Undergraduate Research & Scholarships

S. Zayd Enam Rose Hills

Using Machine Learning Techniques to Fit Receptive Fields of Speech Spectrogram Trained Auditory Neurons

Using the tools of machine learning one can determine parameters of a model that probabilistically best fit experimentally collected data. This gives us insight into determining the model that best describes the data. I will be using machine learning to fit models of receptive fields of auditory neurons (a receptive field is any stimulus that maximizes activation of a neuron) that have been generated using a sparse-coding model. In this case, a sparse-coding model is one that minimizes the number of neurons required to represent different sounds. Sparse-coding models have been shown to accurately predict the receptive fields of auditory neurons in the Inferior Colliculus. It will be my job to determine a mathematical model that best describes the receptive fields of these neurons.

Message To Sponsor

I am extremely thankful to the SURF Rose Hills committee for awarding this fellowship. It has granted me the freedom to focus on a neuroscience research problem that I am passionate about and has given me the resources to tackle this problem with a powerful support structure. This award will do wonders to the refinement of my research abilities and will help me focus on the long-term goal of determining the big problems I will tackle in my research career. Thank you for making working on world-class research accessible to undergraduates.
Profile image of S. Zayd Enam
Major: Electrical Engineering, Computer Science
Mentor: Michael DeWeese, Electrical Engineering & Computer Science
Sponsor: Rose Hills Foundation
Back to Listings
Back to Donor Reports