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Salva Ardid

Salva Ardid





Affiliation: Politechnic University of València

Fields or areas of research Computational neuroscience , Artificial Inteligence , Machine learning

My background is in Physics and Electric Engineering, which I studied at Universitat de València. I earned my PhD in Neuroscience in the Neuroscience Institute of Alacant at Universitat Miguel Hernández. 

After my PhD, I carried out postdoctoral research stays at Yale and Boston University in the United States, and York University in Canada.

In 2020, under the framework of the second generation of the CIDEGENT Program, I joined the Universitat Politècnica de València (UPV) to establish the "Lab of Natural and Designed Intelligence". Research in my Lab focuses on understanding mechanisms that support and facilitate learning, both in AI and in the brain.

The CIDEGENT research project I lead, called "Artificial General Intelligence: Beyond Deep Learning", intends to delve into Deep Learning and overcome the limitations it faces Today. Our approach is applying knowledge and techniques at the intersection of machine learning and computational neuroscience: we seek to adapt computationally efficient mechanisms of the brain into AI architectures, aiming for more powerful and explainable learning algorithms.

More particularly, we are testing the capabilities of the learning algorithm after introducing the following changes to the network: enhanced non-linearity of the network;  strengthened neurobiological substrate of the algorithm; and locally applied learning based on meta-learning principles. Using these techniques, we expect to improve the behavior of the algorithms in terms of flexibility, agility and robustness.

Within Neuroscience, we are highly interested in identifying physiological mechanisms of brain function and dysfunction, specifically in what relates to learning and cognition. To accomplish this, we simulate reinforcement learning models and biological neural networks. One of these studies focuses on cognitive dynamics, where we combine experimental procedures (psychophysics and neuroimaging) with computational approaches (machine learning and neural circuit modeling) to study the dynamics, development, and decline of human cognition across the life span.