Exploring the role of intrinsic plasticity for the learning of sensory representations

ESANN 2006 Proceedings - European Symposium on Artificial Neural Networks

Volumn , Issue , 2006, Pages 467-472

  Butko, Nicholas J ^a   Triesch, Jochen ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b FRANKFURT INSTITUTE FOR ADVANCED STUDIES   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INTERNET PROTOCOLS; NEURONS; VECTOR SPACES;

CORTICAL NEURONS; COUPLED MODELING; INTRINSIC PLASTICITY; NON-LINEAR ICA; PRIMARY VISUAL CORTEX; RECEPTIVE FIELDS; SIMPLE MODELING; SYNAPTIC PLASTICITY;

NEURAL NETWORKS;

EID: 84918541962     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. W. Zhang and D. J. Linden, The other side of the engram: experience-dependent changes in neuronal intrinsic excitability. Nature Reviews Neuroscience, 4:885-900, 2003.
2. N. S. Desai, L. C. Rutherford and G. Turrigiano, Plasticity in the intrinsic excitability of cortical pyramidal neurons, Nature Neuroscience, 2(6):515:520, 1999.
3. R. Baddeley, L. F. Abbott, M. C. Booth, F. Sengpiel, T. Freeman, E. A. Wakeman, and E. T. Rolls, Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. Roy. Soc. of Lond. B, 264:1775-1783, 1998.
4. M. Stemmler and C. Koch, How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neuroscience, 2:521-527, 1999.
5. J. Triesch, Synergies between Intrinsic and Synaptic Plasticity in Individual Neurons, Advances in Neural Information Processing Systems, 17:1417-1424, 2005.
6. R. Linsker, From basic network principles to neural architecture: emergence of oriented columns. Proceedings of the National Academies of Science, 83:8779-8783, 1986.
7. K. D. Miller, A Model for the Development of Simple Cell Receptive Fields and the Ordered Arrangement of Orientation Columns through Activity-dependent Competition between ON- and OFF-Center Inputs, The Journal of Neuroscience, 14(1):409-441, 1994.
8. S. Laughlin, A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch 36:910-912, 1981.
9. A. J. Bell and T. J. Sejnowski, The “Independent Components” of Scenes are Edge Filters. Vision Research, 37(23):3327-3338, 1997.
10. B. A. Olshausen and D. J. Field, Sparse Coding with an Overcomplete Basis Set: A strategy employed by V1? Vision Research, 37(23):3311-3325, 1997.
11. D. L. Ruderman and W. Bialek, Statistics of natural images: scaling in the woods, Physical Review Letters 73, 1994.
12. L. N. Cooper, N. Intrator, B. S. Blais, and H. Z Shouval. Theory of Cortical Plasticity, World Scientific, London, 2004.
13. J. Triesch, A Gradient Rule for the Plasticity of a Neuron's Intrinsic Excitability, Proc. Int. Conf. on Artificial Neural Networks 65-70, 2005.
14. P. Földiák, Forming sparse representation by local anti-hebbian learning, Biological Cybernetics, 64:165-170, 1990.
15. J. H. van Hateren and A. van der Schaaf, Independent component filters of natural images comparsed with simple cells in primary visual cortex. Proc. Roy. Soc. of Lond. B, 265:359-366, 1998.