200140 SE Vertiefungsseminar: Geist und Gehirn (2020W)
Cognitive Modeling in Python
Prüfungsimmanente Lehrveranstaltung
Labels
Vertiefungsseminare können nur fürs Pflichtmodul B verwendet werden! Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Di 01.09.2020 07:00 bis Do 24.09.2020 07:00
- Abmeldung bis Fr 02.10.2020 07:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
The seven theoretical/discussion sessions will take place in person if the Covid situation allows (Dates: 06.10; 20.10; 03.11; 17.11; 01.12; 12.01.2021; 26.01.2021).
The six practical sessions (every second class on 13.10; 27.10; 10.11 ;24.11; 15.12; 19.01.2021) will take place remotely for sure.
- Dienstag 06.10. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 13.10. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 20.10. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 27.10. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 03.11. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 10.11. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 17.11. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 24.11. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 01.12. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 15.12. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 12.01. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 19.01. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
- Dienstag 26.01. 09:45 - 11:15 Hörsaal D Psychologie, NIG 6.Stock A0624
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
1. Reading summary for each lecture (30%)
2. Programming tutorials (40%)
3. Final project (coding or essay, 30%)
2. Programming tutorials (40%)
3. Final project (coding or essay, 30%)
Mindestanforderungen und Beurteilungsmaßstab
-Detailed requirements will be given in class.
Minimum requirements are class attendance with maximum of two missed sessions (without special arrangement made with the lecturer before a missed class).
Minimum requirements are class attendance with maximum of two missed sessions (without special arrangement made with the lecturer before a missed class).
Prüfungsstoff
There is no exam, evaluation is based on the tutorials, reading summaries and the final project.
Literatur
Research papers:
L Griffiths, T., Kemp, C., & B Tenenbaum, J. (2008). Bayesian models of cognition.Blohm, G., Kording, K. P., & Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1).Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ... & Gillon, C. J. (2019). A deep learning framework for neuroscience. Nature neuroscience, 22(11), 1761-1770.Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. (2019). Engineering a less artificial intelligence. Neuron, 103(6), 967-979.Niv, Y., & Langdon, A. (2016). Reinforcement learning with Marr. Current opinion in behavioral sciences, 11, 67-73.Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.Yang, G. R., & Wang, X. J. (2020). Artificial neural networks for neuroscientists: A primer. arXiv preprint arXiv:2006.01001.Optional textbook:
Frisby, J. P., & Stone, J. V. (2010). Seeing: The computational approach to biological vision. The MIT Press.
L Griffiths, T., Kemp, C., & B Tenenbaum, J. (2008). Bayesian models of cognition.Blohm, G., Kording, K. P., & Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1).Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ... & Gillon, C. J. (2019). A deep learning framework for neuroscience. Nature neuroscience, 22(11), 1761-1770.Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. (2019). Engineering a less artificial intelligence. Neuron, 103(6), 967-979.Niv, Y., & Langdon, A. (2016). Reinforcement learning with Marr. Current opinion in behavioral sciences, 11, 67-73.Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.Yang, G. R., & Wang, X. J. (2020). Artificial neural networks for neuroscientists: A primer. arXiv preprint arXiv:2006.01001.Optional textbook:
Frisby, J. P., & Stone, J. V. (2010). Seeing: The computational approach to biological vision. The MIT Press.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Do 24.09.2020 12:09
The hybrid format will consist of -if possible- in person lectures and remote Python programming tutorials alternating weekly, where a lecture is followed by related tutorial the week after.
Students are expected to prepare with reading a research paper and writing a short reading summary for each lecture. Students will work remotely on the tutorials, but will receive help and feedback during the sessions, with virtual intros/summaries. Experience with Python programming is not required but very helpful.