School on Nonlinear Dynamics, Complex Networks, Information Theory and Machine Learning in Neuroscience

Start time:  May 22, 2023

Ends on: May 26, 2023

Location: São Paulo, Brazil



In the field of neuroscience, to better understand complex phenomena that occur in a wide range of scales (from the dynamics of single neurons, in vitro neuronal cultures, to the whole brain), appropriate models, experimental techniques and data analysis techniques are needed.

This school for PhD students and young postdocs will focus on five main areas: nonlinear dynamics, complex networks, data analysis, information theory and machine learning. The school will cover fundamental and applied aspects such as excitability and neuronal dynamics, neural coding, entropy and complexity measures, machine learning and data analysis methods for inferring functional connectivity, methods for characterizing functional networks, etc.

A “student’s presentation” session will be organized on Monday afternoon, where participants will have 3 min to present themselves and explain their research topic. At the end of the school, participants will present group projects based on the tools learned in the school. Besides the lectures, tutored sessions and discussions will be organized in order to help participants on the development of the group projects.

There is no registration fee and limited funds are available for travel and local expenses.


  • Ana Amador(Universidad de Buenos Aires, Argentina): Nonlinear dynamics of neuronal models with applications to bird song dynamics 
  • Cristina Masoller(Universitat Politecnica de Catalunya, Spain): Time Series Analysis tools with applications to Neuroscience
  • Jesús Gomez-Gardeñes(Universidad de Zaragoza, Spain): Complex networks and applications to neuroscience
  • Osvaldo A. Rosso(Universidade Federal de Alagoas, Brazil): Information theory tools for neuroscience applications
  • Jordi Soriano(Universidad de Barcelona, Spain): Structure-to-function relationship in neuronal cultures: applications to biological machine learning and reservoir computing

Application deadline: March 12, 2023

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