Psy 506A Neural Encoding: Memory & Comprehension of Mammals

Psyc 506a: Neural Systems, Neural Encoding, and Computation

Tu/Th 2:00-3:15PM, Spring 2014

Psychology, Rm. 323A

Instructor: Stephen Cowen, Ph.D.


Cowen Office Hours: Monday 2:45-3:45.

OFFICE:  Life Sciences North, Rm. 347 (near the South entrance of UMC). Please email or call ahead as the floor is keycard locked. I’ll need to let you in.


Phone: (520) 626-2615


Final Exam:   5/13/14, 1:00pm

Course overview:

This course is intended to provide a basis for understanding how brains acquire, assimilate, store and retrieve information and how they compute adaptive responses to external inputs.  Understanding these processes requires a basic working knowledge of both the theoretical principles and biological mechanisms underlying neural signaling, knowledge representation and data storage. This goals of this course are twofold: 1) to introduce students to some of the fundamental observations and “facts” about neuroscience that every cognitive scientist should know, and 2) to put these observations into wider systems-level and computational frameworks so that these observations can be connected. As a result, the course will cover the fundamental biological and computational principles underlying processes such as learning, memory, sensation, and decision making.


Perhaps just as important as providing a background to systems and computational neuroscience, this course will also introduce students to the process of writing a NIH/NSF grant – an absolutely vital skill in today’s exceedingly competitive environment.

Required Texts:

All reading is required to be completed prior to each class so that discussions of the material can occur. On some classes there will be pop quizzes. Most but not all readings will be from:

·         Principles of Neural Science, Fifth Edition (Kandel et al. 2012). It is available in Kindle and hardcover. It may be cheaper at the website.

·         Journal Articles: Available on D2L or (soon)

·         PowerPoints: Will be archived on the D2L site. (uploaded immediately prior to each lecture)

Other resources…

·         Good computational books…

o   Brain Computation as Hierarchical Abstraction Hardcover by Dana H. Ballard (Author) – may make this a text for the next course…

o   Principles of Neural Design – sterling and Laughlin

o (Gerstner et al.)

·         Good intro to computation:

o   Python Machine Learning

·         Delcomyn, F. (ed). Foundations of Neurobiology. New York, 1998. (Chapters 2,4 & 5)

·         Squire L.R. et al., (Eds) Fundamental Neuroscience 2nd Edition, Academic Press, 2003

·         Shepherd G.M. (Ed.) The Synaptic Organization of the Brain, Oxford University Press 1990

·         Arbib, M.A., Erdi, P., Szentagothai, J. Neural Organization: Structure, Function and Dynamics. MIT Press, 1998.  An excellent introduction to understanding neural circuits and their relationship to behavior and cognition.

·         Dayan and Abbott’s Theoretical Neuroscience

·         Thomas Trappenberg’s Fundamentals of Computational Neuroscience.

·         Michael Frank’s Computational Cognitive Neuroscience (FREE!)

·         Churchland, P.S. and Sejnowski T.J. The Computational Brain. Bradford MIT Press, 1989. (An excellent introduction to neural computation and coding – no math). Oldie but a goodie (not as complex as others).


·         2 Exams (15% each). They are not cumulative. The large majority of questions will be based on the topics covered in lecture and especially the figures (and from the student-submitted questions). A good strategy would be to go through the slides and then read up (e.g. in the Kandel et al. book) on any terms or concepts in the slides that are remotely confusing. The tests will be paper and pen or pencil, mixed format (multi choice, short answer, and short essay), closed book, in class, and one 8½ x 11” sheet of notes is allowed for each test.

·         A one-page “Specific Aims” that describes your R21 grant proposal (10%) and 2 aims. This will also follow the format of a typical R21 specific aims sheet. (Mid semester)

·         Grant proposal (20%): This proposal follows, to-the-letter, the format of an R21 NIH grant proposal. It’s 6 pages single spaced. The details regarding formatting and structure will be provided in separate lectures, links, and handouts. The pages devoted to the references do not count towards the page count. The paper follows the precise format of an R21 grant. The grant proposal topic must cover a topic pertinent to the course scope. Some broad topic areas could include neural network models, neural coding and computation, plasticity, place/head direction cells, system and cellular consolidation, neural oscillations and relation to single-unit activity, population coding, temporal coding, mirror neurons, effects of attention on neural activity, prediction errors and neural coding of prediction errors, effects of neuromodulators on coding.

