Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties - Nobukawa, Sou (Frontiers in Applied Mathematics and Statistics, 2022)
Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity. These studies have been proceeded at multiple spatial-temporal scale levels among spikes at the neural microcircuit level, neural activity at the intra-brain regional level, and neural interactions at the whole-brain network level. One of the structural and functional neural characteristics widely observed among large spatial-temporal scale ranges is long-tail distribution, typified as power-low distribution, gamma distribution, and log-normal distribution. In particular, long-tailed distributions found in excitatory postsynaptic potentials (EPSP) induce various types of neural dynamics and functions. We reviewed recent studies on neural dynamics produced by the structural long-tailed characteristics of brain neural networks. In particular, the spiking neural network with a log-normal EPSP distribution was first introduced for the essential factors to produce spontaneous activity and was extended and utilized for studies on the association of neural dynamics with the network topology depending on EPSP amplitude. Furthermore, the characteristics of the response to a steady stimulus and its dependence on E/I balance, which are widely observed under pathological conditions, were described by the spiking neural networks with EPSP long-tailed distribution. Moreover, this spiking neural network has been utilized in modeling studies of mutual interactions among local microcircuit circuits. In future studies, the implementation of more global brain network architectures in modeling studies might reveal the mechanisms by which brain dynamics and brain functions emerge from the whole brain network architecture.