From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction.

Peter John Hellyer,Angie A Kehagia,Federico E Turkheimer,Robert Leech,Claudia Clopath,Daniele Marinazzo

doi:10.1371/journal.pcbi.1005721

Abstract

In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional “task negative” activity that compensates for “task positive”, sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic “task-negative” patterns of activity (e.g., the default mode network).

Highlights

In recent years, empirical and theoretical work indicates that homeostatic mechanisms play an important role in the regulation of neural activity
There has been growing interest in using computational models based on the human structural connectome to better understand the brain
We observe interesting brain-environment feedback interactions and observe how different homeostatic systems are needed to compensate for this feedback

Summary

Introduction

Empirical and theoretical work indicates that homeostatic mechanisms play an important role in the regulation of neural activity. Computational simulations have suggested the importance of homeostatic mechanisms in regulating neural dynamics; facilitating complex patterns of neural activity (e.g., [15,16]). Such computational models typically simulate the brain at rest or under highly constrained task settings. Two pairs of bilateral nodes reacted to “visual” input from the environment to the model and one pair of “somatosensory” nodes activated if the agent collided with the bounding walls of the environment. This set-up leads to brain/environment interactions as follows (Fig 2): 1. This set-up leads to brain/environment interactions as follows (Fig 2): 1. Sensory input from the environment evokes regionally specific visual and sensory activity within the model

Methods

Results

Discussion

Conclusion