Neuroscience is a highly interdisciplinary field focused on uncovering the dynamics of brain and, more in general, the complex functions and structures of neural systems. These topics constitute paradigmatic examples of complex systems, and can be studied by using different frameworks, spanning from nonlinear dynamics to complex networks. In addition, the increasing availability of data coming from tools like fMRI, EEG, and others, has strongly supported new investigations. At the same time, although these topics are maybe among mostly investigated in science, a lot must yet be discovered. Remarkably, neural systems have had a great impact also in parallel fields, e.g. artificial intelligence, leading to propose new algorithms and computational techniques. For instance, neural networks and their evolution to the modern deep learning represent one of the most successful cases. It is worth to highlight that some of these tools (e.g. Deep Learning) are now widely used for investigating (biological) neural systems, e.g. for analyzing brain waves. As result, a big interdisciplinary community composed of neuroscientists, physicists, mathematicians, computer scientists, and many others, nowadays collaborates on the same projects and interacts trying to obtain new insights in this complex and exciting field. The proposed satellite will be focused on theoretical neuroscience, and its extensions to AI/Deep Learning, in order to attract the interest of researchers working in a highly interdisciplinary contexts, often overlapping, with the aim to trigger discussions and sharing novel ideas on the field. In particular, we aim to have a specific focus on the synopsis of current research into complex networks in human neuroscience, supported by data coming from fMRI/EEG/etc, on the complexity emerging in artificial neural networks, and on the potential synergy between the two fields.


The aim of this satellite is to gather scholars, belonging to different fields such as neuroscience, physics, computer science, mathematics and biology, to review the state of art of Theoretical Neuroscience and its applications, as Deep Learning and other ‘brain-inspired’ methods. Interested contributors will be invited to submit an abstract mainly (but not exclusively) on the following topics:

  • Theoretical models for brain/neural dynamics;
  • System neuroscience;
  • Connectome and other brain network models;
  • Statistical Physics and computational models for studying neural systems;
  • Signal processing;
  • Application of Neuroscience to Artificial Intelligence;
  • Deep Learning and its connections/applications in Neuroscience



Srivas Chennu

Brain Connectivity and Complexity: Complementary Approaches to Modelling Brain Dynamics

The study of human brain connectivity rests on the notion that the brain spontaneously generates networks of correlated activations involving functionally specific regions. A vast body of research into so-called resting state brain activity, using functional MRI, MEG and EEG, suggests that these brain networks are metastable, in that they exist in a state of dynamic equilibrium that is usually perturbed by a change in the state of consciousness. Alongside, borrowing a rich seam of ideas from dynamical systems theory, researchers have modelled the ongoing complexity of brain dynamics as means to characterise states of consciousness. In my talk, I will elaborate these strands of research alongside my own work on the characterisation of brain networks in altered states of consciousness, including sleep, anaesthesia, meditation, and clinical disorders of consciousness. I propose that the study of connectivity and complexity are deeply interrelated approaches to modelling ongoing brain dynamics. Together, they provide complementary descriptions of how the structural networks of the brain generates complex functional interactions. Further, I suggest that novel research into analysing the emergent complexity in artificial networks modelled after the human brain could generate deeper insights into the how such networks learn about the world and generate flexible behaviours.


Sebastiano Stramaglia

Network approach to describe synergetic and redundant information flow in multivariate systems

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex system. The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. Adopting a suitable definition of Granger causality, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. The pairwise synergy index, here introduced, maps the informational character of the system at hand onto a weighted complex network. We report the application of the proposed approach to resting state fMRI data from the Human Connectome Project; the same approach can be applied to any other complex system whose "healthy" state corresponds to a balance between redundant and synergetic circuits.


Robert Rallo





Marco Alberto Javarone


Guido Caldarelli

De Domenico

Manlio De Domenico


Andrea Gabrielli


Tommaso Gili


Daniele Marinazzo


Abstracts are submitted via the EasyChair website. Authors who do not have an EasyChair account should sign up for an account (for identification purposes, make sure to use the same email address as the one used for the conference registration). Submissions are required to be at most one page long including the following information: title, author(s), affiliation(s), e-mail address, abstract (max 1 figure). The DEADLINE for submission is 19th June 2018. Please, send your submission here: SUBMISSION
Some Abstracts can be accepted as POSTER.
All the participants of the satellite have to register here: REGISTRATION