·         Presentations (15%): Each student will give 1 presentation to the class (entire class period). The student can choose the topic, but given the scope of the course, it should cover a level of granularity below fMRI, PET and lesion studies and get at specific mechanisms of neural computation/plasticity/coding. Some broad topic areas could include neural network models, neural coding and computation, plasticity, place/head direction cells, system and cellular consolidation, neural oscillations and relation to single-unit activity, population coding, temporal coding, mirror neurons, effects of attention on neural activity, prediction errors and neural coding of prediction errors, effects of neuromodulators on coding. Please provide links to at least one article for background reading 3 days before the presentation. Plan for a 50 minute presentation. It will probably go longer given questions.

·         Weekly report (10%) (See below): A ½+ page set of sample-questions and mental musings related to each week’s readings due on the Monday evening prior to the week that the material is covered (uploaded to D2L). Summaries will be due on the second week of class.

·         Class attendance and participation (15% of the course grade).

Weekly Reports

Active participation is crucial to the success and at least my enjoyment of the class. As a result, it is imperative that everyone completes the assigned readings in preparation for the lectures. By Monday at midnight to the D2L dropbox each student will provide a ½ page (or more) response to the following questions regarding the week’s readings:

·         A brief summary that covers at least 1 point from the readings that the student found particularly interesting. Musings/rantings regarding novel experiments, philosophical or scientific implications of the results, etc… are encouraged. Have fun with this! Also, if it's easier to express something with hand drawn (and legible) notes and pictures or flowcharts that can be scanned and uploaded, go for it.

·         2 multiple choice or short essay questions (with answers) that would be tests of understanding some aspect of the week’s readings. They can cover concepts or details from a figure in the text. Make them fair and clear questions as these questions will make up ~50% of the exam questions.





K = Kandel et al. book, Iz = Izhikevich book





Full references on next page



PPT 1: Hellos. Overview and intro to brains and neurons. Figure out presentation schedule.

K: Part 1, Intro Ch. 1,2

Jan 16 (R)



Jan  21 (T)

PPT 2: The active neuron: Channels and Excitability.  Review of fundamental neural signaling mechanisms; membranes, channels, ionic equilibria and neuronal excitation

K: Ch. 5,6,7

Jan  23 (R)



Jan  28 (T)

PPT 3: Synaptic Transmission and Physiology.  Transmitter release, excitation/inhibition in post-synaptic cell.  Electrical signaling.  Fields.

K: Ch. 7,8,10

Jan  30 (R)

Student Presentation 1: Laura on Ocular Dominance Columns

(Crowley and Katz, 2002)


Feb 4 (T)

PPT 4: A Survey of Circuitry in the Nervous System Synaptic convergence and divergence, multiplicity of synaptic types, divergent synaptic properties both between and within neuron types, mixed synapses, electrical synapses.

K: Ch. 35

Lateral inhibition and center surround

Feb 6 (R)

Student Presentation 2: Simon and sleep oscillations

(Astori et al., 2013)


Feb 11 (T)

PPT 5: Synaptic Bases of Memory (Plasticity).  Modification of synaptic communication through experience; short and long-term changes; associative vs. non-associative synaptic plasticity.  Associative Memory part 1.

K: Ch. 67

Feb 13 (R)

Student Presentation 3 Tony and DA

(Schultz, 2013)


Feb 18 (T)

PPT 6: Associative Memory in Neural Networks:  Basic principles of associative memory; pattern completion; constraints on storage capacity; sequence storage and retrieval.

K: Appendix E

(McNaughton and Morris, 1987)

Feb 20 (R)

Student Presentation 4 Katie and Spindles


(Nir et al., 2011)


Feb 25 (T)

PPT 7: Consolidation, the Hippocampus, and beyond.  Memory consolidation in cortical hierarchies, and the hippocampus.

K: 67 (not place field part)

(McNaughton et al., 2003; Rasch and Born, 2013; Stickgold and Walker, 2007)

Feb 27 (R)

Student presentation 5 Philip on bio-machine interfaces



Mar 4 (T)

The R21 Grant Proposal: Overview and where to learn more.

Read sample specific aims.

Mar  6 (R)

Exam Review and continuation of PPT 7



Mar  11 (T)

Exam Review


Mar  13 (R)

Exam 1



Mar  18 (T)

Spring Break!


Mar  20 (R)



Mar  25 (T)

PPT 8: Spatial Representation in the Hippocampus.  Place fields, head direction, and spatial navigation, including recent advances in understanding the origins of spatial information.

