The Convallis Rule for Unsupervised Learning in Cortical Networks

PLoS Computational Biology

Volumn 9, Issue 10, 2013, Pages

  Yger, Pierre ^a   Harris, Kenneth D ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED OPTIMIZATION; NEURONS;

CELLULAR MECHANISMS; CORTEXES; CORTICAL NETWORK; CORTICAL PLASTICITY; DOWN-STREAM; REAL-WORLD; SPEECH STIMULI; SPIKE PATTERNS; SPIKING NEURONES; SYNAPTIC PLASTICITY;

UNSUPERVISED LEARNING;

ARTICLE; BRAIN CORTEX; CONVALLIS RULE; IN VITRO STUDY; LEARNING; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MENTAL TASK; NEOCORTEX; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NERVE CELL STIMULATION; POSITIVE FEEDBACK; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; SPIKE WAVE; TASK PERFORMANCE; VALIDATION PROCESS;

ACTION POTENTIALS; ARTIFICIAL INTELLIGENCE; COCHLEA; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NEOCORTEX; NEURONAL PLASTICITY; NEURONS; SPEECH PERCEPTION;

EID: 84887298711     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003272     Document Type: Article

References (88)

1. Feldman DE, (2009) Synaptic mechanisms for plasticity in neocortex. Annu Rev Neurosci 32: 33-55.
2. Malenka RC, Bear MF, (2004) LTP and LTD: an embarrassment of riches. Neuron 44: 5-21.
3. Shouval HZ, Wang SSH, Wittenberg GM, (2010) Spike timing dependent plasticity: a consequence of more fundamental learning rules. Front Comput Neurosci 4: 19.
4. Lisman J, Spruston N, (2005) Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nat Neurosci 8: 839-841.
5. Lisman J, Spruston N, (2010) Questions about STDP as a general model of synaptic plasticity. Front Synaptic Neurosci 2: 140.
6. Feldman DE, (2012) The spike-timing dependence of plasticity. Neuron 75: 556-571.
7. Larsen RS, Rao D, Manis PB, Philpot BD, (2010) STDP in the Developing Sensory Neocortex. Frontiers in synaptic neuroscience 2: 9.
8. Kuhl PK, (2004) Early language acquisition: cracking the speech code. Nat Rev Neurosci 5: 831-843.
9. DiCarlo JJ, Zoccolan D, Rust NC, (2012) How does the brain solve visual object recognition? Neuron 73: 415-434.
10. Barlow HB, (1989) Unsupervised learning. Neural Computation 1: 295-311.
11. Hastie T, Tibshirani R, Friedman JH (2003) The Elements of Statistical Learning. Springer, corrected edition.
12. Marr D, (1970) A theory for cerebral neocortex. Proc R Soc Lond B Biol Sci 176: 161-234.
13. Konorski J, (1967) Some new ideas concerning the physiological mechanisms of perception. Acta Biol Exp (Warsz) 27: 147-161.
14. Bienenstock EL, Cooper LN, Munro PW, (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci 2: 32-48.
15. Cooper LN, Bear MF, (2012) The BCM theory of synapse modification at 30: interaction of theory with experiment. Nat Rev Neurosci 13: 798-810.
16. Intrator N, Cooper LN, (1992) Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions. Neural Networks 5: 3-17.
17. Friedman JH, Stuetzle W, (1981) Projection pursuit regression. Journal of the American Statistical Association 76: 817-823.
18. Bell AJ, Sejnowski TJ, (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7: 1129-1159.
19. Hyvärinen A, Karhunen J, Oja E (2001) Independent Component Analysis. Wiley-Interscience, 1 edition, citeulike:105835 pp.
20. Izhikevich EM, Desai NS, (2003) Relating STDP to BCM. Neural Comput 15: 1511-1523.
21. Pfister JP, Gerstner W, (2006) Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 26: 9673-9682.
22. Boustani SE, Yger P, Frégnac Y, Destexhe A, (2012) Stable learning in stochastic network states. J Neurosci 32: 194-214.
23. Clopath C, Büsing L, Vasilaki E, Gerstner W, (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nat Neurosci 13: 344-352.
24. Senn W, Markram H, Tsodyks M, (2001) An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Comput 13: 35-67.
25. Artola A, Bröcher S, Singer W, (1990) Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature 347: 69-72.
26. Sjöström PJ, Turrigiano GG, Nelson SB, (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32: 1149-1164.
27. Nevian T, Sakmann B, (2006) Spine Ca2+ signaling in spike-timing-dependent plasticity. J Neurosci 26: 11001-11013.
