1. Introduction to Computational Neuroscience

1.1 Brains and Computation [Wed. January 17] Transparencies

• Churchland, P. S. (1986) Neurophilosophy: Toward a Unified Science of the Mind-Brain. Chapter 3, Functional Neuroanatomy, sections 3.1 and 3.2, pp 99-107. 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 above two readings are not required for anyone who has had a neuroanatomy course.
• [optional] Cherniak, C. (1990) The bounded brain: toward quantitative neuroanatomy. Journal of Cognitive Neuroscience, 2:58-68.

1.2 Neurophysiology for Computer Scientists [Mon. January 22] 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.
  The above 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.

2. The Hippocampus

2.1 Vectors, Matrices, and Associative Memory [Wed. January 24] 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.
• Matrix memory demo (Matlab)

2.2 Anatomy of the Hippocampal System [Mon. January 29]

• 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 of Hippocampus [Wed. January 31] 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.]
• 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.

2.4 Hippocampus as a Cognitive Map [Mon. February 5]

• Touretzky, D. S. and Redish, A. D. (1996) Theory of rodent navigation based on interacting representations of space. Hippocampus, 6(3):247-270.
• Redish, A.D. and Touretzky, D. S. (1997) Cognitive maps beyond the hippocampus. Hippocampus, 7(1):15-35.
• [optional] O'Keefe, J., and Burgess, N. (1996) Geometric determinants of the place fields of hippocampual neurons. Nature, 381:425-428.

2.5 Cholinergic Modulation: Learning vs. Recall [Wed. February 7] 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.

2.6 Pattern Completion/Separation [Mon. February 12] Transparencies

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

2.7 Phase Precession in Hippocampus [Wed. February 14]

• Tsodyks, M. V., Skaggs, W. E., Sejnowski, T. J., and McNaughton, B. L. (1996) Population dynamics and theta rhythm phase precession of hippocampal place cell firing: a spiking neuron model. Hippocampus 6(3):271-280.
• Bose, A., Booth, V., and Recce, M. (2000) A temporal mechanism for generating the phase precession of hippocampal place cells. Journal of Computational Neuroscience 9:5-30.

3. Neural Basis of Learning and Memory

3.1 Synaptic Learning Rules [Mon. February 19]

• 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. (1995) Handbook of Brain Theory and Neural Networks. Articles on BCM rule, Hebbian synaptic plasticity, post-Hebbian learning rules, and principal component analysis.
• Synaptic learning demo (Matlab)

3.2 LTP and the NMDA receptor [Wed. 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.

3.3 Invertebrate Learning [Mon. February 26] 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, pp. 101-146. Columbia University Press.

Mid-term Exam (take home)

4. Cerebellum

4.1 Anatomy of the Cerebellum [Wed. February 28] Transparencies

• Ghez, C. (1991) The cerebellum. In E. R. Kandel, J. H. Schwartz, and T. M. Jessell (Eds.), Principles of Neural Science, 3rd edition, chapter 41, pp. 626-646. 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 [Mon. March 5]

• 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.

4.3 Motor Learning [Wed. March 7]

• Barto, A., Fagg, A., Sitkoff, N., and Houk, J. (1999) A cerebellar model of timing and prediction in the control of reaching. Neural Computation, 11:565-594.
• [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 [Mon. March 12]

• 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.

5.2 Cerebellar Timing and Classical Conditioning [Wed. March 14]

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

5.3 Predictive Hebbian Learning [Mon. March 19]

• 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. (1995) A framework for mesencephalic dopamine systems based on predictive hebbian learning. Journal of Neuroscience, 16(5);1936-1947.

5.4 Multi-Region Models of Associative Learning [Wed. March 21]

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

Spring Break [March 26-30]

6. Basal Ganglia

6.1 Anatomy of the basal ganglia [Mon. April 2] Transparencies

• Côté, L. and Crutcher, M. D. (1991) The basal ganglia. In E. R. Kandel, J. H. Schwartz, and T. M. Jessell (Eds.), Principles of Neural Science, 3rd edition, ch. 42, pp. 647-659. 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 [Wed. 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.
• Houk, J. C., Adams, J. L., and Barto, A. G. (1995) A model of how the basal ganglia generate and use neural signals that predict reinforcement. In J. C. Houk, J. L. Davis, and D. G. Beiser (Eds.), Models of Information Processing in the Basal Ganglia, chapter 13, pp. 249-270.
• [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 [Mon. April 9]

• 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): 2913-2927.
• 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.

7.2 Attractor Model of the Rodent Head Direction System [Wed. 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.

7.3 Coordinate Transformations In Parietal Cortex [Mon. April 16]

• 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. Vision

8.1 Neuroanatomy of the Visual System [Wed. April 18]

• Van Essen, D. C. and Anderson, C. H. (19??) Information processing strategies and and pathways in the primate retina and visual cortex. In S. F. Zornetzer, J. L. Davis and C. Lau (Eds.), An Introduction to Neural and Electronic Networks, pp. 43-72. San Diego, CA: Academic Press.
• Van Essen, D. C., and DeYoe, E. A. (1995) Concurrent processing in the primate visual cortex. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences, pp. 383-400.
• [optional] Kaas, J. H. (1989) Why does the brain have so many visual areas? Journal of Cognitive Neuroscience, 1:121-135.

8.2 Ocular Dominance and Orientation Columns in V1 [Mon. April 23]

• 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.

8.3 Object Recognition in Temporal Cortex [Wed. April 25]

• 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 Birdsong Learning [Monday April 30]

• Troyer, T.W. and Doupe, A.J. (2000) An associational model of birdsong sensorimotor learning I. Efference copy and the learning of song syllables. Journal of Neurophysiology, 84:1204-1223.
• Troyer, T.W. and Doupe, A.J. (2000) An associational model of birdsong sensorimotor learning II. Temporal hierarchies and the learning of song sequences. Journal of Neurophysiology, 84:1224-1239.
• Zebra finch song web site at http://www.williams.edu/Biology/ZFinch/zfsong.html. Contains an example song. Also see the Zeba Finch Song Archive at http://wso.williams.edu/~hwilliam/ZFsongs/.

9.2 Models of Working Memory [Wednesday May 2]

• Camperi, M., and Wang, X.-J. (1998) A model of visuospatial working memory in prefrontal cortex: Recurrent network and cellular bistability. Journal of Computational Neuroscience, 5:383-405.
• [optional] Durstewitz, D., Seamans, J.K., and Sejnowski, T.J. (2000) Neurocomputational models of working memory. Nature Neuroscience, 3:1184-1191.

Final Exam: Tuesday, May 8, 5:30-8:30 pm, Wean Hall 8427