Neuro-DYVERSE: building hybrid systems neuroscience

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Abstract

Tools from control engineering, formal methods of computer science and network science hold the promise of transforming the course of computational neuroscience. In this talk, we will explore how these tools can be combined in a framework called Neuro-DYVERSE. Neuro-DYVERSE is a work in progress and aims towards a further understanding of the adaptive dynamical processes involved in the formation and consolidation of memory in the human brain. In neuroscience, this is known as neuroplasticity: the brain's ability to change due to experience or damage. The dynamical behaviour of networks of billions of neurons is still poorly understood, as is its relationship to the emergence of learning and memory. Pre-existing models are still fairly limited. The multi-scale complexity of the problem requires the combination of paradigms from different fields, mainly: hybrid systems, control engineering, automated verification, dynamical systems and network science. Neuro-DYVERSE is built upon the computational-mathematical framework DYVERSE. DYVERSE stands for the DYnamically-driven VERification of Systems with Energy considerations, and focuses on hybrid systems models and tools capturing the mixture of continuous dynamics with discontinuities – that is, abrupt changes or transitions. This work leads towards a new branch of computational neuroscience: hybrid systems neuroscience, to coin a term.

Keyword(s)

Brain networks; Complex networks; Computational neuroscience; Control engineering; Formal verification; Hybrid systems

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