1. Introduction to Computational Neuroscience

1.1 Brains and Computation [Mon. August 31] Slides: Course intro, and Brains and Computation

• Churchland, P. S. (2002) Brain-Wise: Studies in Neurophilosophy, chapter 1, pp. 1-34. MIT Press.

• Crick, F. and Asanuma, C. (1986) Certain aspects of the anatomy and physiology of the cerebral cortex. In J. L. McClelland and D. E. Rumelhart (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. II, chapter 20, pp. 333-371. MIT Press.

  ***Note: The Crick & Asanuma reading is not required for anyone who has had neuroanatomy.

• [optional] Churchland, P. S. and Sejnowski, T. J. (1992) The Computational Brain, chapter 3, sections 3.1 and 3.2, pp. 61-76. MIT Press.

• [optional] Dewdney, A. K. (1989) A Tinkertoy computer that plays tic-tac-toe. Scientific American, October, 1989, pp. 120-123.

1.2 Neurophysiology for Computer Scientists [Wed. September 2] Slides

• Churchland, P.S. (1986) Neurophilosophy: Toward a Unified Science of the Mind-Brain. Chapter 2, Modern Theory of Neurons, pp 48-77. MIT Press.

• Matlab demo: HHSim: Hodgkin-Huxley simulator.

  The Churchland reading is not required for anyone who has taken neurophysiology. The following paper is still worth reading, though, as an illustration of how information processing within a dendritic tree can be far more complex than linear summation of inputs:

• Shepherd, G. M., Woolf, T. B., and Carnevale, N. T. (1989) Comparison between active properties of distal dendritic branches and spines: Implications for neuronal computations. Journal of Cognitive Neuroscience, 1(3):273-286.

• Homework 1 out

No Class on Labor Day [Mon. September 7]

2. Cerebellum

2.1 Anatomy of the Cerebellum [Wed. September 9] slides

• 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: Elsevier.

• 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 14] slides

• 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 16] slides

• 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

2.4 Cerebellar Timing and Classical Conditioning [Mon. September 21] slides

• 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 23] slides

• 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 28] Slides

• [optional] Jordan, M. I. (1986) An introduction to linear algebra in parallel distributed processing. In D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. I, chapter 9, pp. 365-422. MIT Press.

  The above is for those of you who would like a quick review of dot products and eigenvectors. It also contains some material on why linear algebra is relevant to neural network models.

• Kohonen, T., Oja, E., and Lehtiö, P. (1981) Storage and processing of information in distributed associative memory systems. In G. E. Hinton and J. A. Anderson (eds.), Parallel Models of Associative Memory, chapter 4, pp. 105-143. Lawrence Erlbaum Associates.

• Matlab demo: Math for matrix memories

• Matlab demo: Matrix memory (download matmem.zip)

• Homework 3

3.2 Anatomy of the Hippocampal System [Wed. September 30] Slides

• 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 resources.

• [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 5] slides

• 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 7] slides

• Willshaw, D. J. and Buckingham, J. T. (1990) An assessment of Marr's theory of the hippocampus as a temporary memory store. Philosophical Transactions of the Royal Society of London B, 329:205-215.

3.5 Pattern Completion/Separation [Mon. October 12] slides

• O'Reilly R. C. and McClelland, J. L. (1994) Hippocampal conjunctive encoding, storage and recall: avoiding a tradeoff. Hippocampus, 4(6):661-682.

• [optional] Bakker, A., Kirwan, C. B., Miller, M., and Stark, C. E. L. (2008) Pattern separation in the human hippocampal CA3 and dentate gyrus. Science, 319:1640-1642.

• Homework 4 (Matlab, and codon calculations)

Midterm Exam [Wed. October 14]

Sample midterm questions

Society for Neuroscience Meeting, October 17-21 (no class)

3.6 Hippocampus as a Cognitive Map [Mon. October 26] slides

• Sharp, P. E. (ed.) (2002) The Neural Basis of Navigation: Evidence from Single Cell Recording. pp. vii-xxiii. Kluwer Academic Publishers.

