.
Course Overview
Computational neuroscience is the intersection of neurophysiology, neuroanatomy, mathematical modeling and computer science. Its primary target is to describe how the brain “computes” by simplifying neuronal biology to a set of equations. As most branches of Science, it contains elements of Philosophy and Art. Emphasis will be given on mathematical descriptions and computational techniques used to study and understand neurons and network of neurons. Weekly assignments will allow students to learn through direct experience. The course will provide a glimpse of this exciting field aiming to motivate the young mind by covering the following topics:
- Mathematical modeling in neurophysiology. An introduction.
- Classical, membrane potential theory
- Electrical analogue of the cell membrane – The Lapique model (Leaky Integrate and Fire)
- Action potential theory. The Hodgkin – Huxley model
- Cable theory, multi-compartmental single neuron model
- Models of synaptic transmission (AMPA, NMDA, GABAA, GABAB)
- Models of synaptic plasticity (LTP/LTD, STDP, Hebbian, Delta rule, backpropagation, etc)
- Models of neural networks (feedforward, feedback, competitive, etc)
- Computational tools (NEURON and MATLAB)
Skills and Learning outcomes
- Understand and appreciate the integral role of computational techniques and concepts in neuroscience. Study and critique review papers relating the use of computational techniques to the broader development of theories and experimental methods in neuroscience.
- Understand basic concepts for ion channel and single cell modeling, possibly including:
- I-V curves, the Hodgkin-Huxley model of action potential generation, and simple kinetic models of ion channels, integrate-and-fire approximation
- Mathematical representations of conductances, currents, and their relationship to dynamic changes in nerve cell behavior
- Use these models and associated methods to predict qualitative functional outcomes or quantitative state changes when varying parameters or changing structural properties of the models.
- Use one or more software tool that facilitates the calculation of such predictions.
Titles of lectures and names of the lecturers
| Computational Neuroscience | Lecturers |
| Co-ordinators: Stratos Kosmidis, Vasilis Cutsuridis | |
| Math foundations | Ioannis Dellis (Leeds) |
| Neural data analysis | Ioannis Dellis |
| Intro to neyrophys | Stratos Kosmidis (AUTH) |
| Conductance based neuron models | Stratos Kosmidis |
| Minimal neuron models | Stratos Kosmidis |
| Synaptic models | Vassilis Cutsuridis (UoL) |
| Network models | Vassilis Cutsuridis |
| Synaptic plasticity models | Vassilis Cutsuridis |
| Models of dendrites | Nassi Papoutsi/Spiros Chavlis (FORTH) |
