Eduardo Serrano

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Neural signatures are cell-specific interspike interval distributions found in bursting neurons. These intraburst signatures have been first described for the neurons of the pyloric CPG of crustacean. Their functional role is still unknown and their presence in other CPG systems has not been shown yet. Modeling has shown that neural signatures can be part(More)
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The retrieval abilities of spatially uniform attractor networks can be measured by the global overlap between patterns and neural states. However, we found that nonuniform networks, for instance, small-world networks, can retrieve fragments of patterns (blocks) without performing global retrieval. We propose a way to measure the local retrieval using a(More)
Models of neurons based on iterative maps allows the simulation of big networks of coupled neurons without loss of biophysical properties such as spiking, bursting or tonic bursting and with an affordable computational effort. These models are built over a phenomenological basis and are mainly implemented by the use of iterative two-dimensional maps that(More)
Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network(More)
Severe weather can lead to significant runway capacity reductions. Runway priority is typically given to inbound flights, thus fewer flights depart and fewer gates become available for arriving aircraft, leading to delays on the tarmac. We provide a simulation-based framework for evaluating gate allocation policies under reduced runway capacity. We first(More)
A wide range of networks, including those with small-world topology, can be modeled by the connectivity ratio and randomness of the links. Both learning and attractor abilities of a neural network can be measured by the mutual information (MI) as a function of the load and the overlap between patterns and retrieval states. In this letter, we use MI to(More)