Formations are often seen in nature, and bring many benefits for the group as a whole. They can allow a group to explore a large area more effectively, can ease movement of the group through the environment, and can increase group perceptual coverage and increase defensive capabilities, for example. The benefits of any particular formation vary and are obtained from the structure the formation provides. Robotic formations can have similar applications. To date, the techniques used and formations employed in robotic applications are significantly simpler than those seen in nature. Current techniques often require some level of global knowledge, central processing or other unrealistic assumptions. We seek to develop a formation control technique that has as few of these limitations as possible. Each agent under our approach has only local knowledge of the environment, uses no broadcast communication, and can communicate only over a limited range. Formations are achieved by organizing agents into a graph structure, where agents occupying the vertices take on the role of maintaining an appropriate number of agents on each edge, thus preserving the formation's shape and scale. We do not assume a known or static population: the evolving formation acts as a physical data structure to assist in placing and rearranging agents as the population changes. This approach does not require a global coordinate system, fixed positions within the formation, or any single lead agent. All agents within our approach are peers, and any can adopt any role within the formation.

The field of multi-robot coordination, specifically robot formation control, is rapidly expanding, with many applications proposed. In our previous work, we considered the problem of establishing and maintaining a formation of robots given an already connected network. We now propose a distributed auction-based method to autonomously initialize and reorganize the network structure of a formation of robots.

As a robot formation increases in size or explores places where it is difficult for a human operator to interact, autonomous control becomes critical. We propose a distributed autonomous method for evaluating properties of multi-robot systems, and then discuss how this information can be applied to improve performance with respect to a given operation. We present this as an extension of our previous work on robot formations; however, the techniques described could be adapted to other multi-robot systems.

We study several statistically and biologically motivated learning rules using the same visual environment, one made up of natural scenes, and the same single cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the BCM learning rule, and single cell ICA. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

We study several statistically and biologically motivated learning rules using the same visual environment, one made up of natural scenes, and the same single cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the BCM learning rule, and single cell ICA. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

The details of these rules are different as well as their computational reasoning, however they all depend on statistics of order higher than two and they all produce sparse distributions. In what follows we investigate several specific modification functions that have the.

Analytic solutions to the information-theoretic evolution equation of the connection strength of a three-layer feedforward neural net for visual information processing are presented. The results are (1) the receptive fields of the feature-analysing cells correspond to the eigenvector of the maximum eigenvalue of the Fredholm integral equation of the first kind derived from the evolution equation of the connection strength; (2) a symmetry-breaking mechanism (parity-violation) has been identified to be responsible for the changes of the morphology of the receptive field; (3) the conditions for the formation of different morphologies are explicitly identified.

Analytic solutions to the information-theoretic evolution equation of the connection strength of a three-layer feedforward neural net for visual information processing are presented. The results are (1) the receptive fields of the feature-analysing cells correspond to the eigenvector of the maximum eigenvalue of the Fredholm integral equation of the first kind derived from the evolution equation of the connection strength; (2) a symmetry-breaking mechanism (parity-violation) has been identified to be responsible for the changes of the morphology of the receptive field; (3) the conditions for the formation of different morphologies are explicitly identified.

Analytic solutions to the information-theoretic evolution equation ofthe connection strength of a three-layer feedforward neural net for visual information processing are presented. The results are (1) the receptive fields of the feature-analysing cells correspond tothe eigenvector of the maximum eigenvalue of the Fredholm integral equation of the first kind derived from the evolution equation of the connection strength; (2) a symmetry-breaking mechanism (parity-violation) has been identified to be responsible forthe changes of the morphology of the receptive field; (3) the conditions for the formation of different morphologies are explicitly identified.