Today, unprecedented advances in machine learning applied to biomedicine have led to a variety of unsupervised techniques for remote image analysis, enabling cost-effective early detection of diseases. In this talk I will present various methods for ophthalmic image analysis, which take advantage of network theory and non-linear data analysis tools.
The brain is a complex system with a diverse hierarchy of spatial and temporal scales. Single neurons at a spatial scale of tens of micrometers interact with each other building a mesoscopic self-organised entity at a spatial scale of few millimetres. This entity is called neuronal patch, neural column in the cortex or just neural population. Moreover, single neurons evolve at various temporal scales, ranging from few milliseconds to hundreds of milliseconds and the mesoscopic neural population evolves on a slower range of time scale between 20 milliseconds and 1 second.
The movement of many animals follows Lévy patterns. The possible endogenous neuronal dynamics that generates this behaviour is unknown. In this work, we show the novel discovery of multifractality in winnerless competition systems and how it reveals an encoding mechanism able to generate two dimensional superdiffusive Lévy movements from a Lotka-Volterra map, and experimental data for Long-Evans rats during chasing tasks, mice motor cortex neurons and the Grasshopper auditory receptor cell.