1). To understand the neural and biophysical mechanisms underlying the generation and learning of complex sequences
2). To develop advanced optical and electrical techniques for measurement of brain activity in behaving animals.
We study how the brain learns and generates sequential behaviors, with a focus on the songbird as a model system. We are currently trying to understand how circuitry in two forebrain nuclei, RA and HVC, produce the complex sequence of vocal/motor gestures that comprise the song. We have recently found neurons in nucleus HVC that generate only a single brief burst in the sequence, and may form an explicit representation of time in the brain.
Young songbirds learn their song by imitating a tutor. Young birds start by babbling, just as humans do, and gradually refine their highly variable juvenile songs to produce a good copy of the adult song. We are trying to understand the brain mechanisms that underlie this vocal imitation, and are focusing on a model called reinforcement learning. Reinforcement learning would suggest that the bird learns its song by ‘trial-and-error' search for the pattern of control parameters that will produce the correct song. We are currently exploring the neural origin of this ‘trial-and-error' search, and have identified a brain area that may be responsible for generating this creative vocal exploration.
We are also interested in developing advanced techniques for recording electrical and optical signals from neurons in behaving animals. Recently developed techniques include a 1.5 gram motorized microdrive for chronic recording, an active electrode stabilizer for intracellular recording in awake animals, and a miniature two-photon microscope for intracellular imaging in freely behaving animals.
Olveczky B., Andalman A.S., M.S. Fee. 2005. Vocal Experimentation in the Juvenile Songbird Requires a Basal Ganglia Circuit, PLoS Biology , 3(5): e153.
Luo, M., Fee M.S. and L.C. Katz. 2003. Encoding Pheromonal Signals in the Accessory Olfactory Bulb of Behaving Mice, Science 299: 1196-1201.
Hahnloser, R.H.R., Kozhevnikov, A.A. and M.S. Fee. 2002. An ultra-sparse code underlies the generation of neural sequences in a songbird, Nature 419: 65-70.