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164 nips-2009-No evidence for active sparsification in the visual cortex


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Author: Pietro Berkes, Ben White, Jozsef Fiser

Abstract: The proposal that cortical activity in the visual cortex is optimized for sparse neural activity is one of the most established ideas in computational neuroscience. However, direct experimental evidence for optimal sparse coding remains inconclusive, mostly due to the lack of reference values on which to judge the measured sparseness. Here we analyze neural responses to natural movies in the primary visual cortex of ferrets at different stages of development and of rats while awake and under different levels of anesthesia. In contrast with prediction from a sparse coding model, our data shows that population and lifetime sparseness decrease with visual experience, and increase from the awake to anesthetized state. These results suggest that the representation in the primary visual cortex is not actively optimized to maximize sparseness. 1


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[1] D.J. Field. What is the goal of sensory coding? Neural Computation, 6(4):559–601, 1994.

[2] B.A. Olshausen and D.J. Field. Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14(4):481–487, 2004.

[3] B.A. Olshausen and D.J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607–609, 1996.

[4] A.J. Bell and T.J. Sejnowski. The ‘independent components’ of natural scenes are edge filters. Vision Research, 37(23):3327–3338, 1997.

[5] J.H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. B, 265:359–366, 1998.

[6] A.S. Hsu and P. Dayan. An unsupervised learning model of neural plasticity: Orientation selectivity in goggle-reared kittens. Vision Research, 47(22):2868–2877, 2007.

[7] R. Baddeley, L.F. Abbott, M.C.A. Booth, F. Sengpiel, T. Freeman, E. Wakeman, and E.T. Rolls. Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proceedings of the Royal Society B: Biological Sciences, 264(1389):1775–1783, 1997.

[8] W.E. Vinje and J.L. Gallant. Sparse coding and decorrelation in primary visual cortex during natural vision. Science, 297(5456):1273–1276, 2000.

[9] M. Weliky, J. Fiser, R.H. Hunt, and D.N. Wagner. Coding of natural scenes in primary visual cortex. Neuron, 37(4):703–718, 2003.

[10] S.R. Lehky, T.J. Sejnowski, and R. Desimone. Selectivity and sparseness in the responses of striate complex cells. Vision Research, 45(1):57–73, 2005. 8

[11] S.C. Yen, J. Baker, and C.M. Gray. Heterogeneity in the responses of adjacent neurons to natural stimuli in cat striate cortex. Journal of Neurophysiology, 97(2):1326–1341, 2007.

[12] D.J. Tolhurst, D. Smyth, and I.D. Thompson. The sparseness of neuronal responses in ferret primary visual cortex. Journal of Neuroscience, 29(9):2355–2370, 2009.

[13] A. Treves and E.T. Rolls. What determines the capacity of autoassociative memories in the brain? Network: Computation in Neural Systems, 2(4):371–397, 1991.

[14] P. Foldiak and D. Endres. Sparse coding. Scholarpedia, 3(1):2984, 2008.

[15] B. Willmore and D.J. Tolhurst. Characterizing the sparseness of neural codes. Network: Computation in Neural Systems, 12:255–270, 2001.

[16] S. Osindero, M. Welling, and G.E. Hinton. Topographic product models applied to natural scene statistics. Neural Computation, 18:381–344, 2006.

[17] P. Berkes, R. Turner, and M. Sahani. On sparsity and overcompleteness in image models. In Advances in Neural Information Processing Systems, volume 20. MIT Press, 2008. Cambridge, MA.

[18] P.O. Hoyer and A. Hyvarinen. Interpreting neural response variability as monte carlo sampling of the posterior. In Advances in Neural Information Processing Systems, volume 15. MIT Press, 2003. Cambridge, MA.

[19] T.S. Lee and D. Mumford. Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20(7):1434–1448, 2003.

[20] P. Berkes, G. Orban, M. Lengyel, and J. Fiser. Matching spontaneous and evoked activity in V1: a hallmark of probabilistic inference. Frontiers in Systems Neuroscience, 2009. Conference Abstract: Computational and systems neuroscience.

[21] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Science, 220:671– 680, 1983.

[22] B. Chapman and M.P. Stryker. Development of orientation selectivity in ferret visual cortex and effects of deprivation. Journal of Neuroscience, 13:5251–5262, 1993.

[23] L.E. White, D.M. Coppola, and D. Fitzpatrick. The contribution of sensory experience to the maturation of orientation selectivity in ferret visual cortex. Nature, 411:1049–1052, 2001.

[24] P.H. Schiller, B.L. Finlay, and S.F. Volman. Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. Journal of Neurophysiology, 39(6):1288–1319, 1976.

[25] D.M. Snodderly and M. Gur. Organization of striate cortex of alert, trained monkeys (Macaca fascicularis): ongoing activity, stimulus selectivity, and widths of receptive field activating regions. Journal of Neurophysiology, 74(5):2100–2125, 1995.

[26] V.A.F. Lamme, K. Zipser, and H. Spekreijse. Figure-ground activity in primary visual cortex is suppressed by anesthesia. PNAS, 95:3263–3268, 1998.

[27] K. Guo, P.J. Benson, and C. Blakemore. Pattern motion is present in V1 of awake but not anaesthetized monkeys. European Journal of Neuroscience, 19:1055–1066, 2004.

[28] O. Detsch, C. Vahle-Hinz, E. Kochs, M. Siemers, and B. Bromm. Isoflurane induces dose-dependent changes of thalamic somatosensory information transfer. Brain Research, 829:77–89, 1999.

[29] H. Hentschke, C. Schwarz, and A. Bernd. Neocortex is the major target of sedative concentrations of volatile anaesthetics: strong depression of firing rates and increase of GABA-A receptor-mediated inhibition. European Jounal of Neuroscience, 21(1):93–102, 2005.

[30] P. Dayan and L.F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, 2001.

[31] C.J. Rozell, D.H. Johnson, R.G. Baraniuk, and B.A. Olshausen. Sparse coding via thresholding and local competition in neural circuits. Neural Computation, 20:2526–2563, 2008.

[32] J.J. Atick. Could information theory provide an ecological theory of sensory processing? Computation in Neural Systems, 3(2):213–251, 1992. Network:

[33] V. Balasubramanian and M.J. Berry. Evidence for metabolically efficient codes in the retina. Network: Computation in Neural Systems, 13(4):531–553, 2002.

[34] Y. Karklin and M.S. Lewicki. A hierarchical bayesian model for learning non-linear statistical regularities in non-stationary natural signals. Neural Computation, 17(2):397–423, 2005.

[35] M.J. Wainwright and E.P. Simoncelli. Scale mixtures of gaussians and the statistics of natural images. In Advances in Neural Information Processing Systems. MIT Press, 2000. Cambridge, MA. 9