1. Introduction to Computational Neuroscience

1.1 Brains and Computation [Mon. August 27] Intro, Lecture Slides

• 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 a neuroanatomy course.

• [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.

1.2 Neurophysiology for Computer Scientists [Wed. August 29] 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 (and the lecture) are not required for anyone who has taken a neurophysiology course. 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, 1:273-286.

• Homework 1 out

No Class on Labor Day [Mon. September 3]

2. The Hippocampus

2.1 Vectors, Matrices, and Associative Memory [Wed. September 5] 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 2

2.2 Anatomy of the Hippocampal System [Mon. September 10] 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.

• [enrichment] 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.]

2.3 Marr's Associative Memory Model, Part I [Wed. September 12] 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.     [Note: it's probably best to read the commentaries first, then the paper.]

2.4 Marr's Associative Memory Model, Part II [Mon. September 17] 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.

2.5 Cholinergic Modulation: Learning vs. Recall [Wed. September 19] slides

• Hasselmo, M. E. and Schnell, E. (1994) Laminar selectivity of the cholinergic suppression of synaptic transmission in rat hippocampal region CA1: computational modeling and brain slice physiology. Journal of Neuroscience, 14(6):3898-3914.

• [optional] Hasselmo, M. E., Wyble, B. P., and Wallenstein, G. V. (1996) Encoding and retrieval of episodic memories: role of cholinergic and GABAergic modulation in the hippocampus. Hippocampus, 6(6):693-709.

2.6 Pattern Completion/Separation [Mon. September 24] slides

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

• Homework 3 (Matlab, and codon calculations)

2.7 Hippocampus as a Cognitive Map [Wed. September 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)

2.8 Entorhinal Grid Cells and Path Integration [Mon. October 1] 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. Neural Basis of Learning and Memory

3.1 Synaptic Learning Rules [Wed. October 3] 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)

3.2 Synaptic Plasticity and the NMDA receptor [Mon. October 8] slides

• Malenka, R. C. (1995) LTP and LTD: dynamic and interactive processes of synaptic plasticity. The Neuroscientist, 1(1):35-42.

• Bliss, T. V. P. and Collingridge, G. L. (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361:31-39.

• Bear, M. F., Cooper, L. N., and Ebner, F. F. (1987) A physiological basis for a theory of synapse modification. Science, 237:42-48.

• [optional] Bear, M. F. (1995) Mechanism for a sliding synaptic modification threshold. Neuron, 15:1-4.

• Homework 4 (synaptic learning rules)

3.3 Invertebrate Learning [Wed. October 10] slides

• Hawkins, R. D. and Kandel, E. R. (1984) Is there a cell-biological alphabet for simple forms of learning? Psychological Review, 91(3)375-391.

• Gluck, M. A. and Thompson, R. F. (1987) Modeling the neural substrates of associative learning and memory: a computational approach. Psychological Review, 94(2):176-191.

• [optional] Glanzman, D. L. (1995) The cellular basis of classical conditioning in Aplysia californica - it's less simple than you think. Trends in Neurosciences, 18(1):30-36.

• [optional] Glanzman, D. L. (2006) The cellular mecvhanisms of learning in Aplysia: of blind men and elephants. Biological Bulletin 210:271-279.

• [optional] MacPhail, E. (1993) The Neuroscience of Animal Intelligence: From the Sea Hare to the Seahorse, chapter 4, pp. 101-156. Columbia University Press.

• Matlab demo: Aplysia conditioning (download aplysia.zip)

Mid-term Exam (take home):

Out: October. 10. Due back: October 17.

4. Cerebellum

4.1 Anatomy of the Cerebellum [Mon. October 15] 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.

4.2 Table Lookup/Basis Function Models [Wed. October 17] slides

• Albus, J. S. (1971) A theory of cerebellar function. Mathematical Biosciences, 10:25-61.

• Tyrrell, T. and Willshaw, D. (1992) Cerbellar 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.

• Matlab demos: CMAC models: 1-dimensional function approximator (download cmac1.zip), and 2-dimensional arm kinematics (download cmac2.zip)

• Homework 5 (CMAC learning)

4.3 Cerebellar Timing and Classical Conditioning [Mon. October 22] 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] 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.

• [optional] Fiala, J.C., Grossberg, S., and Bullock, D. (1996) Metabotropic glutamate receptor activation in cerebellar Purkinje cells as substrate for adaptive timing of the classically conditioned eye-blink response. Journal of Neuroscience, 16(11):3760-3774.

4.4 Dynamics of Parallel Fibers and Purkinje Cells [Wed. October 24] 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.

5. Conditioning and Reinforcement Learning

5.1 The Rescorla-Wagner Model and Its Descendants [Mon. October 29] 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 [Wednesday, October 31] 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)

Society for Neuroscience Meeting: no class on November 5 or November 7

5.3 Multi-Region Models of Associative Learning [Monday, November 12] slides

• Gluck, M.A., and Myers, C. E. (2001) Gateway to Memory, chapter 6, pp. 145-187. Cambridge, MA: MIT Press. Complete book is available here.

• Matlab demos: Backpropagation learning (download bp.zip)

Start Work on Final Projects

• Final project instructions.

6. Basal Ganglia

6.1 Anatomy of the basal ganglia [Wednesday, November 14] 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.

• Alexander, G. E. and Crutcher. M. D. (1990) Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends in Neurosciences, 13(7):266-271.

6.2 Reinforcement learning models of the basal ganglia [Monday, November 19] 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.

No class on Wednesday, November 21 (day before Thanksgiving)

7. Cortical Representations

7.1 Coordinate Transformations In Parietal Cortex [Monday, November 26] 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.

7.2 Probablistic Population Codes in Cortex [Wednesday,, November 28] 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 [Monday, December 3] 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 [Wednesday, December 5] slides

• Riesenhuber, M., and Poggio, T. (1999) Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019-1025.

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

Final Exam: Tuesday, Dec. 11, 1:00-4:00 PM, Porter Hall room 125C.

Final Projects are due Monday, Dec. 17, by 5:00 PM.