227-0395-00L Neural Systems
|Dozierende||R. Hahnloser, M. F. Yanik, B. Grewe|
|Periodizität||jährlich wiederkehrende Veranstaltung|
|Kurzbeschreibung||This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experimental techniques used in studies of animal behavior and underlying neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on efforts to design artificially intelligent systems.|
|Lernziel||This course introduces |
- Methods for monitoring of animal behaviors in complex environments
- Information-theoretic principles of behavior
- Methods for performing neurophysiological recordings in intact nervous systems
- Methods for manipulating the state and activity in selective neuron types
- Methods for reconstructing the synaptic networks among neurons
- Information decoding from neural populations,
- Sensorimotor learning, and
- Neurobiological principles for machine learning.
|Inhalt||From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.|
|Voraussetzungen / Besonderes||Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).|
As part of the exercises for this class, students are expected to complete a (python) programming project to be defined at the beginning of the semester.