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Original Articles

Real-time simulation of large-scale neural architectures for visual features computation based on GPU

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Pages 272-291 | Received 15 Jun 2012, Accepted 03 Oct 2012, Published online: 01 Nov 2012

References

  • Adelson E, Bergen J. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Amer. 1985; 2: 284–321
  • Adelson E, Bergen J. The plenoptic function and the elements of early vision. Computational models of visual processing, M Landy, J Movshon. MIT Press, Cambridge, MA 1991; 3–20
  • Brette R, Goodman DFM. Vectorized algorithms for spiking neural network simulation. Neural Computation 2011; 23(6)1503–1535
  • Brumby SP, Galbraith AE, Ham M, Kenyon G, George JS. 2010. Visual cortex on a chip: Large-scale, real-time functional models of mammalian visual cortex on a GPGPU. Proceedings of the GPU Technology Conference (GTC), San Jose, CA.
  • Burt P, Adelson E. The laplacian pyramid as a compact image code. IEEE Trans. Commun. 1983; 31: 532–540
  • Carandini M, Heeger DJ. Normalization as a canonical neural computation. Nat Rev Neurosci 2012; 13(1)51–62
  • Chen Y, Qian N. A coarse-to-fine disparity energy model with both phase-shift and position-shift receptive field mechanisms. Neural Computation 2004; 16: 1545–1577
  • Chessa M, Sabatini S, Solari F. 2009a. A fast joint bioinspired algorithm for optic flow and two-dimensional disparity estimation. International Conference on Computer Vision Systems (ICVS), 184–193.
  • Chessa M, Solari F, Sabatini S. 2009b. A virtual reality simulator for active stereo vision systems. Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, 444–449.
  • Daugman J. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Amer. 1985; A/2: 1160–1169
  • Deneve S, Pouget A, Latham P. Divisive normalization, line attractor networks and ideal observers. Advances in neural processing systems. Proceedings of 7th IEEE Workshop on Embedded Computer Vision, J Kearns, Michael, A Solla, Sara, A Cohn, and David. The MIT Press, Cambridge, MA 1999; 104–110
  • Douglas RJ, Martin KA. A functional microcircuit for cat visual cortex. The Journal of Physiology 1991; 440(1)735–769
  • Everson RM, Prashanth AK, Gabbay M, Knight BW, Sirovich L, Kaplan E. Representation of spatial frequency and orientation in the visual cortex. Proceedings of the National Academy of Sciences of the United States of America 1998; 95(14)8334–8338
  • Farell B. Two-dimensional matches from one dimensional stimulus components in human stereopsis. Nature 1998; 395(6703)689–693
  • Ferreira JF, Lobo J, Dias J. Bayesian real-time perception algorithms on GPU. J. Real-Time Image Process. 2011; 6(3)171–186
  • Fleet D, Wagner H, Heeger D. Neural encoding of binocular disparity: Energy models, position shifts and phase shifts. Vision Research 1996; 36(12)1839–1857
  • Heeger DJ. Normalization of cell responses in cat striate cortex. Visual neuroscience 1992; 9(2)181–197
  • Humenberger M, Zinner C, Weber M, Kub, inger W, Vincze M. A fast stereo matching algorithm suitable for embedded real-time systems. Comput. Vis. Image Underst. 2010; 114(11)1180–1202
  • Kloeckner A, Pinto N, Lee Y, Catanzaro B, Ivanov P, Fasih A. PyCUDA and PyOpenCL: A scripting- based approach to GPU run-time code generation. Parallel Computing 2012; 38(3)157–174
  • Kouh M, Poggio T. A canonical neural circuit for cortical nonlinear operations. Neural Computation 2008; 20(6)1427–1451
