About this Research Topic
This Research Topic derives from the 2019 course of the School of Brain Cells and Circuits dedicated to modeling the brain and its pathologies. Modeling 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 modeling 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 behaviors, impacting on artificial intelligence and robotics as tools for modeling brain function and dysfunction.
The Research Topic will consider the different approaches needed to model various aspects of neuronal function, structure and dynamics. At one extreme, there are bottom-up models (i.e. constructed from the knowledge of elementary properties) implementing a process of “reverse engineering”. At the other extreme, there are top-down models (i.e. derived from an intuition about system organization).
Papers in the Special Topic should provide the basis to understand how integrated brain signals can be used to understand brain function and structure at different scales. Functional brain signals (e.g. and fMRI or EEG signal) contain information about the activity of all the neurons generating them but the question (known as “inverse” problem) is how this information can be extracted. The operation needs models that can be used to approximate the brain signal datasets and, through a process of parameter optimization, to extract information about the underlying neuronal population. Similar approaches also allow to decipher structural data in terms of the underlying microstructure, which subtends brain function and brain dynamics.
Commentaries confronting the two different approaches (bottom-up and top-down) and highlighting the ways data, models and theory interplay in addressing brain function and dysfunction are welcome. A critical issue that could addressed is how the convergence between bottom-up and top-down models will may occur.
Some contributions will develop on how brain modeling, at its different levels, can be projected into technological and biomedical applications. These encompass cognitive architectures implemented in AI and closed-loop robotic controllers. These advanced applications, in turn can give important clues to understand how the brain works. Finally, some contributions will face the issue on what aspects of brain modeling can be exploited for medical informatics, i.e. being integrated into big-data analysis and machine learning techniques.
The Research Topic will consider the different approaches needed to model various aspects of neuronal function, structure and dynamics. At one extreme, there are bottom-up models (i.e. constructed from the knowledge of elementary properties) implementing a process of “reverse engineering”. At the other extreme, there are top-down models (i.e. derived from an intuition about system organization).
Papers in the Special Topic should provide the basis to understand how integrated brain signals can be used to understand brain function and structure at different scales. Functional brain signals (e.g. and fMRI or EEG signal) contain information about the activity of all the neurons generating them but the question (known as “inverse” problem) is how this information can be extracted. The operation needs models that can be used to approximate the brain signal datasets and, through a process of parameter optimization, to extract information about the underlying neuronal population. Similar approaches also allow to decipher structural data in terms of the underlying microstructure, which subtends brain function and brain dynamics.
Commentaries confronting the two different approaches (bottom-up and top-down) and highlighting the ways data, models and theory interplay in addressing brain function and dysfunction are welcome. A critical issue that could addressed is how the convergence between bottom-up and top-down models will may occur.
Some contributions will develop on how brain modeling, at its different levels, can be projected into technological and biomedical applications. These encompass cognitive architectures implemented in AI and closed-loop robotic controllers. These advanced applications, in turn can give important clues to understand how the brain works. Finally, some contributions will face the issue on what aspects of brain modeling can be exploited for medical informatics, i.e. being integrated into big-data analysis and machine learning techniques.
Keywords: MRI, virtual brain, single neuron models, microcircuit models, large-scale networks, connectomics, brain pathology, brain modeling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
