  In recent years, there has been increasing interest in developing models and
tools to address the complex patterns of connectivity found in brain tissue.
Specifically, this is due to a need to understand how emergent properties
emerge from these network structures at multiple spatiotemporal scales. We
argue that computational models are key tools for elucidating the possible
functionalities that can emerge from interactions of heterogeneous neurons
connected by complex networks on multi-scale temporal and spatial domains. Here
we review several classes of models including spiking neurons, integrate and
fire neurons with short term plasticity (STP), conductance based
integrate-and-fire models with STP, and population density neural field (PDNF)
models using simple examples with emphasis on neuroscience applications while
also providing some potential future research directions for AI. These
computational approaches allow us to explore the impact of changing underlying
mechanisms on resulting network function both experimentally as well as
theoretically. Thus we hope these studies will inform future developments in
artificial intelligence algorithms as well as help validate our understanding
of brain processes based on experiments in animals or humans.
