• Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches 

      Heiney, Kristine; Huse Ramstad, Ola; Sandvig, Ioanna; Sandvig, Axel; Nichele, Stefano (2019 IEEE Symposium Series on Computational Intelligence (SSCI);, Chapter; Conference object; Peer reviewed, 2020)
      In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide ...
    • Criticality as a measure of developing proteinopathy in engineered human neural networks 

      Valderhaug, Vibeke Devold; Heiney, Kristine; Huse Ramstad, Ola; Bråthen, Geir; Kuan, Wei-Li; Nichele, Stefano; Sandvig, Axel; Sandvig, Ioanna (bioRxiv;, Journal article, 2020-05-04)
      A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson’s disease (PD), misfolded forms of alpha-synuclein proteins aggregate and accumulate in hallmark ...
    • 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 (IEEE Symposium Series on Computational Intelligence (SSCI);, Chapter; Peer reviewed, 2020-02-20)
      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 (Cognitive Neurodynamics;, Journal article; Peer reviewed, 2020-06-11)
      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, ...
    • 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 (bioRxiv;, Journal article, 2020-05-02)
      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 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 ...