  • Mattoccia S, Viti M, Ries F. Near real-time Fast Bilateral Stereo on the GPU. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, 2011 June 20–25. Colorado Springs, CO, pp. 136–143. doi: 10.1109/CVPRW.2011.5981835.
  • Morgan MJ, Castet E. The aperture problem in stereopsis. Vision Research 1997; 37(19)2737–2744
  • Movshon JA, Thompson ID, Tolhurst DJ. Spatial summation in the receptive fields of simple cells in the cat's striate cortex. The Journal of physiology 1978; 283: 53–77
  • Mutch J, Knoblich U, Poggio T. 2010. CNS: A GPU-based framework for simulating cortically-organized networks. Technical Report MIT-CSAIL-TR-2010-013 / CBCL-286, Massachusetts Institute of Technology, Cambridge, MA.
  • Nere A, Hashmi A, Lipasti M. 2011. Profiling heterogeneous multi-GPU systems to accelerate cortically inspired learn- ing algorithms. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS 2011).
  • NVIDIA (2012). NVIDIA CUDA C Programming Guide 4.2.
  • Ohzawa I, DeAngelis G, Freeman R. Stereoscopic depth discrimination in the visual cortex: Neurons ideally suited as disparity detectors. Science 1990; 249: 1037–1041
  • Pauwels K, Tomasi M, Diaz J, Ros E, Hulle MMV. A comparison of FPGA and GPU for real-time phase-based optical flow, stereo, and local image features. IEEE Transactions on Computers 2012; 61: 999–1012
  • Pinto N, Cox DD. GPU metaprogramming: A case study in biologically-inspired computer vision. GPU Computing Gems, Jade Edition. Morgan Kaufmann Publishers, Waltman, MA 2012
  • Pouget A, Dayan P, Zemel R. Informa- tion processing with population codes. Nat Rev Neurosci 2000; 1(2)125–132
  • Pouget A, Dayan P, Zemel RS. Inference and computation with population codes. Annu Rev Neurosci 2003; 26: 381–410
  • Pouget A, Zhang K, Deneve S, Latham PE. Statistically efficient estimation using population coding. Neural Computation 1998; 10(2)373–401
  • Priebe NJ, Ferster D. Inhibition, spike threshold, and stimulus selectivity in primary visual cortex. Neuron 2008; 57(4)482–497
  • Qian N, Zhu Y. Physiological computation of binocular disparity. Vision Research 1997; 37: 1811–1827
  • Richert M, Nageswaran JM, Dutt N, Krichmar JL. An effcient simulation environment for modeling large-scale cortical processing. Frontiers in Neuroinformatics 2011; 5: 1–15
  • Rust NC, Mante V, Simoncelli EP, Movshon JA. How MT cells analyze the motion of visual patterns. Nature Neuroscience 2006; 9(11)1421–1431
  • Salinas E, Abbott L. Vector reconstruction from firing rates. J. Neuroscience 1994; 1: 89–107
  • Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Journal of Computer Vision 2002; 47(1/2/3)7–42
  • Serre T, Oliva A, Poggio T. A feed forward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences 2007; 104(15)6424–6429
  • Simoncelli EP, Heeger DJ. A model of neuronal responses in visual area MT. Vision Res. 1998; 38(5)743–761
  • Solomon SG, Lee BB, Sun H. Suppressive surrounds and contrast gain in magnocellular-pathway retinal ganglion cells of macaque. Journal of Neuroscience 2006; 26(34)8715–8726
  • Theimer W, Mallot H. Phase- based binocular vergence control and depth reconstruction using active vision. Computer Vision, Graphics and Image Processing: Image Understanding 1994; 60(3)343–358
  • Wang X, Shi BE. 2010. GPU implementation of fast Gabor filters. Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, 373–376.
  • Woodbeck K, Roth G, Chen H. 2008. Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition. In Computer Vision on GPU (2008), 1–8.
  • Zhao Y, Taubin G. Real-time stereo on GPGPU using progressive multi-resolution adaptive windows. Image Vision Comput. 2011; 29(6)420–432

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