This course introduces principles of information processing in neural systems. It covers basic neuroscience on a level suitable for engineering students. The course introduces neuroscientific techniques used in studies of both animal behaviors and their underlying neural mechanisms. Students learn about neural signaling principles gained from experimental data.
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, 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)
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs