Advanced Vision Processing Systems: Spike-Based Simulation and Processing

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.

[1]  Timothée Masquelier,et al.  Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..

[2]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[3]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[4]  G. Shepherd The Synaptic Organization of the Brain , 1979 .

[5]  Bernabé Linares-Barranco,et al.  On algorithmic rate-coded AER generation , 2006, IEEE Transactions on Neural Networks.

[6]  Bernabé Linares-Barranco,et al.  A Neuromorphic Cortical-Layer Microchip for Spike-Based Event Processing Vision Systems , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[8]  Massimo A. Sivilotti,et al.  Wiring considerations in analog VLSI systems, with application to field-programmable networks , 1992 .

[9]  Beat Fasel,et al.  Robust face analysis using convolutional neural networks , 2002, Object recognition supported by user interaction for service robots.

[10]  Bernabé Linares-Barranco,et al.  On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing , 2008, IEEE Transactions on Neural Networks.

[11]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[12]  Gert Cauwenberghs,et al.  An analog VLSI chip with asynchronous interface for auditory feature extraction , 1998 .

[13]  T. Delbruck,et al.  A 64x64 aer logarithmic temporal derivative silicon retina , 2005, Research in Microelectronics and Electronics, 2005 PhD.

[14]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.