Topic: Network Neuroscience

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing

One of the important subfields of computational neuroscience is network neuroscience, which widely uses machine learning techniques, these include artificial neural networks. The latter ones owe a lot to neuroscience in the history of their development. Artificial neural networks (ANN) are often represented in the form of a graph – a visual representation of connections between the elements of the system, which form a network. However, this does not mean that an ANN was designed based on graph theory or with consideration of the special properties of complex networks. These representations ar