Three case studies of the GasNet model in discrete domains

A new neural network model - the GasNet - has been recently reported in the literature, which, in addition to the traditional electric type, point-to-point communication between units, also uses communication through a diffilsable chemical modulator. Here we assess the applicability of this model in three different scenarios, the XOR problem, a food gathering task for a simulated robot, and a docking task for a virtual spaceship. All of them represent discrete domains, a contrast with the one where the GasNet was originally introduced, which had an essentially continuous nature. These scenarios are well-known benchmark problems from the literature and, since they exhibit varying degrees of complexity, they impose distinct performance demands on the GasNet. The experiments were primarily intended to better understand the model, by extending the original problem domain where GasNet was introduced. The results reported point at some difficulties with the current GasNet model.

Four-dimensional neuronal signaling by nitric oxide: a computational analysis

Nitric oxide (NO) is now recognized as a transmitter of neurons that express the neuronal isoform of the enzyme nitric oxide synthase. NO, however, violates some of the key tenets of chemical transmission, which is classically regarded as occurring at points of close apposition between neurons. It is the ability of NO to diffuse isotropically in aqueous and lipid environments that has suggested a radically different form of signaling in which the transmitter acts four-dimensionally in space and time, affecting volumes of the brain containing many neurons and synapses. Although "volume signaling" clearly challenges simple connectionist models of neural processing, crucial to its understanding are the spatial and temporal dynamics of the spread of NO within the brain. Existing models of NO diffusion, however, have serious shortcomings because they represent solutions for "point-sources," which have no physical dimensions. Methods for overcoming these difficulties are presented here, and results are described that show how NO spreads from realistic neural architectures with both simple symmetrical and irregular shapes. By highlighting the important influence of the geometry of NO sources, our results provide insights into the four-dimensional spread of a diffusing messenger. We show for example that reservoirs of NO that accumulate in volumes of the nervous system where NO is not synthesized contribute significantly to the temporal and spatial dynamics of NO spread.

Artificial evolution: a new path for artificial intelligence?

Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual issues surrounding the work are discussed, as is the use of more abstract evolutionary simulations as a new potentially useful tool in theoretical biology.