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This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access Poster presentation

Biologically realistic excitatory and inhibitory cell properties emerge from a sparse coding network

Mengchen Zhu1* and Christopher J Rozell2

Author Affiliations

1 Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA

2 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

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BMC Neuroscience 2012, 13(Suppl 1):P55  doi:10.1186/1471-2202-13-S1-P55

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/13/S1/P55


Published:16 July 2012

© 2012 Zhu and Rozell; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Neurons in the primary visual cortex exhibit a baffling array of tuning properties, often unaccountable by the classical linear feedforward model. Specifically, excitatory neurons display a number of nonlinear effects collectively known as non-classical receptive field (nCRF) effects [1], and inhibitory neurons have diverse orientation tuning properties [2]. Furthermore, excitatory cells outnumber inhibitory cells by a ratio of 9:1 [3], yet the excitatory and inhibitory drives are balanced.

Efficient coding models of early vision have been shown to be able to explain key features of linear filtering properties [4] and some single cell nonlinear effects [5]. However, population statistics of nonlinear properties have not been studied in these models. In addition, inhibitory cells were not typically modeled.

Here we demonstrate that many of the aforementioned excitatory cell and inhibitory cell properties emerge naturally from a network that implements sparse coding. To be specific, several nCRF effects including surround suppression, contrast invariant orientation tuning, and cross orientation suppression emerge in the excitatory cell population as a result of sparse coding strategy; the excitatory to inhibitory cell ratio could be understood largely as a result of the overcompletness of representation; moreover, a subpopulation of inhibitory interneurons exhibit orientation tuning due to sparse recurrent connections with the principal cells; another subpopulation displays untuned properties due to low rank connectivity patterns. We also demonstrate that the network exhibits balanced excitation and inhibition, as a result of the receptive field structure.

We simulated a population of 2048 excitatory neurons with graded response described by the dynamics of locally competitive algorithm (LCA; [6]), which converges to the sparse coding representation at steady state. Inhibitory interneurons were described by linear units. The low rank and sparse recurrent connectivity pattern was a result of low rank plus sparse decomposition [7] of the LCA connectivity matrix. Non-classical receptive field effects were studied by presenting bar and drifting grating stimuli to the simulated network. Receptive fields of the inhibitory cells were mapped by sparse dots patterns.

References

  1. Seriès P, Lorenceau J, Frégnac Y: The "silent" surround of V1 receptive fields: theory and experiments.

    Journal of physiology-Paris 2003, 97:453-474. Publisher Full Text OpenURL

  2. Hirsch JA, Martinez LM, Pillai C, Alonso J-M, Wang Q, Sommer FT: Functionally distinct inhibitory neurons at the first stage of visual cortical processing.

    Nat Neurosci 2003, 6:1300-1308. PubMed Abstract | Publisher Full Text OpenURL

  3. Meyer HS, Schwarz D, Wimmer VC, Schmitt AC, Kerr JND, Sakmann B, Helmstaedter M: Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A.

    Proceedings of the National Academy of Sciences 2011, 108:16807-16812. Publisher Full Text OpenURL

  4. Olshausen B, Field D: Sparse coding with an overcomplete basis set: A strategy employed by V1?

    Vision research 1997, 37:3311-3325. PubMed Abstract | Publisher Full Text OpenURL

  5. Zhu M, Rozell CJ: Sparse coding models demonstrate some non-classical receptive field effects.

    Nineteenth Annual Computational Neuroscience Meeting 2010. OpenURL

  6. Rozell C, Johnson D, Baraniuk R, Olshausen B: Sparse coding via thresholding and local competition in neural circuits.

    Neural Computation 2008, 20:2526-2563. PubMed Abstract | Publisher Full Text OpenURL

  7. Lin Z, Chen M, Ma Y: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices.

    ArXiv e-prints 2010. OpenURL