Complex cell-like direction selectivity through spike-timing dependent plasticity

IETE Journal of Research

Volumn 49, Issue 2-3, 2003, Pages 97-111

  Rao, Rajesh P N ^a   Sejnowski, Terrence J ^b  

^a University of Washington   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MOTION DETECTION; NEUROSCIENCE; PRIMARY VISUAL CORTEX;

CELLS; MATHEMATICAL MODELS; NEURAL NETWORKS; NEUROPHYSIOLOGY; PLASTICITY;

VISION;

EID: 0041941434     PISSN: 03772063     EISSN: 0974780X     Source Type: Journal    
DOI: 10.1080/03772063.2003.11416329     Document Type: Article

References (69)

1. D H, Hubel & T N, Wiesel, Receptive fields and functional architecture of monkey striate cortex, Journal of Physiology (London), vol 195, p 215–243, 1968.
2. D H, Hubel & T N, Wiesel, Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. Journal of Physiology (London), vol 160, pp 106–154, 1962.
3. D, Ferster, S, Chung & H, Wheat, Orientation selectivity of thalamic input to simple cells of cat visual cortex, Nature, vol 380, pp 249–252, 1996.
4. R C, Reid & J M, Alonso, The processing and encoding of information in the visual cortex, Curr Opin Neurobiol, vol 6, no 4, pp 475–480, 1996.
5. D, Ferster & S, Lindstrom, An intracellular analysis of geniculocortical connectivity in area 17 of the cat, Journal Physiol (Lond), vol 342, pp 181–215, 1983.
6. G, Henry, M, Mustan & J, Bullier, Different geniculate inputs to B and C cells of cat striate cortex, Exp Brain Research, vol 52, pp 179–189, 1983.
7. S, LeVay & C, Gilbert, Laminar patterns of geniculocortical projections in the cat, Brain Research, vol 113, pp 1–19, 1976.
8. W, Singer, F, Tretter & M, Cynader, Organization of cat striate cortex:a correlation of receptive-feld properties with afferent and efferent connections, Journal Neurophysiol, vol 30, pp 1080–1098, 1975.
9. G M, Ghose, R D, Freeman & I, Ohzawa, Local intracortical connections in the cat's visual cortex:postnatal development and plasticity, Journal Neurophysiol, vol 72, pp 1290–1303, 1994.
10. K, Toyama, M, Kimura & K, Tanaka, Organization of cat visual cortex as investigated by cross-correlation technique, Journal Neurophysiol, vol 46, pp 202–214, 1981.
11. J M, Alonso & L M, Martinez, Functional connectivity between simple cells and complex cells in cat striate cortex, Nat Neurosci, vol 1, no 5, pp 395–403, 1998.
12. D C, Burr, M C, Morrone & L, Maffei, Intra-cortical inhibition prevents simple cells from responding to textured visual patterns, Exp Brain Res, vol 43, no 3–4, pp 455–458, 1981.
13. P, Hammond & D M, MacKay, Differential responsiveness of simple and complex cells in cat striate cortex to visual texture, Exp Brain Res, vol 30, nos 2-3, pp 275–296, 1977.
14. J A, Movshon, The velocity tuning of single units in cat striate cortex. Journal Physiol (Lond), vol 249, no 3, pp 445–468, 1975.
15. J G, Malpeli, C, Lee, H D, Schwark & T G, Weyand, Cat area 17.1. Pattern of thalamic control of cortical layers, Journal Neurophysiol, vol 56, no 4, pp 1062–1073, 1986.
16. K A, Archie & B W, Mel, A model for intradendritic computation of binocular disparity, Nature Neurosci, vol 3, no 1, pp 54–63, 2000.
17. B W, Mel, D L, Ruderman & K A, Archie, Translation-invariant orientation tuning in visual "complex" cells could derive from intradendritic computations, Journal Neuroscience, vol 18, no 11, pp 4325–4334, 1998.
18. M S, Livingstone, Mechanisms of direction selectivity in macaque V1, Neuron, vol 20, pp 509–526, 1998.
19. J C, Anderson, T, Binzegger, O, Kahana, K A, Martin & I, Segev, Dendritic asymmetry cannot account for directional responses of neurons in visual cortex, Nature Neurosci, vol 2, no 9, pp 820–824, 1999.
20. N W, Daw & H J, Wyatt, Kittens reared in a unidirectional environment:evidence for a critical period, Journal of Physiology, vol 257, no 1, pp 155–170, 1976.
21. A L, Humphrey & A B, Saul, Strobe rearing reduces direction selectivity in area 17 by altering spatiotemporal receptive-field structure, Journal Neurophysiol, vol 80, no 6, pp 2991–3004, 1998.
