Massively distributed digital implementation of an integrate-and-fire LEGION network for visual scene segmentation

Despite several previous studies, little progress has been made in building successful neural systems for image segmentation in digital hardware. Spiking neural networks offer an opportunity to develop models of visual perception without any complex structure based on multiple neural maps. Such models use elementary asynchronous computations that have motivated several implementations on analog devices, whereas digital implementations appear as quite unable to handle large spiking neural networks, for lack of density. Recent results show that this trend is now counterbalanced by FPGA technological improvements and new implementation schemes. In this work, we consider a model of integrate-and-fire neurons organized according to the standard LEGION architecture to segment gray-level images. Taking advantage of the local and distributed structure of the model, a massively distributed implementation on FPGA using pipelined serial computations is developed. Results show that digital and flexible solutions may efficiently handle large networks of spiking neurons. (c) 2007 Elsevier B.V. All rights reserved.