1. Introduction to Computational Neuroscience

1.1 Brains and Computation [Mon. January 10] Intro, Transparencies

• 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. January 12] Transparencies

• 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 Martin Luther King's Birthday [Mon. January 17]

2. The Hippocampus

2.1 Vectors, Matrices, and Associative Memory [Wed. January 19] Transparencies

• [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. January 24]

• 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.

• [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.]

2.3 Marr's Associative Memory Model, Part I [Wed. January 26] Transparencies

• 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. January 31]

• 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. February 2] Transparencies

• 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.

No class: mini mid-semester break [Mon. February 7]

2.6 Pattern Completion/Separation [Wed. February 9] Transparencies

• 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 (codon calculations)

2.7 Hippocampus as a Cognitive Map [Mon. February 14]

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

• 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. Neural Basis of Learning and Memory

3.1 Synaptic Learning Rules [Wed. February 16]

• 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 LTP and the NMDA receptor [Mon. February 21]

• 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. February 23] Transparencies

• 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] 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: Feb. 23. Due back: March 2.

4. Cerebellum

4.1 Anatomy of the Cerebellum [Mon. Feb 28] Transparencies

• 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. March 2]

• 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 demo: CMAC model (download cmac1.zip)

• Homework 5 (CMAC learning)

CMU Spring Break [March 7-11]

4.3 Motor Learning [Mon. March 14]

• Barto, A., Fagg, A., Sitkoff, N., and Houk, J. (1999) A cerebellar model of timing and prediction in the control of reaching. Neural Computation, 11(3):565-594.

• [optional] A. Fagg, N. Sitkoff, A. Barto, J. Houk. (1997) Cerebellar learning for control of a two-link arm in muscle space. Proceedings of the IEEE Conference on Robotics and Automation, pp. 2638-2644.

• [optional] A. Fagg, N. Sitkoff, A. Barto, J. Houk. (1997) A model of cerebellar learning for control of arm movements using muscle synergies. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 6-12.

5. Conditioning and Reinforcement Learning

5.1 The Rescorla-Wagner Model and Its Descendants [Wed. March 16]

• 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 Cerebellar Timing and Classical Conditioning [Mon. March 21]

• 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.

• 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.

5.3 Predictive Hebbian Learning [Wed. March 23]

• 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)

5.4 Multi-Region Models of Associative Learning [Mon. March 28]

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

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

6. Basal Ganglia

6.1 Anatomy of the basal ganglia [Wed. March 30] Transparencies

• 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 Models of the basal ganglia [Mon. April 4]

• 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.

7. Spatial Representations

7.1 Population Vectors and the Motor System [Wed. April 6]

• Georgopoulos, A. P., Kettner, R. E., and Schwartz, A. B. (1988) Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. Journal of Neuroscience, 8(8): 2928-2937.

• Redish, A. D. and Touretzky, D. S. (1994) The reaching task: Evidence for vector subtraction in the motor system? Biological Cybernetics, 71(4): 307-317.

• [optional] Sanger, T. D. (1994) Theoretical considerations for the analysis of population coding in motor cortex. Neural Computation, 6(1):29-37.

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

7.2 Attractor Model of the Rodent Head Direction System [Mon. April 11]

• Goodridge, J. P., and Touretzky, D. S. (2000) Modeling attractor deformation in the rodent head direction system. Journal of Neurophysiology, 83(6):3402-3410.

• Taube, J.S., Goodridge, J.P., Golob, E.J., Dudchenko, P.A., & Stackman, R.W. (1996). Processing the head direction cell signal: a review and commentary. Brain Research Bulletin, 40:477-486.

• Matlab demo: Attractor bump model (download bump.zip)

7.3 Coordinate Transformations In Parietal Cortex [Wed. April 13]

• 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.

8. Visual System

8.1 Ocular Dominance and Orientation Columns in V1 [Mon. April 18]

• Swindale, N. V. (1996) The development of topography in the visual cortex: a review of models. NETWORK: Computation in Neural Systems, 7(2):161-247.

8.2 Object Recognition in Temporal Cortex [Wed. April 20]

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

• [optional] Tanaka, K. (1996) Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19:109-139.

9. Miscellaneous Topics

9.1 Neural Engineering: Silicon Retinas [Monday April 25]