Spatiotemporal Patterns in Neurobiology: An Overview for Future Artificial Intelligence

TL;DR

This paper reviews various computational neural models that capture complex brain connectivity patterns across multiple scales, aiming to inform future AI development and validate neuroscience theories.

Contribution

It provides a comprehensive overview of neural modeling approaches and discusses their potential applications in advancing artificial intelligence and understanding brain functions.

Findings

Neural models help explore emergent properties of brain networks.

Models can simulate effects of mechanisms like plasticity on network function.

Insights from models can guide AI algorithm development.

Abstract

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.