The brain is not a huge fixed neural network, but a dynamic, changing neural network that continuously adapts to meet the demands of communication and computational needs. In classical neural networks approaches, particularly associative memory models, synapses are only adjusted during the training phase. After this phase, synapses are no longer adjusted. In this paper we describe a new dynamical model where synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. We provide some propositions that guarantee perfect and robust recall of the fundamental set of associations. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model.

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of truecolor patterns.

Recent studies warn of a possible major earthquake off the coast of State of Guerrero, Mexico, so that, it turns important to alert the population as long as possible and avoid a great disaster. This requires the construction of a network of seismic sensing stations, located at strategical positions, to detect earthquakes and issue a timely warning. In this research, we investigate how a genetic algorithm can be applied to design this network and determine the optimal location of each seismic sensing station. The number of earthquakes detected by the designed network will be used as a reference point with respect to the currently installed seismic alert system (SAS). This metric will justify the use of the genetic algorithms as a designing tool prior to the construction of the network in different regions of Mexico. The SAS stations and each solution proposed by a genetic algorithm underwent a procedure, in which it is simulated the occurrence of earthquakes obtained from the Mexico's National Seismological Service (SSN) database, to determinate its efficiency in terms of the time to warn Mexico City.

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