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
- Major: Electrical Engineering, Computer Science
- Sponsor: Rose Hills Foundation
- Mentor: Michael DeWeese, Electrical Engineering & Computer Science