Syllabus for 15-883 Fall '17:
Computational Models of Neural Systems
Version of August 28, 2017
Course web site: http://www.cs.cmu.edu/afs/cs/academic/class/15883-f17
David S. Touretzky
1. Introduction to Computational Neuroscience
Some quick Khan Academy lectures for people without a neuroscience background:
Is Khan Academy too elementary for you? Want something meatier? How
about a book chapter by Nobel Laureate Francis Crick? It's a bit
dated now (dendritic spikes are no longer controversial), but still
very good. This is an optional reading.
Optional readings for people who want to explore the nature of computation more deeply:
- Churchland, P. S. (2002) Brain-Wise: Studies in Neurophilosophy,
chapter 1, pp. 1-34. MIT Press.
1.2 Neurophysiology for Computer Scientists [Wed. August 30]
No Class on Labor Day [Mon. September 4]
Special resource (not required reading): Jaeger, D., Jorntell, H., and Kawato, M. (Eds.) Computation
in the Cerebellum. Neural Networks, 47, November 2013.
Special issue on the cerebellum.
2.1 Anatomy of the Cerebellum [Wed. September 6]
- Ghez, C. and Thach, W. T. (2000) The cerebellum. In E. R. Kandel,
J. H. Schwartz, and T. M. Jessell (Eds.), Principles of Neural
Science, 4th edition, chapter 42, pp. 832-852. New York:
- Glickstein, M. and Yeo, C. (1990) The cerebellum and motor
learning. Journal of Cognitive Neuroscience, 2:69-80.
2.2 Table Lookup/Basis Function Models [Mon. September 11]
- Albus, J. S. (1971) A theory of cerebellar function.
Mathematical Biosciences, 10:25-61.
- Tyrrell, T. and Willshaw, D. (1992) Cerebellar cortex: its
simulation and the relevance of Marr's theory. Philosophical
Transaction of the Royal Society of London, Series B, 336:239-257.
- [optional] Albus, J. S. (1975) Data storage in the Cerebellar Model
Articulation Controller (CMAC). Transactions of the ASME,
Journal of Dynamic Systems, Measurement, and Control, September
1975, pp. 228-233.
- [optional] Basics of
Information Theory, a short little tutorial explaining why
low-probability events transmit a large number of bits of information.
- Matlab demos: CMAC models: 1-dimensional
function approximator (download cmac1.zip), and 2-dimensional arm kinematics (download cmac2.zip)
- Homework 2 (CMAC learning)
2.3 Cerebellar Forward and Inverse Models in Motor Control [Wed. September 13]
- Wolpert, D. M., Miall, R. C., and Kawato, M. (1998) Internal models in the
cerebellum. Trends in Cognitive Sciences, 2(9):338-346.
- PID controller simulation: Excel spreadsheet
or OpenOffice spreadsheet
- YouTube videos: P vs. PID Control,
2-DOF inverted pendulum,
Torque vs. muscle control,
Learning inverse dynamics for a robot arm
- [optional] Manto et
Consensus paper: roles of the cerebellum in motor control -- the
diversity of ideas on cerebellar involvement in movement.
Cerebellum 11(2);457-487, June, 2012.
2.4 Cerebellar Timing and Classical Conditioning [Mon. September 18]
- Medina, J.F., and Mauk, M.D. (2000) Computer simulation of cerebellar
information processing. Nature Neuroscience, vol 3,
supplement, November 2000, pp. 1205-1211.
- Ohyama, T., Nores, W.L., Murphy, M., and Mauk, M.D. (2003) What the cerebellum computes.
Trends in Neurosciences, 26(4):222-227.
- [optional] Hesslow, G., Jirenhed, D.-A., Rasmussen, A.,
and Johansson, F. (2013) Classical
conditioning of motor responses: What is the learning mechanism?.
Neural Networks, 47:81-87, November 2013.
- [optional] Medina, J.F., Garcia, K.S., Nores, W.L.,
Taylor, N.M., and Mauk, M.D. (2000) Timing mechanisms in the
cerebellum: Testing predictions of a large-scale computer
simulation. Journal of Neuroscience, 20(14):5516-5525.
