How does one aim to understand neural information processing? One of the difficult first challenges is to identify the roles of the network's elements. To this end a functional contribution analysis (FCA) method has been developed and applied for studying the neurocontrollers of evolutionary autonomous agents (EAAs). The FCA processes data composed of multiple lesion experiments and the corresp...

With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unkn...

Evolutionary robotics is an interesting novel approach to shape the control system of autonomous robots. This explores issues related to readaptation in changed environments of a population of evolved individuals. Experimental studies are reported for genetic evolution of neurocontrollers that have developed the ability to perform homing navigation for battery recharge of miniature mobile robot...

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. The stability in this context corresponds to the convergence in adjustable parameters of the neural network structure. It is shown that th...

The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hamper...

Artificial neural networks offer an attractive paradigm for the design of behavior and control systems in robots and autonomous agents for a variety of reasons, including: ability to adapt and learn, potential for resistance to noise, faults and component failures, potential for real-time performance in dynamic environments (through massive parallelism and suitable hardware realization) etc. Ho...

This paper presents experimental results of an original approach to the Neural Network learning architecture for the control and the adaptive control of mobile robots. The basic idea is to use non-recurrent multi-layer-network and the backpropagation algorithm without desired outputs, but with a quadratic criterion which spezify the control objective. To illustrate this method, we consider an e...