Computational Neuroscience: Single-Neuron Models
This module starts with a primer on neuroscience and the role of computational neuroscience. The next part of the module covers biologically-grounded models of single neurons, including leaky-integrate-and-fire and conductance-based neurons, but also more abstract models of neural activity and spike trains. You will learn how these different computational models describe and simplify the underlying biological processes to a different degree. We will examine in detail how these different neuron models can be used in numerical simulations to address research questions on computation in single neurons and circuits. In the exercises accompanying the lectures you will gain hands-on experience in implementing the different neuron models in Python, running numerical simulations, and performing calculations related to analytical solutions of the model equations and biophysics. The focus is on single neuron models, but we will also make use of available software (e.g. NEST Desktop) to examine how single neuron models can be integrated into simulations of neural networks. While the emphasis throughout the module is on methodological issues, how models can be built, tested and validated at each level, we will also draw connections to specific brain regions to motivate and illustrate the models.
apply techniques from computational neuroscience to simulate neural activity
become familiar with different types of single neuron models, their mathematical description, and their different levels of biological abstraction
acquire skills in modelling neurons, synapses and circuits and connect these models to biology and computation
understanding of the biological basis for computation in neurons
written exam at the end of the semester (120 min)
Prof. Dr. Robert SchmidtLecturer
|(+49) 234-32-27300 email@example.com NB 3/68|
- Course type
- Summer Term 2023
- moodle course available
every week on Monday from 08:30 to 10:00 in room IA 03/466.
First appointment is on 03.04.2023
Last appointment is on 10.07.2023
every week on Friday from 12:00 to 14:00 in room ID 03/121.
First appointment is on 14.04.2023
Last appointment is on 14.07.2023
Programming in Python, mathematical knowledge (linear algebra and calculus) and an interest in neurobiology
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition . Cambridge University Press.
Dayan, P., & Abbott, L. F. (2005). Theoretical neuroscience: computational and mathematical modeling of neural systems . MIT press.
The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210