Manuscript Topics

In recent years, the field of mathematical and computational neuroscience has remarkably advanced, in conjunction with recent theoretical and technological advances and the increasing amount of experimental data available for human and animal brains at different scales. While some biophysically accurate mathematical models (e.g. Hodgkin-Huxley) are able to reproduce the neuronal dynamics with a high level of accuracy, for the construction of the neuronal network it is of uttermost importance to adopt models with a low computational cost. The current main goal in the area of neuroscience modelling is therefore to find a valid compromise between model complexity, biological plausibility, and computational efficiency, to allow for a realistic representation of brain dynamics through large-scale networks.

The aim of this special issue is therefore to present the most recent progress in the area of mathematical and data-driven computational modelling in neuroscience. Moreover, in this issue it will be shown how mathematical models and tools can often not only provide further insights on neuronal dynamics, but also support and simplify computational investigations, leading to an increasingly accurate description of neuronal phenomena to be widely used in real-life applications.

Topics of interest include, but are not limited to, the:
• Mathematical modelling of a single neuron and neuronal systems
• Theoretical neuroscience
• Computational neuroscience
• Multiscale modelling and numerical analysis
• Data-Driven modelling
• Reduced-order modelling
• Reconstruction of connectivity networks from data
• Source Localization
• Machine Learning for modelling and classification
• Modelling and analysis of the dynamics of neurological disorder

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