Bakgrunn og aktiviteter

I'm developing algorithms for efficient inference of interactions from high-throughput data and I'm going to apply them to multi-electrode neural recordings.

PhD Project 

My PhD project focuses on the inverse problem related to the kinetic Ising Model and to the Generalized Linear Model. With my group we aim to set the analitical framework and to test numerically the algorithms associated to:

  • adaptive TAP equations;
  • Bethe approximation;

Once studied the ability of such techniques in recostructing networks connectivity/ dynamics, we will start apply them to multi-neural data collected at the Kavli Institute to understand the circuitry that underlies spatial navigation in mammals. 

 

Education

  • 2012-dd PhD Candidate in Neuroscience / Early Stage Researcher in the NETADIS  project;
  • 2012 MSc in Theoretical Physics, University of Trieste (IT). Thesis on "Criticality of models inferred in Boltzmann learning", supervisor: Dr. Matteo Marsili;
  • 2008 BSc  in Physics, University of Trieste (IT). Thesis on "Caos ed Entanglement in condensati di Bose Einstein" (Chaos and Entanglement of Bose-Einstein condensates)supervisor: Dr. Fabio Benatti;

Vitenskapelig, faglig og kunstnerisk arbeid

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Tidsskriftspublikasjoner

Rapport/avhandling

  • Battistin, Claudia. (2018) Dynamics of randomly connected neural networks and inference in the presence of hidden nodes. 2018. ISBN 978-82-326-3548-1.