Learn More
Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can(More)
Neuromodulation is considered a key factor for learning and memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural plasticity at target neural nodes. Simulated(More)
— Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions , animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in regulating learning and adaptivity when reward conditions are subject to variability. In biological(More)
Neuromodulation is thought to be one of the underlying principles of learning and memory in biological neural networks. Recent experiments have shown that neuroevo-lutionary methods benefit from neuromodulation in simple grid-world problems. In this paper we investigate the performance of a neuroevolutionary method applied to a more realistic robotic task.(More)
A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, ana-log electronic circuits, and control systems. Analog networks(More)
In this paper we describe a new class of representations for real-valued parameters called Center of Mass Encoding (CoME). CoME is based on variable length strings, it is self-adaptive, and it permits the choice of the degree of redundancy of the genotype-to-phenotype map and the choice of the distribution of the redundancy over the space of phenotypes. We(More)
Keywords: Graph Terminal graph (t-graph) Network representation Network synthesis Network inference Complex networks Gene networks Neural networks a b s t r a c t Artificial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we(More)
  • M.-O Hongler, Floreano, Paolo E Di, Dr F Kaplan, Prof T Ziemke, Mototaka Suzuki +38 others
  • 2008
The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels(More)
  • 1