28. Sjöström PJ, Turrigiano GG, Nelson SB, (2003) Neocortical LTD via coincident activation of presynaptic NMDA and cannabinoid receptors. Neuron 39: 641-654.
29. Sjöström PJ, Turrigiano GG, Nelson SB, (2004) Endocannabinoid-dependent neocortical layer-5 LTD in the absence of postsynaptic spiking. J Neurophysiol 92: 3338-3343.
30. Bender VA, Bender KJ, Brasier DJ, Feldman DE, (2006) Two coincidence detectors for spike timingdependent plasticity in somatosensory cortex. J Neurosci 26: 4166-4177.
31. Rodriguez-Moreno A, Paulsen O, (2008) Spike timing-dependent long-term depression requires presynaptic NMDA receptors. Nat Neurosci 11: 744-745.
32. Karmarkar UR, Buonomano DV, (2002) A model of spike-timing dependent plasticity: one or two coincidence detectors? J Neurophysiol 88: 507-513.
33. Min R, Nevian T, (2012) Astrocyte signaling controls spike timing-dependent depression at neocortical synapses. Nature neuroscience 15: 746-53.
34. Larkum ME, Zhu JJ, Sakmann B, (1999) A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398: 338-341.
35. Brader JM, Senn W, Fusi S, (2007) Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural computation 19: 2881-912.
36. Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB, (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391: 892-896.
37. Abraham W, Bear M, (1996) Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19: 126-30.
38. Hulme SR, Jones OD, Abraham WC, (2013) Emerging roles of metaplasticity in behaviour and disease. Trends in neurosciences 36: 353-62.
39. Gjorgjieva J, Clopath C, Audet J, Pfister JP, (2011) A triplet spike-timing-dependent plasticity model generalizes the bienenstock-cooper-munro rule to higher-order spatiotemporal correlations. Proc Natl Acad Sci U S A 108: 19383-19388.
40. Leonard R, Doddington G, (1993) Linguistic Data Consortium. Philadelphia.
41. Lyon R (1982) A computational model of filtering, detection, and compression in the cochlea. In: Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '82. volume 7, pp. 1282-1285.
42. Freund Y, Schapire RE, (1999) Large margin classification using the perceptron algorithm. Machine Learning 37: 277-296.
43. Huang G, Zhu Q, Siew C, (2006) Extreme learning machine: Theory and applications. Neurocomputing 70: 489-501.
44. Luo SX, Axel R, Abbott LF, (2010) Generating sparse and selective third-order responses in the olfactory system of the fly. Proc Natl Acad Sci U S A 107: 10713-10718.
45. Brunel N, (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8: 183-208.
46. Renart A, de la Rocha J, Bartho P, Hollender L, Parga N, et al. (2010) The asynchronous state in cortical circuits. Science 327: 587-590.
47. Harris KD, Thiele A, (2011) Cortical state and attention. Nat Rev Neurosci 12: 509-523.
48. Maass W, Natschlger T, Markram H, (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14: 2531-2560.
49. Buonomano DV, Maass W, (2009) State-dependent computations: spatiotemporal processing in cortical networks. Nat Rev Neurosci 10: 113-125.
50. Buonomano D, Merzenich M, (1995) Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267: 1028-1030.
51. Morrison A, Diesmann M, Gerstner W, (2008) Phenomenological models of synaptic plasticity based on spike timing. Biological cybernetics 98: 459-78.
52. Wang HX, Gerkin RC, Nauen DW, Bi GQ, (2005) Coactivation and timing-dependent integration of synaptic potentiation and depression. Nat Neurosci 8: 187-193.
53. Froemke RC, Dan Y, (2002) Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416: 433-438.
54. Froemke RC, Debanne D, Bi GQ, (2010) Temporal modulation of spike-timing-dependent plasticity. Frontiers in synaptic neuroscience 2: 19.
55. Kirkwood A, Rioult MC, Bear MF, (1996) Experience-dependent modification of synaptic plasticity in visual cortex. Nature 381: 526-528.
56. O'Connor DH, Wittenberg GM, Wang SSH, (2005) Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proceedings of the National Academy of Sciences of the United States of America 102: 9679-84.
57. Ko H, Hofer SB, Pichler B, Buchanan KA, Sjöxström PJ, et al. (2011) Functional specificity of local synaptic connections in neocortical networks. Nature 473: 87-91.
