NEURL-UA 302 Special Topics: Decisions and Actions Sample Syllabus
Prerequisites: NEURL-UA 220 (Behav & Integ NS)
This course will present the fundamental of the fields of sensory perception, actions and decision making, as well as the links between them.
The course will consist of a series of lectures with background mandatory reading, mostly book chapters, reviews or classical papers. The students are expected to read the background material before the class. Students will get much more out of the lectures if they have done the background reading before coming to class. To motivate this, there will be short quizzes about the reading material due the day before class (see grading below). There will also be a considerable amount of lecture content that will not be found in the readings; thus, class attendance and participation is very important. In addition, on selective weeks, the lecture will be shorter and the second half of the class will be discussion sessions in which the class will critically evaluate primary literature relevant to the topics of the course. These will not be lecture-style, but rather the paper will be presented by the students, asked in random, in a discussion led by the Professor. Materials from these discussion papers will also be included in the quizzes (see below).
Most readings will be classical papers and reviews. The most relevant general neuroscience textbook is the 6th edition of “Neuroscience” by Purves, Augustine, Fitzpatrick, Hall, LaMantia, McNamara, and White; however, class sessions draw on material that is not in the textbook. Required readings will be drawn from reviews, journal articles, and book chapters and will be available electronically.
Much of the course material does not come from the textbook because I want to expose you to some of the really important issues in sensory perception and decisions, and I have not found a textbook that contains the right material and operates at the right level. The emphasis is on understanding core principles and ideas about how neural systems work and how we can start to understand how neural activity gives rise to behavior. We focus on a smaller number of topics but go into them in greater depth. As a result, simply memorizing facts will generally not yield good performance.
Each student’s grade will be determined by a combination of performance on exams, quizzes, and class participation. These components will be weighted as follows:
- 50% Take home Exam
- 30% Research proposal
- 20% Class Participation (including project presentation)
The exam will consist of multiple-choice and short answer questions. In addition, there will be a few assay questions; from these you only need to answer one. These assay questions will involve more intricate problem-solving that will require students to synthesize material and apply key concepts to hypothetical problems. The Exam will focus heavily on the material that we discuss in class, thus understanding deeply the material covered in lectures and discussions is the key to success (and this generally will not happen without classroom attendance).
In this research paper, your group will come up with a novel hypothesis or question, and design an experiment to test it. The paper will follow a short grant format, with these sections (pages single-spaced, 11 pt): - Specific aims (0.5-1 page, describing 2-3 questions or hypotheses you would like to test). - Background and Significance (2-3 pages) – why is this question important and significant and what has been done previously to motivate it? What would be the broader benefits from answering this question/testing this hypothesis? - Experimental plan (2 pages) – details about the experiment and data analyses themselves. What outcome would support or refute your hypothesis?
Students needing special arrangements should contact Dr. Angelaki and arrange an office visit.
Class 1: Introduction: Basic principles of sensory perception; Concepts of psychophysical measurement and signal detection theory (Angelaki)
Reading: (1) Appendix A, Goldstein, Sensation and Perception
Class 2: Basic principles of perceptual decision making – sensory representation (Angelaki)
Reading: (1) Britten KH, Shadlen MN, Newsome WT, Movshon JA. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci. 1992 Dec;12(12):4745-65;
(2) Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci. 1996 Jan-Feb;13(1):87-100.
Class 3: Basic principles of perceptual decision making – evidence accumulation (Angelaki)
Reading: (1) Gold JI, Shadlen MN. The neural basis of decision making. Annu Rev Neurosci. 2007;30:535-74. Review.
(2) Shadlen MN, Kiani R. Decision making as a window on cognition. Neuron. 2013 Oct 30;80(3):791-806. doi: 10.1016/j.neuron.2013.10.047. Review.
(3) Brody CD, Hanks TD. Neural underpinnings of the evidence accumulator. Curr Opin Neurobiol. 2016 Apr;37:149-157. doi: 10.1016/j.conb.2016.01.003. Epub 2016 Feb 12. Review.
Class 4: Multisensory Decisions (Angelaki)
Reading: (1) Fetsch CR, DeAngelis GC, Angelaki DE. Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons Nat Rev Neurosci. 2013 Jun;14(6):429-42. doi: 10.1038/nrn3503. Review.
(2) Drugowitsch J, DeAngelis GC, Klier EM, Angelaki DE, Pouget A. Elife. 2014 Jun 14;3. doi: 10.7554/eLife.03005.
