Differential flatness for neuroscience population dynamics -- A preliminary study

TL;DR

This paper explores the application of differential flatness to neural population dynamics, aiming to enhance control and analysis methods for neural systems.

Contribution

It introduces the concept of differential flatness in neural population models and discusses potential applications like trajectory tracking and stability control.

Findings

Differential flatness can be identified in neural population models.

Potential applications include trajectory tracking and control switching.

The study provides a foundational framework for future research.

Abstract

The present document is devoted to structural properties of neural population dynamics and especially their differential flatness. Several applications of differential flatness in the present context can be envisioned, among which: trajectory tracking, feedforward to feedback switching, cyclic character, positivity and boundedness.