# System-level brain modeling

## TL;DR

This paper explains how system-level brain modeling helps create brain models that can produce measurable behavior for scientific testing.

## Contribution

The paper introduces system-level brain modeling as a method to bridge detailed neuronal models and abstract cognitive models.

## Key findings

- System-level brain models resemble the brain structurally and functionally.
- These models allow for thorough testing and evaluation of cognitive and behavioral phenomena.
- The paper emphasizes modeling components and their connections to produce measurable behavior.

## Abstract

System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing system-level brain models, several questions need to be addressed. What are the components of the system? At what level should these components be modeled? How are the components connected—that is, what is the structure of the system? What is the function of each component? What kind of information flows between the components, and how is that information coded? We mainly address models of cognitive abilities or subsystems that produce measurable behavior rather than models that to reproduce internal states, signals or activation patterns. In this method paper, we argue that system-level modeling is an excellent method for addressing complex cognitive and behavioral phenomena.