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Blar i NTNU Open på forfatter "Mello, Gustavo"

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    • A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents 

      Gulden Dahl, Annelene; Nichele, Stefano; Mello, Gustavo (Chapter, 2021)
      Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle ...
    • EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality 

      Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano (Chapter, 2020)
      Dynamical systems possess a computational capacity that may be exploited in a reservoir computing paradigm. This paper presents a general representation of dynamical systems which is based on matrix multiplication. That ...
    • A general representation of dynamical systems for reservoir computing 

      Pontes-Filho, Sidney; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, I.; Tufte, Gunnar; Nichele, Stefano (Chapter, 2019)
      Dynamical systems are capable of performing computation in a reservoir computing paradigm. This paper presents a general representation of these systems as an artificial neural network (ANN). Initially, we implement the ...
    • Method to Obtain Neuromorphic Reservoir Networks from Images of in Vitro Cortical Networks 

      Mello, Gustavo; Pontes-Filho, Sidney; Sandvig, Ioanna; Valderhaug, Vibeke Devold; Zouganeli, Evi; Huse Ramstad, Ola; Sandvig, Axel; Nichele, Stefano (Chapter, 2020)
      In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons ...
    • A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality 

      Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano (Peer reviewed; Journal article, 2020)
      Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, ...

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