Food & Paper: How music and the brain can illuminate each other via complexity science (Rosas)
Dr. Fernando E. Rosas from Imperial College London will give a talk on "How music and the brain can illuminate each other via complexity science"
In this talk we explore two applications of complexity science that shed new light into aspects of neuroscience and music.
First, we consider complexity as "richness of content" (i.e. how hard is to describe something), following the work of Kolmogorov, Solomonoff, and Chaitin. We show how these methods can be used to quantify the level of awareness of subjects from their neural signals, and hence discriminate between various conscious states. In particular, we review results from the neuroscience of psychedelics, and show interesting similarities with brain signatures obtained during musical improvisation. In the second part of the talk we explore complexity as "richness of structure", following ideas from Tononi, Sporns and Edelman. We show how these principles, originally proposed to explain high brain functions, can be used to illuminate unseen aspects of the polyphony of J.S. Bach.
I am a Postdoctoral Researcher at Imperial College London, based at the Centre For Psychedelic Research (Department of Medicine), and also affiliated with the Centre for Complexity Science, the Department of Mathematics, and the Data Science Institute.
My current work is focused in the development of tools to enable a deeper understanding of the interdependencies that can take place in systems composed of many interacting agents. I am interested in the most fundamental and theoretical aspects of this problem, and also in the consequences and applications in diverse contexts, related to basic sciences, engineering and arts.
Although we currently live in a world characterized by interrelationship and connectivity, the interdependencies that can exist between three or more variables or stochastic processes are poorly understood. Improving this understanding is paramount for future advances in neuroscience, genetics, network science and many other fields that explore systems composed by many interacting variables. Moreover, this understanding might prove to be fundamental in our post-modern society, where the surprising outcomes of recent political polls (e.g. the Brexit referendum and the latest US presidential election) are revealing the limitation of our current understanding of social behaviour in a highly-interconnected world.