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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)
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)
— 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)
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)
The manual design of control systems for robotic devices can be challenging. Methods for the automatic synthesis of control systems, such as the evolution of artificial neural networks, are thus widely used in the robotics community. However, in many robotic tasks where multiple interdependent control problems have to be solved simultaneously, the(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)
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)