Francisco de Borja Rodríguez Ortiz

Characterization of Triphasic Rhythms in Central Pattern Generators (I): Interspike Interval Analysis

Central Pattern generators (CPGs) neurons produce patterned signals to drive rhythmic behaviors in a robust and flexible manner. In this paper we use a well known CPG circuit and two different models of spiking-bursting neurons to analyze the presence of individual signatures in the behavior of the network. These signatures consist of characteristic interspike interval profiles in the activity of each cell. The signatures arise within the particular triphasic rhythm generated by the CPG network. We discuss the origin and role of this type of individuality observed in these circuits.

Characterization of Triphasic Rhythms in Central Pattern Generators (II): Burst Information Analysis

Central Pattern generators (CPGs) are neural circuits that produce patterned signals to drive rhythmic behaviors in a robust and flexible manner. In this paper we analyze the triphasic rhythm of a well known CPG circuit using two different models of spiking-bursting neurons and several network topologies. By means of a measure of mutual information we calculate the degree of information exchange in the bursting activity between neurons. We discuss the precision and robustness of different network configurations.

Using virtual worlds for behaviour clustering-based analysis

Virtual Worlds have become a very popular domain and its high inmersive characteristics can be used to extract information about the avatars behaviour. In this kind of environment it is possible to obtain interesting data about avatars, such as their exact position in the world, what they are looking at (eye-gazing) or what they are talking about. This paper studies how this information, obtained from avatars interactions, can be integrated in order to apply clustering techniques. Monitoring avatars in a virtual world is a useful task that allows the identification of behavioral groups. The meaning of these groups depends on the application domain, for example in educational virtual worlds, they can represent whether students are paying attention to the teachers explanation or not.

Optimal Message Interchange in a Self-organizing Multi-agent System

Over the last decade there has been a growing interest on Intelligent Agents and Multi-Agent Systems (MAS) in several fields such as Artificial Intelligence (AI), Software Engineering, Psychology, etc... Different problems can be solved in these fields by creating societies of agents that communicate with each other. Nevertheless, when the number of agents is large and the connectivity is extensive, the system suffers from overhead in the communication among agents due to the large number messages exchanged. This work addresses the search for an optimal communication topology to avoid these situations. This optimal topology is characterized by the use of a redirecting probability in the communication. The redirection of a communication is performed before the execution of the MAS. Once agents start the execution, the topology is fixed and remains unchanged. This characteristic is useful in those systems where a given topology can not be changed as, for example, in wired networks. On the other hand, in the proposed solution agents contain a local message discrimination process as a function of the sender of the message. Experiments show an important improvement in terms of a reduction in the number of iterations needed to solve the problem and also in the number of messages exchanged.

Dynamical invariants for CPG control in autonomous robots

This is an electronic version of the paper presented at the 7th International Conference on Informatics in Control, Automation and Robotics, held in Madeira on 2010

Richer Network Dynamics of Intrinsically Non-regular Neurons Measured through Mutual Information

Central Pattern Generators (CPGs) are assemblies of neurons that act cooperatively to produce regular signals to motor systems. The individual behavior of some members of the CPGs has often been observed as highly variable spiking-bursting activity. In spite of this fact, the collective behavior of the intact CPG produces always regular rhythmic activity. In this paper we show that simple networks built out of intrinsically non-regular units can display modes of regular collective behavior not observed in networks composed of intrinsically regular neurons. Using a measure of mutual information we characterize several patterns of activity observed by changing the coupling strength and the network topology. We show that the cooperative behavior of these neurons can display a rich variety of information transfer while maintaining the regularity of the rhythms.

Periodic and Synchronic Firing in an Ensemble of Identical Stochastic Units: Structural Stability

A model build with an ensemble of identical, noisy, integrate-and-fire oscillators with messages interchange has been studied to find out regimes in which the collective behavior is stable at mesoscopic level. We have found for our model a range in the strength of messages for which the system settles into a periodic synchronic behavior that only depends on structural parameters: the number of units in the ensemble and the time taken by the message to arrive from the sender to the receiving unit. This type of periodic and synchronic firing pattern could be relevant for information processing since it is independent of the detailed dynamics of units making up the ensemble. The periodic pattern is drive by passing messages and it, remains stable even under severe noise affecting the evolution of isolated units.

A Simple Probabilistic Neural Model Producing Multimodal ISHs

Simple probabilistic neural models can be used to study the information processing occurring in the brain. Suitable models have to reproduce complex Interspike Histograms (ISHs) observed experimentally, but have to be simple enough to allow theoretical analysis. The simple probabilistic integrate and fire model we present in this paper can be used to identify the origin of peaks appearing in complex multimodal ISHs.

Communication by identity discrimination in bio-inspired multi-agent systems

Network communications have been widely studied in the last decades in different research fields: artificial intelligence, computer science, biology, medicine and psychology among others. Some important efforts have been carried out to analyse communication features such as overhead, connectivity or communication protocols in these areas from their own perspectives. When this problem is restricted to intelligent agents or multi‐agent systems, networks are built by a set of interconnected agents that can be software or hardware. In multi‐agent systems, communication optimization is used to improve the overall performance of the system by reducing the information sharing (i.e. number of messages or message size) between the agents. This paper analyses a scale‐free network topology of agents to solve a multi‐sorting problem. The agents use their local information as well as a bio‐inspired identity discrimination process to select only those messages that are relevant for each agent to solve jigsaw puzzles. We provide a comprehensive study on the influence of some essential parameters (memory information size and reconnection probability) in an agent network, and how they can be set to obtain a better performance in the system. The experiments show that this strategy contributes to reduce the number of iterations needed to solve the problem. Copyright

Stability and Hebbian Learning in Populations of Probabilistic Neurons

The effect of a hebbian learning process in an isolated population of neurons is investigated using numerical simulations on a probabilistic neural network model. An increase of regularity in spike production is observed as a result of exposure to messages received by connections of adapting strength. The simple mechanism of synaptic adaptation that uses local information available at synapses is capable of driving the population towards a stable firing rhythm and does so by selecting a stable set of synaptic weights. The observed stable limit is coherent with a low firing profile in the activity of the isolated population model.