Now showing items 21-34 of 34

    • Investigation of Elementary Cellular Automata for Reservoir Computing 

      Bye, Emil Taylor (Master thesis, 2016)
      Reservoir computing is an approach to machine learning. Typical reservoir computing approaches use large, untrained artificial neural networks to transform an input signal. To produce the desired output, a readout layer ...
    • Machine Learning for Gesture Recognition with Electromyography 

      Chau, Tony (Master thesis, 2017)
      About 70 million deaf people use sign language as their first language or mother tongue, but the lack of a common language between the deaf and hearing individuals makes the general communication difficult. This thesis ...
    • 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, ...
    • Optimizing Bio-Inspired Propulsion Systems using Genetic Algorithms 

      Gjerde, Thomas (Master thesis, 2017)
      Optimization is an important part of the development of an efficient propulsion system. The biomimetic marine propulsion system referred to as an oscillating foil has recently seen an upswing in interest. The optimization ...
    • Optimizing for Energy in High-Level Programming Languages on Embedded Devices 

      Gombos, Péter Henrik (Master thesis, 2015)
      The use of embedded systems has exploded recently, and thus also the number of developers for embedded systems. But the traditional way of programming embedded computers is hard and error prone, and the use of high-level ...
    • Reducing the Search Space of Neuroevolution using Monte Carlo Tree Search 

      Wiker, Erik (Master thesis, 2019)
      Denne oppgaven undersøker muligheten for å bruke Monte-Carlo-tre-søk for å redusere søkeområdet til den velkjente maskinlæringsalgoritmen Neuroevolution of Augmenting Topologies, med sikte på å oppnå kortere kjøretider og ...
    • Reservoir Computing Using Nonuniform Binary Cellular Automata 

      Nichele, Stefano; Gundersen, Magnus Skogstrøm (Journal article; Peer reviewed, 2017)
      The reservoir computing (RC) paradigm utilizes a dynamical system (a reservoir) and a linear classifier (a readout layer) to process data from sequential classification tasks. In this paper, the usage of cellular automata ...
    • Reservoir Computing Using Quasi-Uniform Cellular Automata 

      Gundersen, Magnus Skogstrøm (Master thesis, 2017)
      Unconventional computing offers many advantages over traditional computing systems; vast parallelism, scalability and robustness among others. A Cellular Automaton (CA) is a biologically inspired computational substrate ...
    • Structural and functional alterations associated with the LRRK2 G2019S mutation revealed in structured human neural networks 

      Valderhaug, Vibeke Devold; Huse Ramstad, Ola; van de Wijdeven, Rosanne Francisca; Heiney, Kristine; Nichele, Stefano; Sandvig, Axel; Sandvig, Ioanna (Journal article, 2020)
      Mutations in the LRRK2 gene have been widely linked to Parkinson’s disease. The G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been ...
    • Towards a Plant Bio-Machine 

      Nichele, Stefano; Risi, Sebastian; Tufte, Gunnar; Beloff, Laura (Chapter, 2017)
      Plants are very efficient computing machines. They are able to sense diverse environmental conditions and quickly react through chemical and electrical signaling. In this paper, we present an interface between plants and ...
    • Towards Making a Cyborg: A Closed-Loop Reservoir-Neuro System 

      Aaser, Peter; Knudsen, Martinius; Huse Ramstad, Ola; van de Wijdeven, Rosanne; Nichele, Stefano; Sandvig, Ioanna; Tufte, Gunnar; Bauer, Ulrich Stefan; Halaas, Øyvind; Hendseth, Sverre; Sandvig, Axel; Valderhaug, Vibeke Devold (Chapter; Peer reviewed, 2017)
      The human brain is a remarkable computing machine, i.e. vastly parallel, self-organizing, robust, and energy efficient. To gain a better understanding into how the brain works, a cyborg (cybernetic organism, a combination ...
    • Universality of Evolved Cellular Automata in-Materio 

      Nichele, Stefano; Farstad, Sigve Sebastian; Tufte, Gunnar (Journal article; Peer reviewed, 2017)
      Evolution-in-Materio (EIM) is a method of using artificial evolution to exploit physical properties of materials for computation. It has previously been successfully used to evolve a multitude of different computational ...
    • Using Genomic Parameters to Predict Structural Complexity in Artificial Organisms 

      Anthony, John H. (Master thesis, 2012)
      This thesis investigates the relationship between genomic parameters andproperties of the phenotype. More specifically, we claim that there isa connection between the genomic lambda parameter and the structuralcomplexity ...