Class 5: Decision models (Wang)
Reading: (1) Wang XJ. Probabilistic decision making by slow reverberation in cortical circuits. Neuron. 2002 Dec 5;36(5):955-68. (2) Wang XJ. Decision making in recurrent neuronal circuits. Neuron. 2008 Oct 23;60(2):215-34. doi: 10.1016/j.neuron.2008.09.034. Review.
Quiz due: Monday
Class 6: Causal Manipulations: microstimulation and inactivation (Angelaki)
Reading: (1) Salzman CD, Murasugi CM, Britten KH, Newsome WT. Microstimulation in visual area MT: effects on direction discrimination performance J Neurosci. 1992 Jun;12(6):2331-55.
(2) Hanks TD, Ditterich J, Shadlen MN. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat Neurosci. 2006 May;9(5):682-9. Epub 2006 Apr 9.
(3) Gu Y, Deangelis GC, Angelaki DE. Causal links between dorsal medial superior temporal area neurons and multisensory heading perception. J Neurosci. 2012 Feb 15;32(7):2299-313. doi: 10.1523/JNEUROSCI.5154-11.2012. Discussion (in class by students): Purushothaman G, Bradley DC. Nat Neurosci. 2005 Jan;8(1):99-106. Epub 2004 Dec 19.
Class 7 Bayesian decision models (Ma)
Reading: Ma WJ. Bayesian Decision Models: A Primer. Neuron. 2019 Oct 9;104(1):164-175. doi: 10.1016/j.neuron.2019.09.037. Review.
Class 8: Confidence in decisions (Ma)
Reading: Hsin-Hung Li & Wei Ji Ma Confidence reports in decision-making with multiple alternatives violate the Bayesian confidence hypothesis, https://www.biorxiv.org/content/10.1101/583963v1.full Quiz due: Monday, March 23 by 5 pm
Class 9: Flexible decision-making through long-term integration of evidence (Kiani)
Reading: (1) Waskom ML, Okazawa G, Kiani R. Designing and Interpreting Psychophysical Investigations of Cognition. Neuron. 2019 Oct 9;104(1):100-112. doi: 10.1016/j.neuron.2019.09.016. Review. (2) Purcell BA, Kiani R. (2016). Hierarchical decision processes that operate over distinct time scales underlie choice and changes in strategy. PNAS. 113(31): E4531-4540. (3) Waskom ML, Kiani R. (2018). Decisionmaking through integration of sensory evidence at prolonged timescales. Current Biology. 28(23): 3850-3856.
Class 10: Attention (Angelaki)
Reading: (1) Ashcraft, Cognition, Chapter 4; (2) Reynolds JH, Chelazzi L. Attentional modulation of visual processing. Annu Rev Neurosci. 2004;27:611-47. Review.
Short 3-min presentation of your project
Class 11: Reinforcement learning (Angelaki)
Reading: (1) Chapter 1 of “Reinforcement Learning: An Introduction”, Richard S. Sutton and Andrew G. Barto (read more if you are interested);
(2) Matthew P. H. Gardner, Geoffrey Schoenbaum, Samuel J. Gershman. Rethinking dopamine as generalized prediction error. Proc Biol Sci. 2018 Nov 21; 285(1891): 20181645;
(3) Glimcher PW. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc Natl Acad Sci U S A. 2011.
Class 12: Cognitive maps and flexible behavior – a shift towards cognition (Angelaki)
Reading: (1) Behrens TEJ, Muller TH, Whittington JCR, et al. What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron. 2018;100
(2):490–509. doi:10.1016/j.neuron.2018.10.002 (2) Stachenfeld KL, Botvinick MM, Gershman SJ. Nat Neurosci. 2017 Nov;20(11):1643-1653. doi: 10.1038/nn.4650.
Class 13: Decisions in naturalistic tasks with active sensing (Angelaki)
Reading: (1) Pitkow X, Angelaki DE. Inference in the Brain: Statistics Flowing in Redundant Population Codes. Neuron. 2017 Jun 7;94(5):943-953. doi: 10.1016/j.neuron.2017.05.028. Review.
(2) Lakshminarasimhan KJ, Avila E, Neyhart E, DeAngelis GC, Pitkow X, Angelaki DE. Neuron. 2020 Mar 6. pii: S0896-6273(20)30146- X. doi: 10.1016/j.neuron.2020.02.023.
Class 14: 10 min presentation of individual project
FINAL PAPER DUE: DATE TBD