A short biography

Pedro E. Maldonado, PhD, is a Full Professor within the Faculty of Medicine’s Department of Neuroscience at the University of Chile, where he directs the Neurosystems Laboratory. He serves as a Principal Investigator at the National Center for Artificial Intelligence (CENIA) and is an Associate Researcher at the Millennium Institute of Biomedical Neuroscience (BNI). Dr. Maldonado’s academic background includes a Bachelor in Biology and a Master in Biological Sciences from the University of Chile (1986). He earned his Doctorate in Physiology from the University of Pennsylvania in 1993. He subsequently conducted postdoctoral research at the University of California, Davis, Neuroscience Center before joining the University of Chile’s Medical School in 1997. His research primarily explores the neural foundations of visual perception, mechanism of active sensing and brain-inspired artificial intelligence.

Abstract:

Exploring Energy Dependency within Artificial and Cultured Neuronal Networks.

As artificial intelligence and machine learning technologies are projected to consume increasingly significant amounts of energy, potentially leading to  costly, inefficient, and unsustainable systems. Drawing inspiration from the brain’s exceptional energy efficiency, this series of studies seeks to demonstrate that the execution of simple behavioral tasks by biological neuronal networks is dependent upon the precise regulation of energy homeostasis within individual neurons. Furthermore, we aim to show that Artificial Neural Networks incorporating energy-dependent constraints demonstrate enhanced efficiency in learning behavioral tasks. This presentation will cover the theoretical framework of the project and present recent findings regarding the metabolic dependence of cultured neurons, specifically as observed through gene expression and synaptic connectivity. We will also provide electrophysiological data from neural cultures concerning changes in energy availability, alongside advances in computational modeling for Artificial Neural Networks that utilize energy-dependent learning mechanisms.