A neural cocktail-party processor
Biological Cybernetics
Using and designing massively parallel computers for artificial neural networks
Journal of Parallel and Distributed Computing - Special issue on neural computing on massively parallel processing
Image segmentation based on oscillatory correlation
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Architecture of Oscillatory Neural Network for Image Segmentation
SBAC-PAD '02 Proceedings of the 14th Symposium on Computer Architecture and High Performance Computing
Hardware spiking neural network with run-time reconfigurable connectivity in
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
A novel FPGA architecture supporting wide shallow memories
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Synaptic plasticity in spiking neural networks (SP2INN): a system approach
IEEE Transactions on Neural Networks
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation
IEEE Transactions on Neural Networks
The time dimension for scene analysis
IEEE Transactions on Neural Networks
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
Implementation of Central Pattern Generator in an FPGA-Based Embedded System
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Biologically-Inspired Digital Architecture for a Cortical Model of Orientation Selectivity
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
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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.