Image Processing with Spiking Neuron Networks

International audienceArtificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation.Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach

FPGA implementation of an integrate-and-fire legion model for image 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. In this work, we consider a model of integrate-and-fire neurons organized according to the standard LEGION architecture to segment grey-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

Sistema electrónico para la realización flexible de redes neuronales

El presente trabajo consiste en el diseño, simulación y test experimental de un sistema digital integrado en un solo chip que es capaz de emular el comportamiento de una red neuronal, en concreto el modelo llamado LEGION (Locally Excitatory Globally Inhibitory Oscillator Network), dentro de los múltiples modelos de red neuronal artificial que se han publicado en la actualidad. Dicho modelo es especialmente útil para resolver problemas de ingeniería como la segmentación de patrones o la segregación de figuras. La red se ha basado en el modelo ‘Integrate-and-fire’ de la neurona. Se ha utilizado el lenguaje de descripción de alto nivel VHDL para el diseño del sistema. Finalmente, para comprobar el funcionamiento del sistema se ha implementado una red neuronal que reconoce diferentes figuras y patrones en una imagen de escala de grises, sobre un dispositivo lógico programable FPGA. Se pretende que el sistema sea escalable y eficiente a la vez, utilizando el mínimo recurso posible