M
Mauricio Barahona
Researcher at Imperial College London
Publications - 266
Citations - 11931
Mauricio Barahona is an academic researcher from Imperial College London. The author has contributed to research in topics: Complex network & Computer science. The author has an hindex of 44, co-authored 252 publications receiving 10076 citations. Previous affiliations of Mauricio Barahona include California Institute of Technology & Massachusetts Institute of Technology.
Papers
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Proceedings ArticleDOI
On periodic reference tracking using batch-mode reinforcement learning with application to gene regulatory network control
TL;DR: This paper extends an existing batch-mode reinforcement learning algorithm, known as Fitted Q Iteration, to the periodic reference tracking problem, and presents a new approach that explicitly exploits a priori knowledge of the future values of the reference trajectory and its periodicity.
Posted ContentDOI
Flux-dependent graphs for metabolic networks
TL;DR: A frame-work for the systematic construction of flux-based graphs derived from organism-wide metabolic networks that encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions is presented.
Posted Content
Role-similarity based comparison of directed networks
Kathryn Cooper,Mauricio Barahona +1 more
TL;DR: A novel measure of similarity between nodes in different networks as a generalization of the concept of self-similarity is presented, which has the potential to be influential in tasks such as assigning identity or function to uncharacterized nodes.
Journal ArticleDOI
Great cities look small
TL;DR: In this article, the authors propose a mathematical model of human interactions in terms of a local strategy of maximising the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets.
Posted ContentDOI
Learning spatiotemporal signals using a recurrent spiking network that discretizes time
TL;DR: A model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning is investigated where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read- out neurons encode space.