A learning rule to model the development of orientation selectivity in visual cortex

Neural Processing Letters

Volumn 21, Issue 1, 2005, Pages 1-20

  Jerez, J M ^a   Atencia, M ^a   Vico, F J ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Hebbian learning; Orientation selectivity; Receptive fields; Unsupervised learning; Visual cortex modelling

Indexed keywords

HEBBIAN LEARNING; ORIENTATION SELECTIVITY; RECEPTIVE FIELDS; UNSUPERVISED LEARNING; VISUAL CORTEX MODELING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL METHODS; IMAGE ANALYSIS; NEURAL NETWORKS; ROBOTS; VISION;

LEARNING SYSTEMS;

EID: 12844251923     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11063-004-2023-7     Document Type: Article

References (24)

1. Abraham, W. and Goddard, G.: Asymmetric relationships between homosynaptic long-term potentiation and heterosynaptic long-term depression, Nature 305 (1983), 717-719.
2. Amari, S., Cichocki, A. and Yang, H.: A new learning algorithm for blind signal separation, In Advances in Neural Information Processing Systems, Vol. 8. MIT Press, pp. 757-763.
3. Artola, A., Bröcher, S. and Singer, W.: Different voltage-dependent threshold for inducing long-term depression and long-term potentiation in slices of rat visual cortex, Nature 347 (1990), 69-72.
4. Bell, A. and Sejnowski, T.: An information-maximization approach to blind separation and blind deconvolution, Neural Comput. 7(6) (1995), 1129-1159.
5. Bell, A. and Sejnowski, T.: The independent components of natural images are edge filters, Vision Res. 37(23) (1997), 3327-3338.
6. Bienenstock, E., Cooper, L. and Munro, P.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex, J. Neurosci. 2(1) (1982), 32-48.
7. Biais, B.: The role of the environment in synoptic plasticity: towards an understanding of learning and memory, PhD thesis, Brown University.
8. Hubel, D. and Wiesel, T.: Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, J. Physiol. 160 (1962), 106-154.
9. Hubel, D. and Wiesel, T.: Receptive fields and functional architecture of monkey striate cortex, J. Physiol. 195 (1968), 215-243.
10. Hyvärinen, A. and Hoyer, P.: Topographic independent component analysis as a model of VI organization and receptive fields, Neurocomputing 38-40 (2001), 1307-1315.
11. Law, C. and Cooper, L.: Formation of receptive fields according to the BCM theory of synaptic plasiticity in realistic visual environment, Proc. Natl Acad. Sci. 91 (1994), 7797-7801.
12. Linsenmeier, R., Frishman, L., Jakiela, H. and Enroth-Cugell, C.: Receptive field properties of X and Y cells in the cat retina derived from contrast sensitivity measurements, Vision Res. 22 (1982), 1173-1183.
13. Linsker, R.: From basic network principles to neural architecture: emergence of orientation-selective cells, Proc. Natl Acad. Sci. 83 (1986), 8390-8394.
14. Lynch, G., Dunwiddie, T. and Gribkoff, V.: Heterosynaptic depression: a postsynaptic correlate of long-term potentiation, Nature 266 (1977), 737-739.
15. Miller, K.: 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, J. Neurosci. 14 (1994), 409-441.
16. Miller, K.: Synaptic economics: competition and cooperation in correlation-based synaptic plasticity, Neuron 17 (1996), 371-374.
17. Neskovic, P. and Verschure, P.: Evaluation of a biologically realistic learning rule, BCM, in an active vision system, In Telluride Workshop on Neuromorphic Engineering.
18. Oja, E.: A simplified neuron model as a principal component analyzer, Math. Biol. 15 (1982), 267-273.
19. Sejnowsky, T.: Storing covariance with nonlinearly interacting neurons, J. Math. Biol. 4 (1977), 303-321.
20. Shouval, H. and Liu, Y.: Principal component neurons in a realistic visual environment, Network 7(3) (1996), 501-515.
21. Vico, F. and Jerez, J.: Stable neural attractors formation: learning rules and network dynamics, Neural Process. Lett. 18 (2003), 1-16.
22. Vidyasagar, X. and Mathukumalli, X.: Nonlinear Systems Analysis. Prentice Hall, Englewood Cliffs, NJ, 1993.
23. Von der Malsburg, C.: Self-organization of orientation sensivity cells in striate cortex, Kybernetik 14 (1973), 85-100.
24. Zhang, L. and Mei, J.: Shaping up simple cell's receptive field of animal vision by ICA and its application in navigation system, Neural Networks 16 (2003), 609-615.