2.5 Dynamics of Parallel Fibers and Purkinje Cells [Wed. September 20]
- D'Angelo, E., et
al. (2016) Modeling
the cerebellar microcircuit: New strategies for a long-standing
issue. Frontiers in Cellular Neuroscience 10, article
176, July 2016.
- [optional] Santamaria, F., Tripp, P.G., and Bower, J.M. (2007) Feedforward inhibition controls
the spread of granule cell-induced Purkinje cell activity in the
cerebellar cortex. Journal of Neurophysiology, 97:248-263.
See also this supplementary material.
3. The Hippocampus
3.1 Vectors, Matrices, and Associative Memory [Mon. September 25]
3.2 Anatomy of the Hippocampal System [Wed. September 27]
- Johnston, D. and Amaral, D. G. (1998) Hippocampus. In G. M. Shepherd
(ed.), The Synaptic Organization of the Brain, 4th edition,
chapter 11, pp. 417-458. Oxford University Press. [Read pages 417-435
and 454-458. Skim the rest if you like.]
- Amaral, D. G. (1993) Emerging
principles of intrinsic hippocampal organization. Current
Opinion in Neurobiology, 3:225-229.
- [reference] www.temporal-lobe.com contains
a comprehensive summary of nearly 1600 known connections in the
hippocampal formation and the parahippocampal region (presubiculum,
parasubiculum, perirhinal and postrhinal cortex). It also has links
to hippocampal anatomy sites, latest research news, and other
- [optional] Witter, M. P. (1993) Organization of the
entorhinal-hippocampal system: a review of current anatomical
data. Hippocampus, 3(special issue):33-44. [Very technical,
but Figure 2 is worth looking at.]
3.3 Marr's Associative Memory Model, Part I [Mon. October 2]
- Marr, D. (1971) Simple memory: A
theory for archicortex. In L. M. Vaina (ed.), From the Retina
to the Neocortex: Selected papers of David Marr,
pp. 59-128. Includes commentaries by D. Willshaw and
B. McNaughton. Paper originally appeared in Philosophical
Transactions of the Royal Society of London B, 262:23-81. Read the commentaries first, then the paper. You need only
read sections 0-3 of the paper.
3.4 Marr's Associative Memory Model, Part II [Wed. October 4]
3.5 Pattern Completion/Separation [Mon. October 9]
- O'Reilly R. C. and McClelland, J. L. (1994) Hippocampal conjunctive encoding,
storage and recall: avoiding a tradeoff. Hippocampus,
- [optional] Bakker, A., Kirwan, C. B., Miller, M., and
Stark, C. E. L. (2008) Pattern
separation in the human hippocampal CA3 and dentate gyrus.
- Homework 4 (Matlab, and codon calculations)
Midterm Exam [Wed. October 11]
3.6 Hippocampus as a Cognitive Map [Mon. October 16]
3.7 Entorhinal Grid Cells and Path Integration [Wed. October 18]
3.8 Theta, Gamma, and Working Memory [Mon. October 23]
4. Neural Basis of Learning and Memory
4.1 Synaptic Learning Rules [Wed. October 25]
4.2 Synaptic Plasticity and the NMDA receptor [Mon. October 30]
5. Conditioning and Reinforcement Learning
5.1 The Rescorla-Wagner Model and Its Descendants [Wed. November 1]
- Sutton, R. S. and Barto, A. G. (1990) Time-derivative models of Pavlovian
reinforcement. In M. Gabriel and J. Moore (eds.), Learning and
Computational Neuroscience: Foundations of Adaptive Networks,
chapter 12, pp. 497-537.
- Miller, R. R., Barnet, R. C., and Grahame, N. J. (1995) Assessment of the Rescorla-Wagner
model. Psychological Bulletin, 117(3):363-386.
- Matlab demo: Rescorla-Wagner learning
- Homework 6 (Rescorla-Wagner learning)
5.2 Predictive Hebbian Learning [Mon. November 6]
- Hammer, M. (1993) An
identified neuron mediates the unconditioned stimulus in associative
olfactory learning in honeybees. Nature, 366:59-63.
- Montague, P. R., Dayan, P., Person, C., and Sejnowski,
T. J. (1995) Bee foraging in
uncertain environments using predictive hebbian learning.
