Popular lecture for school children, Devotion elementary school, Brookline, MA
My presentations in a local elementary school, “What can we learn from songbirds?” aim to communicate the passion for science and describe some of the questions we have and how songbirds can help us answer them in the lab.
Tutorial, SFN Birdsong sattelite conference, San Diego CA, USA
Songbirds provide an excellent model system for investigating learned motor skills. Because they sing spontaneously, songbirds produce terabytes of behavioral data. Many analyses require labeling the elements of song, called syllables. Labeling syllables by hand consumes many hours, and labeling all the song is often infeasible, preventing full analysis of this data.
Talk, PyData 2017, New York City, USA
Neural networks for speech-to-text avoid dividing speech into segments, such as syllables, but segmenting has important applications. We compare different neural networks for segmentation of vocalizations using the song of songbirds, which we study as neuroscientists. Initial results suggest a bidirectional LSTM-CNN architecture outperforms others in both segmentation and classification.
Talk, Minna James Heinemann workshop, Weizmann Institute of Science, Rehovot, Israel
Categorizing stimuli imbues the world with structure, yet we have little knowledge of how our brain learns classification that is based on complex labeling rules. Here, we propose a theoretical framework that allows feature-based modeling of such behavior, and use modeling of human behavior and then recordings in behaving primates to explore the mechanisms.