58. Ko H, Cossell L, Baragli C, Antolik J, Clopath C, et al. (2013) The emergence of functional microcircuits in visual cortex. Nature 496: 96-100.
59. Lisman J, (1989) A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc Natl Acad Sci U S A 86: 9574-9578.
60. Shouval HZ, Bear MF, Cooper LN, (2002) A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc Natl Acad Sci U S A 99: 10831-10836.
61. Graupner M, Brunel N, (2012) Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proceedings of the National Academy of Sciences 109: 21551-21551.
62. Nishiyama M, Hong K, Mikoshiba K, Poo MM, Kato K, (2000) Calcium stores regulate the polarity and input specificity of synaptic modification. Nature 408: 584-588.
63. Wittenberg GM, Wang SSH, (2006) Malleability of spike-timing-dependent plasticity at the CA3-CA1 synapse. J Neurosci 26: 6610-6617.
64. Rizzuto R, Pozzan T, (2006) Microdomains of intracellular Ca2+: molecular determinants and functional consequences. Physiological reviews 86: 369-408.
65. Dan Y, Poo MM, (2004) Spike timing-dependent plasticity of neural circuits. Neuron 44: 23-30.
66. Hinton GE, Salakhutdinov RR, (2006) Reducing the dimensionality of data with neural networks. Science 313: 504-507.
67. Lee TS, Mumford D, (2003) Hierarchical bayesian inference in the visual cortex. J Opt Soc Am A Opt Image Sci Vis 20: 1434-1448.
68. Hinton GE, Dayan P, Frey BJ, Neal RM, (1995) The "wake-sleep" algorithm for unsupervised neural networks. Science 268: 1158-1161.
69. Harris KD, (2008) Stability of the fittest: organizing learning through retroaxonal signals. Trends Neurosci 31: 130-136.
70. Watanabe T, Nez JE, Sasaki Y, (2001) Perceptual learning without perception. Nature 413: 844-848.
71. Bear MF, Singer W, (1986) Modulation of visual cortical plasticity by acetylcholine and noradrenaline. Nature 320: 172-176.
72. Takata N, Mishima T, Hisatsune C, Nagai T, Ebisui E, et al. (2011) Astrocyte calcium signaling transforms cholinergic modulation to cortical plasticity in vivo. J Neurosci 31: 18155-18165.
73. Zou Q, Destexhe A, (2007) Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations. Biol Cybern 97: 81-97.
74. Gerstner W, Kempter R, van Hemmen JL, Wagner H, (1996) A neuronal learning rule for submillisecond temporal coding. Nature 383: 76-81.
75. Toyoizumi T, Pfister JP, Aihara K, Gerstner W, (2007) Optimality model of unsupervised spiketiming-dependent plasticity: synaptic memory and weight distribution. Neural Comput 19: 639-671.
76. Legenstein R, Naeger C, Maass W, (2005) What can a neuron learn with spike-timing-dependent plasticity? Neural Comput 17: 2337-2382.
77. Izhikevich EM, (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17: 2443-2452.
78. Sprekeler H, Michaelis C, Wiskott L, (2007) Slowness: an objective for spike-timing-dependent plasticity? PLoS Comput Biol 3: e112.
79. Rao RP, Sejnowski TJ, (2001) Spike-timing-dependent hebbian plasticity as temporal difference learning. Neural Comput 13: 2221-2237.
80. Diesmann M, Gewaltig M, (2001) NEST: An environment for neural systems simulations. Forschung und wisschenschaftliches Rechnen, Beitrage zum Heinz-Biling-Preis 58: 43-70.
81. Davison AP, Brüderle D, Eppler J, Kremkow J, Muller E, et al. (2009) PyNN: A common interface for neuronal network simulators. Front Neuroinformatics 2: 11.
82. Gütig R, Sompolinsky H, (2009) Time-warp-invariant neuronal processing. PLoS Biol 7: e1000141.
83. Gütig R, Sompolinsky H, (2006) The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci 9: 420-428.
84. Florian RV, (2010) The chronotron: a neuron that learns to fire temporally-precise spike patterns. Nature Preceedings.
85. Urbanczik R, Senn W, (2009) A gradient learning rule for the tempotron. Neural Comput 21: 340-352.
86. Turrigiano GG, Nelson SB, (2004) Homeostatic plasticity in the developing nervous system. Nat Rev Neurosci 5: 97-107.
87. van Rossum MC, Bi GQ, Turrigiano GG, (2000) Stable Hebbian learning from spike timingdependent plasticity. J Neurosci 20: 8812-8821.
88. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. (2011) Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12: 2825-2830.