Neural network synchronization affected by delay long-range connection and neuronal properties
Lionel Kusch, Martin Breyton, Spase Petkoski, Viktor Jirsa · Zenodo (CERN European Organization for Nuclear Research) · 2023
This study investigates the synchronization of a spiking neural network in the presence of a large-scale, fibre-like connection with time delay. Neural field models use long connections to capture the spatiotemporal dynamics of the brain, while spiking neural networks typically do not integrate patchy remote connections with transmission speed. To bridge the gap between these two different modelling approaches, this study identifies the synchronization conditions and the role of the delay of a two-dimensional network model composed of adaptive exponential integrate and fire neurons. Through parameter exploration of neuronal and network properties, this study provides insight into the link between synchronization and transmission speed with unidirectional long-range connections. However, for bidirectional connections, the spatiotemporal patterns are more complex and difficult to interpret.