Critical Drift in a Neuro-Inspired Adaptive Network

It has been postulated that the brain operates in a self-organized critical state that brings multiple benefits, such as optimal sensitivity to input. Thus far, self-organized criticality has typically been depicted as a one-dimensional process, where one parameter is tuned to a critical value. However, the number of adjustable parameters in the brain is vast, and hence critical states can be expected to occupy a high -dimensional manifold inside a high-dimensional parameter space. Here, we show that adaptation rules inspired by homeostatic plasticity drive a neuro-inspired network to drift on a critical manifold, where the system is poised between inactivity and persistent activity. During the drift, global network parameters continue to change while the system remains at criticality.

Automated summary

It has been suggested that the brain operates in a self-organized critical state, which offers several advantages including optimal sensitivity to input. Traditionally, self-organized criticality has been viewed as a one-dimensional process, focusing on tuning one parameter to a critical value. However, considering the numerous adjustable parameters in the brain, it is likely that critical states exist in a high-dimensional manifold within a high-dimensional parameter space. This study demonstrates that adaptation rules based on homeostatic plasticity can drive a neuro-inspired network to drift on a critical manifold, maintaining the system in a poised state between inactivity and persistent activity, while global network parameters continue to change.