K: 67 (place field part)

(Moser et al., 2008)

(McNaughton et al., 2006)

Wiki background

Mar  27 (R)

Student Presentation 6 Silvana Attractors

*** 1 page single spaced Specific Aims Due.



Apr 1 (T)

PPT 8 Continued and aims discussion

K: Appendix E

(Georgopoulos et al., 1986)(Pouget et al., 2000)

Apr 3 (R)

Student Presentation 7 Mingzhu



Apr 8 (T)

PPT 9: Neural coding and computation continued: Philosophical look at representation. Time coding, connectionism, sleep-wake algorithms, homeostatic learning and the BCM rule.

Izhikevich: Ch. 1 and preface

K: Appendix E


Apr 10 (R)

Student Presentation 8 Yating



Apr 15 (T)

PPT 11: Reinforcement Learning and its application to the Basal Ganglia and Dopamine, reward prediction error and Parkinson’s disease


REMOVED: PPT 12: Cost-benefit decision making in the brain. Frontal mechanisms. (Tony)


Apr 17 (R)

Student Presentation 9 Heather



Apr 22 (T)

PPT 9 continued.


Apr 24 (R)




Apr 29 (T)

Student Presentation 10 Alisabeth Group discussions regarding R21’s (based on student’s updates Specific Aims)

Updated Specific Aims

May 1 (R)

Exam review and help with the R21



May 6 (T)

Last class: Exam Review help with the R21


May 13  1:00




* = Cowen at conference.







SELECTED READINGS (this will be updated throughout the course):

Astori, S., Wimmer, R.D., and Lüthi, A. (2013). Manipulating sleep spindles - expanding views on sleep, memory, and disease. Trends Neurosci. 36, 738–748.

Crowley, J.C., and Katz, L.C. (2002). Ocular dominance development revisited. Curr. Opin. Neurobiol. 12, 104–109.

Georgopoulos, A.P., Schwartz, A.B., and Kettner, R.E. (1986). Neuronal population coding of movement direction. Science 233, 1416–1419.

McNaughton, B.L., and Morris, R.G.M. (1987). Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci. 10, 408–415.

McNaughton, B.L., Barnes, C.A., Battaglia, F., Bower, M.R., Cowen, S.L., Ekstrom, A.D., Gerrard, J.L., Hoffman, K.L., Houston, F.P., Karten, Y.J., et al. (2003). Off-line Reprocessing of Recent Memory and its Role In Memory Consolidation: A Progress Report. In Behavioral Biology, P. Maquet, C. Smith, and R. Stickgold, eds. (Oxford: Oxford University Press), pp. 225–246.

McNaughton, B.L., Battaglia, F.P., Jensen, O., Moser, E.I., and Moser, M.-B. (2006). Path integration and the neural basis of the “cognitive map”. Nat. Rev. Neurosci. 7, 663–678.

Moser, E.I., Kropff, E., and Moser, M.-B. (2008). Place cells, grid cells, and the brain’s spatial representation system. Annu. Rev. Neurosci. 31, 69–89.

Nir, Y., Staba, R.J., Andrillon, T., Vyazovskiy, V. V, Cirelli, C., Fried, I., and Tononi, G. (2011). Regional slow waves and spindles in human sleep. Neuron 70, 153–169.

Pouget, a, Dayan, P., and Zemel, R. (2000). Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132.

Rasch, B., and Born, J. (2013). About sleep’s role in memory. Physiol. Rev. 93, 681–766.

Schultz, W. (2013). Updating dopamine reward signals. Curr. Opin. Neurobiol. 23, 229–238.

Stickgold, R., and Walker, M.P. (2007). Sleep-dependent memory consolidation and reconsolidation. Sleep Med. 8, 331–343.

Course Policies

Incomplete Grade Policy: Incomplete grades will be given only in special circumstances as outlined in University Policy as stated in “The University of Arizona Record General Academic Manual” at 

Academic Integrity: Cheating and other aspects of academic misconduct are covered under the UA academic code as described in the General Academic Manual. Students are highly encouraged to read the UA code of academic integrity as it appears at:

Special Needs Policy: Students needing special accommodations or special services should contact the Learning Disabilities Program/Salt ( at 621-1242, and/or the Center for Disability Related Resources, located at 1224 E. Lowell Street (621-3268). The needs of specialized services must be documented and verified by one of these units. I will do everything possible to accommodate students with special needs, but it is really important that you provide me with this information in advance.



Drs. Bruce McNaughton and David Euston for their considerable work in the development of this course.