Neurodynamics (Lecture + Tutorial)

8.3047

Dozenten

Beschreibung

Prerequisites: Linear Algebra, Analysis I.
In this lecture, we will cover theoretical accounts of neurons and neural networks that endeavour to explain the computational principles and cognitive functions they implement. The lens through which we will be looking at neural circuits is the theory of dynamical systems.
We start by modelling how single neurons respond to input by considering bio-physical models and increasingly abstract models of spiking activity. By learning to use tools from dynamical systems theory such as attractors, stability, and bifurcations, we will begin to understand how neurons process information.
We then turn our attention to complex dynamical systems, dynamic networks of (spiking) neurons, apply and extend the toolbox from the single neuron case and discover how neurons can collectively compute a meaningful response to a stimulus. Throughout, we will focus on understanding the computation a neuron or neuronal network performs and putting our findings into the context of state-of-the-art research in theoretical/computational neuroscience and on artificial neural networks.
To supplement and embellish the ideas presented in the lecture, an accompanying seminar (“What do single neurons compute?”) is offered. The course is intended for Bachelor students in their third term and Master students.

Weitere Angaben

Ort: 66/E33: Do. 16:00 - 18:00 (13x), 93/E31: Fr. 12:00 - 14:00 (14x), 32/110: Fr. 14:00 - 16:00 (14x)
Zeiten: Do. 16:00 - 18:00 (wöchentlich), Ort: 66/E33, Fr. 12:00 - 14:00 (wöchentlich), Ort: 93/E31, Fr. 14:00 - 16:00 (wöchentlich) - Tutorial, Ort: 32/110
Erster Termin: Donnerstag, 13.04.2023 16:00 - 18:00, Ort: 66/E33
Veranstaltungsart: Vorlesung und Seminar (Offizielle Lehrveranstaltungen)

Studienbereiche

• Cognitive Science > Bachelor-Programm
• Cognitive Science > Master-Programm
• Data Science