Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

TL;DR

This paper reviews how to model and simulate diverse synaptic dynamics in the brain, emphasizing empirical data integration, software implementation, and addressing challenges in synaptic signaling modeling.

Contribution

It introduces a comprehensive workflow for phenomenological modeling of heterogeneous synaptic behaviors based on empirical data and simulation techniques.

Findings

Effective models for synaptic transmission and plasticity

Insights into synaptic heterogeneity and network connectivity

Guidelines for addressing modeling challenges

Abstract

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement such models in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed.