Abstract
In this paper we briefly summarize the fundamental properties of spike events processing applied to artificial vision systems. This sensing and processing technology is capable of very high speed throughput, because it does not rely on sensing and processing sequences of frames, and because it allows for complex hierarchically structured neuro-cortical-like layers for sophisticated processing. The paper describes briefly cortex-like spike event vision processing principles, and the AER (Address Event Representation) technique used in hardware spiking systems. In this paper we present a simulation AER tool that we have developed entirely in Visual C++ 6.0. We have validated it using real AER stimulus and comparing the outputs with real outputs obtained from AER-based devices. With this tool we can predict the eventual performance of AER-based systems, before the technology becomes mature enough to allow such large systems.
This is a preview of subscription content, log in via an institution to check access.
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
Shepherd, G.M.: The Synaptic Organization of the Brain, 3rd edn. Oxford University Press, Oxford (1990)
Rolls, E.T., Deco, G.: Computational Neuroscience of Vision. Oxford University Press, Oxford (2002)
LeCun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time Series. In: Arbib, M. (ed.) The Handbook of Brain Science and Neural Networks, pp. 255–258. MIT Press, Cambridge (1995)
Fasel, B.: Robust Face Analysis using Convolution Neural Networks. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR 2002), Quebec, Canada (2002)
Sivilotti, M.: Wiring Considerations in Analog VLSI Systems with Application to Field-Programmable Networks, Ph.D. Thesis, California Institute of Technology, Pasadena CA (1991)
Mahowald, M.: VLSI Analogs of Neural Visual Processing: A Synthesis of Form and Function, Ph.D. Thesis, California Institute of Technology, Pasadena CA (1992)
Cauwenberghs, G., Kumar, N., Himmelbauer, W., Andreou, A.G.: An analog VLSI Chip with Asynchronous Interface for Auditory Feature Extraction. IEEE Trans. Circ. Syst. Part-II 45, 600–606 (1998)
Oster, M., Liu, S.-C.: Spiking Inputs to a Spiking Winner-Take-All Circuit. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems (NIPS 2006), vol. 18, pp. 1051–1058. MIT Press, Cambridge (2006)
Lichtsteiner, P., Delbrück, T.: 64x64 AER Logarithmic Temporal D rivative Silicon Retina. Research in Microelectronics and Electronics 2, 202–205 (2005)
Serrano-Gotarredona, R., et al.: AER Building Blocks for Multi-Layers Multi-Chips Neu-romorphic Vision Systems. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems (NIPS 2006), vol. 18, pp. 1217–1224. MIT Press, Cambridge (2006)
Serrano-Gotarredona, R., Serrano-Gotarredona, T., Acosta-Jiménez, A., Linares-Barranco, B.: A Neuromorphic Cortical Layer Microchip for Spike Based Event Processing Vi-sion Systems. IEEE Trans. on Circuits and Systems, Part-I 53(12), 2548–2566 (2006)
Serrano-Gotarredona, R., et al.: On Real-Time AER 2D Convolutions Hardware for Neu-romorphic Spike Based Cortical Processing. IEEE Trans. on Neural Networks 19(7), 1196–1219 (2008)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 411–426 (2007)
Masquelier, T., Thorpe, S.J.: Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. PLoS Comput. Biol. 3(2), e31 (2007)
The MNIST database, http://yann.lecun.com/exdb/mnist/index.html
Linares-Barranco, A., Jimenez-Moreno, G., Linares-Barranco, B., Civit-Ballcels, A.: On Algorithmic Rate-Coded AER Generation. IEEE Trans. on Neural Networks 17(3), 771–788 (2006)
Le Cun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1, 541–551 (1989)
Le Cun, Y., et al.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez-Carrasco, JA., Serrano-Gotarredona, C., Acha-Piñero, B., Serrano-Gotarredona, T., Linares-Barranco, B. (2009). Advanced Vision Processing Systems: Spike-Based Simulation and Processing. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-04697-1_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04696-4
Online ISBN: 978-3-642-04697-1
eBook Packages: Computer ScienceComputer Science (R0)