Proceedings of the Annual Meeting of the Cognitive Science Society

The problem of stability has long been a limiting factor in de-veloping neural networks that can grow in size and complex-ity. Outside of particular, narrow parameter ranges, changesin activity can easily result in total loss of control. Humancognition must have reliable means of acting to stay withinthe stable ranges of sensitivity and activation. Learning is onesuch mechanism, and population dynamics are another. Here,we focus on another, often overlooked stability mechanism:cellular homeostasis through metabolism dynamics. We ran avisual change detection experiment designed to strain networkstability while minimizing any learnable patterns. We fit thedata using models with and without cellular energy levels as afactor, finding that the model influenced by its past history ofenergy use was a closer fit to the human data.