Modeling brain function at the level of neurons and circuits via computational and data-driven approaches

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Background

The issue on modeling of brain function that corresponds to the interaction between neurons in the form of network is one of the recent topic of research. Understanding of human brain function has been carried out in two different ways, namely computational and data-driven approaches. The computation of mathematical neuronal model may give clues how the neurons are functioning smoothly in the brain. In presence of neuronal disorders like Parkinson’s disease, epileptic seizures, and even to schizophrenia, mathematical models give insufficient information and in these cases data-driven approach is more helpful. Recently, in neurophysiological experiment allows to collect EEG data to identify coherent brain structures during sensory information processing. Being so relevant, it necessitates to bring further development in this subject of research with a dedicated special issue. This special issue, therefore, plans to assemble original results along with reviews on the aspects of brain function modeling in the level of neurons through computation and data-driven approaches.

Human brain network is one of the complicated network that has been a challenge for researchers from different fields of Science. Still there exist a few crucial domains in which further study on the mathematical modeling and neurophysiological signals via data-driven network and artificial intelligence is highly needed. For instance, mathematical modeling of neuronal diseases is very less explored. Collective behaviors like synchronization and chimera states in neuronal networks is attracting a great deal of researchers' attention in recent days. The role of non-synaptic communication among neurons in order to bring chimera-like or other patterns have not been dealt with rigorously. The effect of Glial cells and astrocytes on neuronal connectivity deserves further research. The issues of information processing in complex neuronal networks and use of artificial intelligence like machine learning or deep learning are yet to be explained in detail. Therefore, as research on brain networks study has been getting developed fast, so it has become increasingly necessary to advance on this phenomenon with a dedicated special issue encircling both review works on existing studies and new original works.

Brain theory, neural and network modeling, and complex computations are essential to turn knowledge into better understanding of the brain, even though this is a formidable task. Generate circuit diagrams that vary in resolution from synapses to the whole brain. It is increasingly possible to map connected neurons in local circuits and distributed brain systems, enabling an understanding of the relationship between neuronal structure and function. Topics include but are not limited to the following:

• Mathematical modeling of neuronal diseases
• Pathogenesis and treatment of CNS disorders
• Information theory in neuroscience: From neurons to whole brain
• Brain connectivity analysis
• Functions of Astrocytes in cognitive and pathological states
• Synaptic Plasticity and memory formation
• Non-synaptic communication in neural structures
• Synchronization and resonance phenomena in neuroscience
• Chaos theory in neuroscience
• Artificial Intelligence in neuroscience
• Brain rhythms
• Advances in neural data analysis methods

The Guest Editors encourage contributors to submit their original research papers for this Article Collection. Articles may be one of the following types: (a) Original Research, (b) Reviews, (c) Minireviews.

Keywords: Modeling, Dynamical Systems, Complex Networks, Neuroscience, Synaptic and non-synaptic communications, Synchronization, Chaos, Information processing, Glial cells, Astrocytes, Artificial Intelligence.

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.

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