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We are interested in understanding the principles and mechanisms used by neural circuits to generate complex, learned behaviors. To this end we use the songbird as a model system, concentrating our efforts on understanding the process of vocal learning.
Song learning and song production in the zebra finch is governed by a set of discrete, but interconnected brain regions, known collectively as the song circuit. To infer the role and function of specific parts of this circuit, we perturb it in various ways (electrical stimulation, pharmacology, etc.) and observe the effects on vocal behavior.
To understand how the song is represented in different parts of the song circuit and how this neural representation evolves as a function of learning, we record the spiking activity of single neurons in the singing bird using a motorized microdrive. We also combine electrical recordings with manipulations of nearby circuits to shed light on how neural responses in a given area are influenced and shaped by activity in adjacent brain regions.
Our experimental observations inform a theoretical model of song learning and song production that incorporates known anatomy, circuit mechanisms, and biophysics. We hope that the iterative process of formulating hypotheses, testing them, and using the results to update our model and generate new hypotheses, will ultimately lead to a mechanistic and biophysically plausible description of how a discrete neural circuit (the song system) can learn to produce a desired motor output (the song).
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