The School will take place partially online and partially in presence (max 20 people)
at Villa del Grumello, Como – 30 August / 3 September 2021
(if presence is not possible the School will be held as a completely in-remote event)
The scientific theme
Science has been working to tackle complexity, emergence, networks and information as inter-related and yet distinct themes connecting today’s neuroscience, data science and artificial intelligence. The human brain is a complex network that shares and processes information by using the structural paths between areas in order to perform a function. This summer school will provide an opportunity for discussion to a very diverse scientific community, creating a forum for young researchers to be exposed to the appealing questions intermingling neuroscience, modelling, robotics and theories. While surfing on the crest forming the basis of modern artificial intelligence, the school will target an eclectic mix of topics including what is the state of the art in today’s: physiology of the neurons and data-driven models, temporal and spatial properties of neural cells and circuits, ensemble phenomena, information processing in the brain, and mental diseases. Outstanding speakers involved in these topics will be invited to give two lectures. Participants will also be invited to tell the audience their research, with the aim of bridging disruptive and confirmative innovations brought in by the young researchers. The distinct goal of this school is to provide cross-disciplinary training relating the theories of brain function with computational modelling techniques relevant to understand brain function and dysfunction and to explore and develop novel bio-inspired abstractions for robotics and artificial intelligence. Two main (interlaced) path of the school: the multiscale organization of the brain, and the measure and the processing of the information.
Modelling local microcircuits properties as well as large scale network properties is essential to understand how the brain works. The complexity of the brain requires all different modelling strategies to deal with both the complexity of its physiology and biology as well as with the large data generated by current imaging techniques. The problem therefore requires understanding local microcircuits as well as global network behaviours.