Abstract
We set up a combined model of sparse coding bottom-up feature detectors and a subsequent attractor with horizontal weights. It is trained with filtered grey-scale natural images. We find the following results on the connectivity: (i) the bottom-up connections establish a topographic map where orientation and frequency are represented in an ordered fashion, but phase randomly. (ii) the lateral connections display local excitation and surround inhibition in the feature spaces of position, orientation and frequency. The results on the attractor activations after an interrupted relaxation of the attractor cells as a response to a stimulus are: (i) Contrast-response curves measured as responses to sine gratings increase sharply at low contrasts, but decrease at higher contrasts (as reported for cells which are adapted to low contrasts [1]). (ii) Orientation tuning curves of the attractor cells are more peaked than those of the feature cells. They have reasonable contrast invariant tuning widths, however, the regime of gain (along the contrast axis) is small before saturation is reached. (iii) The optimal response is roughly phase invariant, if the attractor is trained to predict its input when images move slightly.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Carandini and D. Ferster. Membrane potential and firing rate in cat primary visual cortex. J. Neurosci., 20(1):470–84, 2000.
P. Dayan, G. E. Hinton, R. Neal, and R. S. Zemel. The Helmholtz machine. Neur. Comp., 7:1022–1037, 1995.
P. Dayan. Recurrent sampling models for the Helmholtz machine. Neur. Comp. 11:653–77, 2000.
G.C. DeAngelis, G.M. Ghose, I. Ohzawa, and R.D. Freeman. Functional micro-organization of primary visual cortex: Receptive field analysis of nearby neurons. J. Neurosci., 19(9):4046–64, 2000.
S. Deneve, P.E. Latham, and A. Pouget. Reading population codes: a neural implementation of ideal observers. Nature Neurosci., 2(8):740–5, 1999.
D.W. Dong. Associative decorrelation dynamics: A theory of self-organization and optimization in feedback networks. In Proceedings of NIPS 7, pages 925–32, 1994.
A. Hyvärinen and P.O. Hoyer. Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces. Neur. Comp., 12:1705–20, 2000.
N. Mayer, J.M. Herrmann, H.U. Bauer, and T. Geisel. A cortical interpretation of ASSOMs. In Proceedings ICANN, pages 961–966, 1998.
B.A. Olshausen and D.J. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37:3311–3325, 1997.
J. Sirosh and R. Miikkulainen. Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neur. Comp., 9:577–94, 1997.
K. Zhang. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory. J. Neurosci., 16:2112–26, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weber, C. (2001). Self-Organization of Orientation Maps, Lateral Connections, and Dynamic Receptive Fields in the Primary Visual Cortex. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_160
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_160
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive