Introduction to Computational Neuroscience
Einführung in Computational Neuroscience
6 LPWiSeEN
Voraussetzungen
Empfohlen, im Wortlaut
Knowledge of the topics of the modules • 11112 Mathematik IT-1 (Diskrete Mathematik) • 11113 Mathematik IT-2 (Lineare Algebra) • 11213 Mathematik IT-3 (Analysis) • 11756 Algorithmen und Datenstrukturen, or 12101 Algorithmieren und Programmieren
Inhalte
Based on examples the module presents the methodical procedure for the analysis and modeling of neurons and neural systems. Ethical aspects are discussed in connection with animal and human experiments. Presented Topics: Spiking neurons, resting membrane potential, ion channels, action potential, Hodgkin-Huxley model, phase plane analysis, leaky integrate-and-fire model, synaptic transmission, synaptic plasticity, firing rate neurons, neural networks, perceptron, Hebb's learning rule, attractor networks.
Lernziele
Upon completion of the module, students are able to understandof neuronal systems and behavioral performance, to evaluate the analysis and modeling of neurons, as well as to implement and to analyse neurons and neural networks.
Prüfungsleistung
Prerequisite: • Successful completion of exercises Final Module Examinatio: • Written exam, 120 minutes OR • Oral examination, 30-45 minutes In the first lecture it will be announced, if the examination will be offered in written or oral form.
Literatur (2)
- P. Dayan, L. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (2005), MIT Press, ISBN 978-0262541855
- Gerstner W, Kistler WM, Naud R, Paninski L: Neuronal Dynamics: From single neurons to networks and models of cognition, Cambridge University Press (2014), https://neuronaldynamics.epfl.ch
Bemerkungen
• Study programme Medizininformatik B.Sc.: Compulsory elective module in complex „Informatik" • Study programme Informatik B.Sc.: Compulsory elective module in complex „Praktische Informatik" (level 300) • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Advanded Methods” • Study programme Künstliche Intelligenz Technologie M.Sc.: Compulsory elective module in complex „Kognitions- und Neurowissenschaft” • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Data Science Applications” • Study programme Mathematics M.Sc.: Compulsory elective module in complex „Applications: Natural Sciences and Engineering”
Quelle: Modulbeschreibung der BTU · abgerufen 29.09.2026Original