Browsing Kavliinstitutt for nevrovitenskap by Author "Battistin, Claudia"

Now showing items 1-8 of 8

    • Behavioral decomposition reveals rich encoding structure employed across neocortex in rats 

      Mimica, Bartul; Tombaz, Tuce; Battistin, Claudia; Fuglstad, Jingyi Guo; Dunn, Benjamin Adric; Whitlock, Jonathan Robert (Peer reviewed; Journal article, 2023)
      The cortical population code is pervaded by activity patterns evoked by movement, but it remains largely unknown how such signals relate to natural behavior or how they might support processing in sensory cortices where ...

    • Dynamics of randomly connected neural networks and inference in the presence of hidden nodes 

      Battistin, Claudia (Doctoral theses at NTNU;2018:380, Doctoral thesis, 2018)
      Dynamikken til randomiserte nevrale nettverk og inferens med tilstedeværelse av skjulte noder Hjernen koder informasjon i populasjoner av nevroner, i motsetning til i enkeltceller. Modeller av nevrale nettverk kan hjelpe ...

    • Insights into the quantification and reporting of model-related uncertainty across different disciplines 

      Simmonds, Emily Grace; Dunn-Sigouin, Etienne; Adjei, Kwaku Peprah; Andersen, Christoffer Wold; Aspheim, Janne Cathrin Hetle; Battistin, Claudia; Bulso, Nicola; Christensen, Hannah M.; Cretois, Benjamin; Cubero, Ryan John Abat; Davidovich, Ivan Andres; Dickel, Lisa; Dunn, Benjamin Adric; Dyrstad, Karin; Einum, Sigurd; Giglio, Donata; Gjerløw, Haakon; Godefroidt, Amélie; González-Gil, Ricardo; Gonzalo Cogno, Soledad; Große, Fabian; Halloran, Paul; Jensen, Mari Fjalstad; Kennedy, John James; Langsæther, Peter Egge; Laverick, Jack H; Lederberger, Debora; Li, Camille; Mandeville, Elizabeth G; Mandeville, Caitlin; Moe, Espen; Schröder, Tobias Navarro; Nunan, David; Sicacha-Parada, Jorge; Simpson, Melanie Rae; Skarstein, Emma Sofie; Spensberger, Clemens; Stevens, Richard; Subramanian, Aneesh C.; Svendsen, Lea; Theisen, Ole Magnus; Watret, Connor; O'Hara, Robert B. (Peer reviewed; Journal article, 2022)
      Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world ...

    • Learning with unknowns: analyzing biological data in the presence of hidden variables 

      Battistin, Claudia; Dunn, Benjamin Adric; Roudi, Yasser (Journal article, 2017)
      Despite our improved ability to probe biological systems at a higher spatio-temporal resolution, the high dimensionality of the biological systems often prevents sufficient sampling of the state space. Even with large scale ...

    • Lowering levels of reelin in entorhinal cortex layer II-neurons results in lowered levels of intracellular amyloid-β 

      Kobro-Flatmoen, Asgeir; Battistin, Claudia; Raveendran Nair, Rajeevkumar; Bjorkli, Christiana; Skender, Belma; Kentros, Clifford George; Gouras, Gunnar; Witter, Menno Peter (Peer reviewed; Journal article, 2023)
      Projection neurons in the anteriolateral part of entorhinal cortex layer II are the predominant cortical site for hyper-phosphorylation of tau and formation of neurofibrillary tangles in prodromal Alzheimer’s disease. A ...

    • Task-dependent mixed selectivity in the subiculum 

      Ledergerber Wäfler, Debora; Battistin, Claudia; Blackstad, Jan Sigurd; Gardner, Richard; Witter, Menno; Moser, May-Britt; Roudi, Yasser; Moser, Edvard Ingjald (Peer reviewed; Journal article, 2021)
      CA1 and subiculum (SUB) connect the hippocampus to numerous output regions. Cells in both areas have place-specific firing fields, although they are more dispersed in SUB. Weak responses to head direction and running speed ...

    • The appropriateness of ignorance in the inverse kinetic Ising model 

      Dunn, Benjamin Adric; Battistin, Claudia (Journal article; Peer reviewed, 2017)
      We develop efficient ways to consider and correct for the effects of hidden units for the paradigmatic case of the inverse kinetic Ising model with fully asymmetric couplings. We identify two sources of error in reconstructing ...

    • The Stochastic Complexity of Spin Models: Are Pairwise Models Really Simple? 

      Beretta, Alberto; Battistin, Claudia; de Mulatier, Clelia; Mastromatteo, Iacopo; Marsili, Matteo (Journal article; Peer reviewed, 2018)
      Models can be simple for different reasons: because they yield a simple and computationally efficient interpretation of a generic dataset (e.g., in terms of pairwise dependencies)—as in statistical learning—or because they ...