Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

On the effect of scene motion on color constancy.

A  series of experiments with human subjects have shown that color constancy improves when an object moves. It has been hypothesized that this effect is due to some kind of influence of high-level motion processing. We have built a computational model for color perception which replicates the results qualitatively which have been obtained with human subjects. We show that input from high-level motion processing is not required. In our model, the dependence is an effect of eye movement in combination with neural processing. Depending on the type of stimulus used, the eye either tracks the object or the background. When the object moves but is tracked by the observer, the background appears to move when considering the stimulus with respect to eye coordinates. Hence, the retinal input is different for the two conditions leading to a difference in color constancy performance.

Evolutionary parameter optimization of a fuzzy controller which is used to control a sewage treatment plant.

In order to meet new environmental standards, sewage treatment plants may need to be redesigned or extended. Instead of reconstructing large parts of a sewage treatment plant, which can be very costly, it is in many cases sufficient to install relatively inexpensive equipment, which controls parts of the plant in a new way. Fuzzy controllers are often used for this task. Use of these controllers often leads to an improved water quality. Such fuzzy controllers contain a number of parameters which are determined by a human expert. With this contribution, a dedicated multi-objective evolutionary algorithm is developed to optimize these parameters. The evolutionary algorithm is based on the successful strength pareto evolutionary algorithm 2 (SPEA2). The fuzzy control parameters, which are optimized are continuous parameters. Therefore, an evolution strategy was employed which uses the multi-objective ranking as used by the SPEA2 algorithm. Optimal parameters were first evolved on simulated sewage treatment plants. One set of parameters was also tested on an actual plant. Owing to the enormous computational demands of simulating a sewage treatment plant, it is only possible to work with small population sizes. Nevertheless, it was possible to evolve parameters which were equally well as those found by a human expert indicating that the parameter tuning can be automized.

Integrating color constancy into JPEG2000.

The human visual system is able to perceive colors as approximately constant. This ability is known as color constancy. In contrast, the colors measured by a sensor vary with the type of illuminant used. Color constancy is very important for digital photography and automatic color-based object recognition. In digital photography, this ability is known under the name automatic white balance. A number of algorithms have been developed for color constancy. We review two well-known color constancy algorithms, the gray world assumption and the Retinex algorithm and show how a color constancy algorithm may be integrated into the JPEG2000 framework. Since computer images are usually stored in compressed form anyway, little overhead is required to add color constancy into the processing pipeline.