68
Views
7
CrossRef citations to date
0
Altmetric
Original Articles

Hebbian learning in a model with dynamic rate-coded neurons: An alternative to the generative model approach for learning receptive fields from natural scenes

&
Pages 249-266 | Received 13 Mar 2007, Accepted 04 Sep 2007, Published online: 09 Jul 2009

References

  • Atick JJ, Redlich A. Towards a theory of early visual processing. Neural Comput 1990; 2: 308–320
  • Armstrong KM, Fitzgerald JK, Moore T. Changes in visual receptive fields with microstimulation of frontal cortex. Neuron 2006; 50: 791–798
  • Barlow HB. Possible principles underlying the transformation of sensory messages. Sensory communication, WA Rosenblith. MIT Press, Cambridge, MA 1961; 217–234
  • Barlow HB. Redundancy reduction revisited. Network 1998; 12: 241–253
  • Bayerl P, Neumann H. Disambiguating visual motion through contextual feedback modulation. Neural Comput 2004; 16: 2041–2066
  • Bell AJ, Sejnowski TJ. The ‘independent components’ of natural scenes are edge filters. Vis Res 1997; 37: 3327–3338
  • Bullier J, Hupe JM, James AC, Girard P. The role of feedback connections in shaping the responses of visual cortical neurons. Prog Brain Res 2001; 134: 193–204
  • David SV, Vinje WE, Gallant JL. Natural stimulus statistics alter the receptive field structure of v1 neurons. J Neurosci 2004; 24: 6991–7006
  • Eckhorn R, Reitboeck E, Arndt M, Dicke P. Feature linking via synchronisation among distributed assemblies: Simulations of results from Cat Visual Cortex. Neural Comput 1990; 2: 293–307
  • Falconbridge MS, Stamps RL, Badcock DR. A simple Hebbian/anti-Hebbian network learns the sparse, independent components of natural images. Neural Comput 2006; 18: 415–429
  • Grossberg S. How does the brain build a cognitive code?. Psychol Rev 1980; 87: 1–51
  • Hamker FH. A dynamic model of how feature cues guide spatial attention. Vision Res 2004; 44: 501–521
  • Hamker FH. The reentry hypothesis: The putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. Cereb Cortex 2005; 15: 431–447
  • Hamker FH. Modeling feature-based attention as an active top-down inference process. BioSystems 2006; 86: 91–99
  • Hamker FH. The mechanisms of feature inheritance as predicted by a systems-level model of visual attention and decision making. Adv Cogn Psychol 2007; 3: 111–123
  • Hancock PJB, Baddeley RJ, Smith LS. The principle components of natural images. Network 1992; 3: 61–70
  • Harpur G, Prager R. Development of low entropy coding in a recurrent network. Network: Comput Neural Syst 1996; 7: 277–284
  • Hoyer PO. Modeling receptive fields with non-negative sparse coding. Neurocomputing 2003; 52–54: 547–552
  • Hoyer PO. Non-negative matrix factorization with sparseness constraints. J Mach Learn Res 2004; 5: 1457–1469
  • Hoyer PO, Hyvärinen A. A multi-layer sparse coding network learns contour coding from natural images. Vision Res 2002; 42: 1593–1605
  • Jehee JF, Rothkopf C, Beck JM, Ballard DH. Learning receptive fields using predictive feedback. J Physiol Paris 2006; 100: 125–132
  • Karklin Y, Lewicki MS. Learning higher-order structures in natural images. Network 2003; 14: 483–499
  • Lammé VAF, Roelfsema PR. The distinct modes of vision offered by feedforward and recurrent processing. Trend Neurosci 2000; 23: 571–579
  • Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature 1999; 401: 788–791
  • Li Z, Atick JJ. Towards a theory of striate cortex. Neural Comput 1994; 6: 127–146
  • Linsker R. From basic network principles to neural architecture: Emergence of orientation-selective cells. Proc Natl Acad Sci USA 1986; 83: 8390–8394
  • Nadal J-P, Parga N. Nonlinear neurons in the low-noise limit: A factorial code maximizes information transfer. Network: Comput Neural Sys 1994; 5: 565–581
  • O’Connor DH, Fukui MM, Pinsk MA, Kastner S. Attention modulates responses in the human lateral geniculate nucleus. Nat Neurosci 2002; 5: 1203–1209
  • Oja E. A simplified neuron model as a principal component analyzer. J Math Biol 1982; 15: 267–273
  • Olshausen BA, Field DJ. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 1996; 381: 607–609
  • Olshausen BA, Field DJ. Sparse coding with an overcomplete basis set: A strategy employed by V1?. Vision Res 1997; 37: 3311–3325
  • Rao RP, Ballard DH. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 1999; 2: 79–87
  • Rehn M, Sommer FT. A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. J Comput Neurosci 2007; 22: 135–146
  • Reynolds JH, Chelazzi L. Attentional modulation of visual processing. Annu Rev Neurosci 2004; 27: 611–647
  • Ringach DL. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. J Neurophysiol 2002; 88: 455–463
  • Rockland KS, van Hoesen GW. Direct temporal-occipital feedback connections to striate cortex (V1) in the macaque monkey. Cereb Cortex 1994; 4: 300–313
  • Rockland KS, Saleem KS, Tanaka K. Divergent feedback connections from areas V4 and TEO in the macaque. Visual Neurosci 1994; 11: 579–600
  • Sejnowski T. Storing covariance with nonlinearly interacting neurons. J Math Biol 1977; 4: 303–321
  • Simoncelli EP. Vision and the statistics of the visual environment. Curr Opin Neurobiol 2003; 13: 144–149
  • Sommer MA, Wurtz RH. What the brain stem tells the frontal cortex. I. Oculomotor signals sent from superior colliculus to frontal eye field via mediodorsal thalamus. J Neurophysiol 2004; 91: 1381–1402
  • Spratling MW, Johnson MH. Pre-integration lateral inhibition enhances unsupervised learning. Neural Comput 2002; 14: 2157–2179
  • Turrigiano GG, Nelson SB. Homeostatic plasticity in the developing nervous system. Nat Rev Neurosci 2004; 5: 97–107
  • van Hateren JH, van der Schaaf A. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc Biol Sci 1998; 265: 359–366
  • von der Marlsburg C. Self-organization of orientation selective cells in the striate cortex. Kybernetic 1973; 14: 85–100
  • Willshaw DJ, Dayan P. Optimal plasticity in matrix memories: What goes up must come down. Neural Comput 1990; 2: 85–93
  • Wiltschut J, Zirnsak M, Hamker FH. (in preparation) Hebbian learning of feedforward and feedback connections in dynamic rate coded neurons.
  • Yu A, Giese MA, Poggio T. Biophysiologically plausible implementations of the maximum operation. Neural Comput 2002; 14: 2857–2881

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.