22. B, Roerig & J P Y, Kao, Organization of intracortical circuits in relation to direction preference maps in ferret visual cortex, Journal of Neuroscience, vol 19, no 24, p RC44, 1999.
23. H, Markram, J, Lubke, M, Frotscher & B, Sakmann, Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs, Science, vol 275, pp 213–215, 1997.
24. G Q, Bi & M M, Poo, Synaptic modifications in cultured hippocampal neurons:Dependence on spike timing, synaptic strength, and postsynaptic cell type, Journal Neurosci, vol 18, pp 10464–10472, 1998.
25. W, Levy & O, Steward, Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus, Neuroscience, vol 8, pp 791–797, 1983.
26. L I, Zhang, H W, Tao, C E, Holt, W A, Harris & M M, Poo, A critical window for cooperation and competition among developing retinotectal synapses, Nature, vol 395, pp 37–44, 1998.
27. D, Feldman, Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex, Neuron, vol 27, no 45-56, 2000.
28. L F, Abbott & K I, Blum, Functional significance of long-term potentiation for sequence learning and prediction, Cereb Cortex, vol 6, pp 406–416, 1996.
29. R P N, Rao & T J, Sejnowski, Spike-timing dependent Hebbian plasticity as temporal difference learning, Neural Computation, in press, 2001.
30. R P N, Rao, M S, Livingstone & T J, Sejnowski, Direction selectivity from predictive sequence learning in recurrent neocortical circuits, Soc Neuro Abstr, vol 25, p 1316, 1999.
31. R P N, Rao & T J, Sejnowski, Predictive sequence learning in recurrent neocortical circuits, In Advances in Neural Information Processing Systems 12, pp 164–170. Cambridge, MA:MIT Press, 2000.
32. Z F, Mainen & T J, Sejnowski, Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, vol 382, pp 363–366, 1996.
33. A, Destexhe, Z F, Mainen & T J, Sejnowski, Kinetic models of synaptic transmission, In:C, Koch & I, Segev, editors, Methods in Neuronal Modeling, Cambridge, MA:MIT Press, 1998.
34. S, Song, K D, Miller & L F, Abbott, Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nature Neuroscience, vol 3 pp 919–926, 2000.
35. C, Bell, D, Bodznick, J, Montgomery & J, Bastian, The generation and subtraction of sensory expectations within cerebellum-like structures, Brain, Behavior and Evolution, vol 50 Suppl, no 1, pp 17–31, 1997.
36. J A, Movshon & R C, Van Sluyters, Visual neural development, Annu Rev Psychol, vol 32, pp 477–522, 1981.
37. K D, Miller, Correlation-based models of neural development, In:M A, Gluck & D E, Rumelhart, editors, Neuroscience and Connectionist Theory, pp 267–353. Hillsdale, NJ, Lawrence Erlbaum Associates, 1990.
38. A M, Sillito, The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat, Journal Physiol (Lond), vol 250, no 2, pp 30329, 1975.
39. S, Nelson, L, Toth, B, Sheth & M, Sur, Orientation selectivity of cortical neurons during intracellular blockade of inhibition, Science, vol 265, pp 774–777. 1994.
40. F S, Chance, S B, Nelson & L F, Abbott, Complex cells as cortically amplified simple cells, Nature Neuroscience, vol 2, pp 277–282, 1999.
41. R B, Tootell, E, Switkes, M S, Silverman & S L, Hamilton, Functional anatomy of macaque striate cortex. II. Retinotopic organization, Journal Neurosci, vol 8, no 5, pp 1531–1568, 1988.
42. D C, Van Essen, Functional organization of primate visual cortex, In:A., Peters and E G, Jones, editors, Cerebral Cortex, vol 3, pp 259–329, New York, NY, Plenum, 1985.
43. M J, Berry, I H, Brivanlou, T A, Jordan & M, Meister, Anticipation of moving stimuli by the retina, Nature, vol 398, pp 334–338, 1999.
44. J C, Feidler, A B, Saul, A, Murthy & A L, Humphrey, Hebbian learning and the development of direction selectivity:the role of geniculate response timings, Network:Computation in Neural Systems, vol 8, pp 195–214, 1997.
45. S, Wimbauer, O G, Wenisch, K D, Miller & J L, van Hemmen, Development of spatiotemporal receptive fields of simple cells:I Model Formulation. Biological Cybernetics, vol 77, pp 453–461, 1997.
