Yang Shen: Uncovering continual learning mechanisms from olfactory circuit in Drosophila (1/25/24)

Presented through the Chalk Talks series of the Institute for Neural Computation (UC San Diego)

Abstract:
A key feature of intelligence is the ability to continuously adapt to environmental changes and acquire new skills while preserving previously acquired knowledge. This adaptive ability, however, remains a challenge for current artificial intelligence (AI) algorithms. For example, changing the training regime of artificial neural networks from a mixed to a sequential data input format markedly deteriorates their performance. This decrease of performance is attributed to a phenomenon known as “catastrophic forgetting”, where artificial neural networks “forget” previously learned information upon learning new content. In contrast, biological neural systems, exemplified by even simple organisms like fruit flies, exhibit robust adaptability and sustained learning abilities. These systems efficiently retain and build upon existing memories while integrating new information, an attribute critical for continual learning. Given the simplicity of the fruit fly’s neural circuitry, which is well-studied through extensive research, and their ability to perform complex tasks, they present an ideal model for exploring neural information processing mechanisms. In this talk, I will delve into the intrinsic features of the olfactory circuit in the fly and discuss how insights from the study of fruit fly neurology can inform and enhance the development of more adaptable AI systems.

Bio:
Yang got her PhD in Chemical Physics from the University of Maryland, College Park. She is now a postdoctoral fellow at the Simons Center for Quantitative Biology in Cold Spring Harbor Laboratory. She was a recipient of the Swartz Foundation Postdoctoral Fellowship. Her research aims to understand how features of neural circuits, such as circuit architecture and synaptic plasticity rules, enable the brain to adapt and learn continuously and translate the biological insights into effective machine learning algorithms with better performance in continual learning, paving the way for more adaptable, efficient, and robust artificial agents.