• [optional] Samsonovich, A. and McNaughton, B. L. (1997) Path integration and cognitive mapping in a continuous attractor neural network model. Journal of Neuroscience, 17(15):5900-5920.

• Matlab demo: 2D attractor bump (download hill2d.zip)

• Matlab demo: attractor model with two 1D maps (download twomap.zip)

3.7 Entorhinal Grid Cells and Path Integration [Wed. October 28] slides

• Burgess N, Barry C, O'keefe J. (2007) An oscillatory interference model of grid cell firing. Hippocampus. 17(9):801-812.

• [optional] Fuhs, M. C., and Touretzky, D. S. (2006) A spin glass model of path integration in rat medial entorhinal cortex. Journal of Neuroscience 26(16):4266-4276.

3.8 Theta, Gamma, and Working Memory [Mon. November 2] slides

• Koene, R. A., and Hasselmo, M. E. (2007) First-In-First-Out item replacement in a model of short-term memory based on persistent spiking. Cerebral Cortex 17:1766-1781.

4. Neural Basis of Learning and Memory

4.1 Synaptic Learning Rules [Wed. November 4] slides

• Baxter, D. A. and Byrne, J. H. (1993) Learning rules from neurobiology. In D. Gardner (ed.), The Neurobiology of Neural Networks, chapter 4, pp. 71-105. MIT Press.

• [optional] Arbib, M. A. (2002) Handbook of Brain Theory and Neural Networks, 2nd edition. Articles on Hebbian Learning and Neuronal Regulation, Hebbian Synaptic Plasticity, and Post-Hebbian Learning Algorithms.

• Matlab demo: Synaptic learning rules (download ltp.zip)

• Matlab demos: Principal and independent component analysis (download pca.zip)

4.2 Synaptic Plasticity and the NMDA receptor [Mon. November 9] slides

• Citrai, A., and Malenka, R. C. (2008) Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33:18-41.

• Homework 5 (synaptic learning rules)

5. Conditioning and Reinforcement Learning

5.1 The Rescorla-Wagner Model and Its Descendants [Wed. November 11] slides

• 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 (download rescwag.zip)

• Homework 6 (Rescorla-Wagner learning)

5.2 Predictive Hebbian Learning [Mon. November 16] slides

• 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. Nature, 377:725-728.

• 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 (download td.zip)

6. Basal Ganglia

6.1 Anatomy of the basal ganglia [Wed. November 18] slides

• 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.

6.2 Reinforcement learning models of the basal ganglia [Mon. November 23] slides

• 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, 13(4):841-862.

• [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, 68(3):945-960.

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 25 (day before Thanksgiving)

7. Cortical Representations

7.1 Coordinate Transformations In Parietal Cortex [Mon. November 30] slides

• 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, 9(2):222-237.

• [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 learning algorithm.

• Matlab demo: Population vector encoding (download popvec.zip)

7.2 Probablistic Population Codes in Cortex [Wed. December 2] slides

• Pouget, A., Dayan, P., and Zemel, S. (2003) Inference and computation with population codes. Annual Review of Neurosicence, 26:381-410.

• 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 material.

8. Visual System

8.1 Low-Level Vision: Retina, LGN, and V1 [Mon. December 7] slides and more slides

• 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 9] slides and more slides

• Serre, T., Oliva, A., and Poggio, T. (2007) A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciecnes 104(15):6424-6429. (Also see the Supporting Information.)

• Lee, T. S., and Mumford, D. (2003) Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A, 20(7):1434-1448.

• Optional: Additional slides on radial basis functions which might help you better understand the Serre et al. article. You are not responsible for the material in these slides.

Final Exam: (Tuesday, December 15)

Modeling Projects are due Dec. 18, by 5:00 PM.