TCN

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Topics in Computational Neuroscience

Overview

UPDATE: If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

NOTE: This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

E-mail List

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web link or by sending mail to: majordomo@lists.berkeley.edu that contains: 

       subscribe redwood_tcn

in the body of the message.

Guidelines for Presenting Papers

Each person that selects a paper should present (no slides):

  • an executive summary
  • an outline of the key points, ideas, or contributions
  • a description of the key figures
  • what you took away from the paper
  • some potential questions for discussion

Readings for Next Meeting! (Feb. 8th)

  • Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press link
  • Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 link

New Time and Location

7:00 PM on Wednesdays in the Beach room, 3105 Tolman (the doors on the east side of the building should be open).

Syllabus (Topics and Readings)

✔ indicates papers we've read.

Early Work

  • ✔ K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) link
  • ✔ Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" link
  • ✔ Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. link
  • Marvin Minsky, Steps Toward Artificial Intelligence link
  • McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) link
  • Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr link
  • Readings from Dartmouth Conf. 1956 proceedings
  • Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. link
  • "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 link

Coding

  • ✔ Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. link
  • ✔ Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. link
  • Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). link
  • Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). link
  • Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). link

Cortical Microcircuit/Universal Cortical Algorithm

  • Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press link
  • Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 link
  • Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. link
  • Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. link
  • Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. link
  • Selection from: Hawkins, J., On Intelligence (Chapter 6) link
  • Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. link

Feedback, Hierarchical Organization, Generative Models

  • Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. link
  • Mumford chapter in Large Scale Neuronal Theories of the Brain link
  • TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 link

Manifold Learning

  • Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 link
  • JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 link
  • ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 link

Plasticity, Hebbian Learning

  • Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 link
  • Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. link
  • P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. link
  • M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. link
  • Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. link
  • Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. link
  • H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). link
  • Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. link

Oscillations

  • Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. link
  • Neuron Special Issue on Oscillations, 1999, 24(1) link

Associative Memory

  • J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) link
  • G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). link
  • Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

Models of Invariance

  • Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. link
  • K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 link
  • P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. link
  • L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. link

Active Perception-sensorimotor loops

  • Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. link
  • Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. link
  • Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. link

Theories of the Ventral Stream

  • Ullman "Streams and Counter Streams", chapter in Large Scale Neuronal Theories of the Brain
  • Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. link
  • Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. link

Theories of Hippocampus

  • Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. link
  • Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. link
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