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Machine learning in cognitive computational neuroscience
DozentIn:Prof. Dr. rer. nat. Tim Christian Kietzmann, Dr. Sushrut Thorat, Rowan Sommers, M. Sc.
Veranstaltungstyp:Vorlesung und Seminar (Offizielle Lehrveranstaltungen)
Ort:93/E44: Di. 10:00 - 12:00 (13x) Dienstag, 06.02.2024, Dienstag, 05.03.2024, Dienstag, 19.03.2024 10:00 - 12:00, 93/E12: Do. 16:00 - 18:00 (10x), 69/127: Do. 16:00 - 18:00 (10x), 32/131: Do. 16:00 - 18:00 (10x), 93/E31: Dienstag, 30.01.2024 10:00 - 12:00
Semester:WiSe 2023/24
Zeiten:Di. 10:00 - 12:00 (wöchentlich) - Lectures, Ort: 93/E44, Do. 16:00 - 18:00 (wöchentlich) - Workgroup 1, Ort: 93/E12, Do. 16:00 - 18:00 (wöchentlich) - Workgroup 2, Ort: 69/127, Do. 16:00 - 18:00 (wöchentlich) - Workgroup 3, Ort: 32/131, Termine am Dienstag, 30.01.2024, Dienstag, 06.02.2024, Dienstag, 05.03.2024, Dienstag, 19.03.2024 10:00 - 12:00, Ort: 93/E31, 93/E44
Erster Termin:Dienstag, 17.10.2023 10:00 - 12:00, Ort: 93/E44
Beschreibung:Requirements: a basic understanding of neurobiology and neuropsychology (with a special focus on vision), as well as a solid understanding of deep learning and linear algebra.

Abstract: Machine learning and neuroscience have a long intertwined history of trying to create and understand phenomena of intelligent, adaptive behaviour. The underlying connectionist approach has been highly influential for both fields. For machine learning, it led to the development of deep neural networks, which are built upon multiple biological inspirations (e.g. distributed coding, activation functions, stochasticity, dropout, attention, convolutions, etc). For computational neuroscience, neural networks act as a modelling framework for testing hypotheses of how distributed sets of simpler units can give rise to complex behaviour. In a true interdisciplinary fashion, these modelling efforts have benefited greatly from recent deep learning developments, as they allow for end-to-end trained systems performing complex tasks on real-world data. What emerged from this joint endeavour is the field of neuroconnectionism, which aims to integrate biological details into deep learning systems (architecture, learning objectives, and input statistics), while testing the resulting models against high-dimensional neural data and behaviour.
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