**Scientists Discover How Animals Differentiate Similar Scents, Inspiring AI Advancements**
Scientists from Cold Spring Harbor Laboratory (CSHL) and the Salk Institute have uncovered how animals are able to differentiate between distinct scents, even ones that may seem remarkably similar. This breakthrough discovery could potentially enhance machine-learning models used in artificial intelligence (AI) systems.
The researchers identified two types of neurons involved in scent differentiation: “reliable cells” and “unreliable cells”. The reliable cells are responsible for identifying distinct odors, while the unreliable cells aid in distinguishing similar scents through experience. The variability in neural response was found to originate from a deeper brain circuit, suggesting that it serves a significant purpose.
The study, inspired by previous research on fruit flies, sheds light on how animals, such as fruit flies and mice, are able to discern complex aromas. The team observed that with experience, a group of neurons helps animals distinguish between very similar odors. This finding challenges the previous notion that such variations in neural response were merely background noise.
Professor Saket Navlakha from CSHL explains, “There were two things we were interested in: Where is this variability coming from? And is it good for anything?” Through a fruit fly smell model, the researchers determined that the variations in neural response were meaningful and provided an advantage in distinguishing between different smells.
The researchers also believe that this discovery could have implications for AI and machine-learning models. Unlike animals, computers typically respond the same to identical inputs. By introducing variability inspired by the natural discernment found in animals, AI systems could become more adept at distinguishing nuanced information.
“This research could someday help make AI more discerning and reliable,” suggests Navlakha.
Furthermore, the findings of this study may contribute to a better understanding of how humans differentiate between similar sensations detected by other senses and how they make decisions based on sensory inputs.
The research, led by Saket Navlakha and Shyam Srinivasan, holds potential for a wide range of applications, from wine connoisseurship to the development of advanced AI technologies. Further exploration of these findings could open doors to new possibilities in various fields utilizing scent differentiation and machine learning.
The study will be published in the scientific journal PLOS Biology.
**About the Author:**
Samuel Diamond is a science writer for CSHL, the source of this research. For inquiries, please contact Samuel Diamond at CSHL.
**Image Credit:**
Neuroscience News
**Original Research:**
The findings will appear in PLOS Biology.