46. R C, Emerson, M C, Citron, D J, Felleman & J H, Kaas, A proposed mechanism and site for cortical direction selectivity. In D, Rose and V G, Dobson, editors, Models of the Visual Cortex, pp 420–431, New York, NY, Wiley, 1985.
47. C, Koch & T, Poggio, The synaptic veto mechanism:does it underlie direction and orientation selectivity in the visual cortex, In D, Rose & V G, Dobson, editors, Models of the Visual Cortex, pp 408–419, New York, NY, Wiley, 1985.
48. R, Maex & G A, Orban, Model circuit of spiking neurons generating directional selectivity in simple cells, Journal Neurophysiol, vol 75, no 4, pp 1515–1545, 1996.
49. P, Mineiro & D, Zipser, Analysis of direction selectivity arising from recurrent cortical interactions, Neural Computation, vol 10, no 2, pp 353–371, 1998.
50. H, Suarez, C, Koch & R, Douglas, Modeling direction selectivity of simple cells in striate visual cortex with the framework of the canonical microcircuit, Journal Neurosci, vol 15, no 10, pp 6700–6719, 1995.
51. E H, Adelson & J, Bergen, Spatiotemporal energy models for the perception of motion, Journal Opt Soc Am A, vol 2, no 2, pp 284–299, 1985.
52. R C, Emerson, J R, Bergen & E H, Adelson, Directionally selective complex cells and the computation of motion energy in cat visual cortex, Vision Research, vol 32, no 2, pp 203–218, 1992.
53. M R, Mehta, C A, Barnes & B L, McNaughton, Experience-dependent, asymmetric expansion of hippocampal place fields, Proc Natl Acad Sci, USA, vol 94, pp 8918–8921, 1997.
54. M R, Mehta & M, Wilson, From hippocampus to V1:Effect of LTP on spatiotemporal dynamics of receptive fields, In J, Bower, editor, Computational Neuroscience, Trends in Research 1999, Amsterdam, Elsevier Press, 2000.
55. W, Gerstner, R, Kempter, J L, van Hemmen & H, Wagner, A neuronal learning rule for sub-millisecond temporal coding, Nature, vol 383, no 76-81, 1996.
56. G J, Stuart & B, Sakmann, Active propagation of somatic action potentials into neocortical pyramidal cell dendrites, Nature, vol 367, pp 69–72, 1994.
57. L F, Abbott, J A, Varela, K, Sen & S B, Nelson, Synaptic depression and cortical gain control, Science, vol 275, pp 220–224, 1997.
58. A M, Thomson & J, Deuchars, Temporal and spatial properties of local circuits in neocortex, Trends Neurosci, vol 17, no 3, pp 119–126, 1994.
59. M V, Tsodyks & H, Markram, The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability, Proc Natl Acad Sci USA, vol 94, no 2, pp 719–723, 1997.
60. H, Barlow, Cerebral predictions, Perception, vol 27, pp 885–888, 1998.
61. J G, Daugman & C J, Downing, Demodulation, predictive coding, and spatial vision, Journal Opt Soc Am A, vol 12, pp 641–660, 1995.
62. A A, Minai & W, Levy, Sequence learning in a single trial. Proceedings of the 1993 INNS World Congress on Neural Networks II, pp 505–508. New Jersey:Erlbaum, 1993.
63. P R, Montague & T J, Sejnowski, The predictive brain:Temporal coincidence and temporal order in synaptic learning mechanisms, Learning and Memory, vol 1, pp 1–33, 1994.
64. D, Mumford, Neuronal architectures for pattern-theoretic problems, In C, Koch & J L, Davis, editors, Large-Scale Neuronal Theories of the Brain, pp 125–152, Cambridge, MA, MIT Press, 1994.
65. R P N, Rao & D H, Ballard, Dynamic model of visual recognition predicts neural response properties in the visual cortex, Neural Computation, vol 9, no 4, pp 721–763, 1997.
66. R P N, Rao & D H, Ballard, Predictive coding in the visual cortex:A functional interpretation of some extra-classical receptive field effects, Nature Neuroscience, vol 2, no 1, pp 79–87, 1999.
67. W, Schultz, P, Dayan & P R, Montague, A neural substrate of prediction and reward, Science, vol 275. pp 1593–1598, 1997.
68. C, Koch, Biophysics of Computation:Information Processing in Single Neurons, New York, Oxford University Press, 1999.
69. W, Senn, H, Markram & M, Tsodyks, An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation, vol 13, no 1, pp 35–67, 2001.