- Montague, P. R., Dayan, P., and Sejnowski, T. J. (1996) A framework for mesencephalic
dopamine systems based on predictive hebbian learning. Journal
of Neuroscience, 16(5);1936-1947.
- Matlab demo: Temporal difference learning
6. Basal Ganglia
6.1 Anatomy of the basal ganglia [Wed. November 8]
- Delong, M. (2000) The basal
ganglia. In E. R. Kandel, J. H. Schwartz, and T. M. Jessell
(Eds.), Principles of Neural Science, 4th edition, chapter 43,
pp. 853-867. New York: Elsevier.
- Middleton, F. A., and Strick, P. L. (2001) A revised neuroanatomy of
frontal-subcortical circuits. In D. G. Lichter and J. L. Cummings
(Eds.), Frontal-Subcortical Circuits in Psychiatric and
Neurological Disorders, pp. 44-58. New York: Guilford.
- Videos: (1) Deep Brain Stimulation for Parkinsons;
Society for Neuroscience Meeting, November 11-15 (no class)
6.2 Reinforcement learning models of the basal ganglia [Mon. November 20]
- Schultz, W., Romo, R., Ljungberg, T., Mirenowicz, J., Hollerman,
J. R., and Dickinson, A. (1995) Reward-related signals carried by
dopamine neurons. In J. C. Houk, J. L. Davis, and D. G. Beiser
(Eds.), Models of Information Processing in the Basal Ganglia,
chapter 12, pp. 233-248.
- Suri, R. E., and Schultz, W. (2001) Temporal difference model reproduces
anticipatory neural activity. Neural Computation,
- [optional] Schultz, W., Apicella, P., Scarnati, E., and
Ljungberg, T. (1992) Neuronal
activity in monkey ventral striatum related to the expectation of
reward. Journal of Neuroscience, 12(12):4595-4610.
- [optional] Apicella, P., Scarnati, E., Ljungberg, T., and
Schultz, W. (1992) Neuronal
activity in monkey striatum related to the expectation of predictable
environmental events. Journal of Neurophysiology,
Start Work on Modeling Project
- Standard modeling project
- You can arrange your own modeling project by speaking with the instructor
if you don't want to do the standard project.
No class on Wednesday, November 22 (day before Thanksgiving)
7. Cortical Representations
7.1 Coordinate Transformations In Parietal Cortex [Mon. November 27]
- Zipser, D., and Andersen, R. A. (1988) A back-propagation programmed network
that simulates response properties of a subset of posterior parietal
neurons. Nature, 331: 679-684.
- Pouget, A. and Sejnowski, T. J. (1997)
Spatial transformation in the parietal cortex using basis
functions. Journal of Cognitive Neuroscience,
- [optional] Pouget, A., and Sejnowski, T. J. (1996) A model of
spatial representations in parietal cortex explains hemineglect.
In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (eds.),
Advances in Neural Information Processing Systems 8, pp,
10-16. Cambridge, MA: MIT Press.
- [optional] Backpropagation
learning slides give a quick introduction to the important
- Matlab demo: Population vector encoding
7.2 Probablistic Population Codes in Cortex [Wed. November 29]
- Pouget, A., Dayan, P., and Zemel, S. (2003) Inference and computation with
population codes. Annual Review of Neurosicence,
- Ma, W. J., Beck, J. M., Latham, P. E., and Pouget, A. (2006) Bayesian inference with probabilistic
population codes. Nature Neuroscience, 9(11)1432-1438. See
also this optional supplementary
8. Visual System
8.1 Low-Level Vision: Retina, LGN, and V1 [Mon. December 4]
and more slides
and a diagram
- Van Essen, D. (1992) Information processing in the
primate visual system: an integrated systems perspective.
Science, vol. 225, no. 5043, pp. 419-423.
- Marr, D. (1982) Vision, chapter 1. San Francisco:
W. H. Freeman.
- Marr, D., and Poggio, T. (1976) Cooperative computation of stereo
disparity. Science, vol. 194, no.462, pp. 283-287.
8.2 Models of Object Recognition in Temporal Cortex [Wed. December 6]
slides and more slides
Final Exam The final exam will take place on Thursday,
Dec. 14 from 1-4pm. The location is Hammerschlag Hall room B131.
Modeling Projects are due Friday,
Dec. 15 by 5:00 PM.