Fundamental Frequency; Voice; Hearing; Behavior; Learning
Narula Gagan, Herbst Joshua A., Rychen Joerg, Hahnloser Richard H. R. (2018), Learning auditory discriminations from observation is efficient but less robust than learning from experience, in Nature Communications
, 9(1), 3218-3218.
Lipkind D, Hanuschkin A, Zai AT, Marcus GF, Tchernichovski O, Hahnloser RHR (2017), Songbirds work around computational complexity by learning song vocabulary independently of sequence., in Nature Communication
, 8(1247), 1-11.
Canopoli Alessandro, Zai Anja, Hahnloser Richard (2017), Bilateral neurotoxic lesions in NCM before tutoring onset do not prevent successful tutor song learning, in Matters
Jovalekic A, Cavé-Lopez S, Canopoli A, Ondracek J M, Nager A, Vyssotski A L, Hahnloser R H R (2017), A lightweight feedback-controlled microdrive for chronic neural recordings, in Journal of Neural Engineering
, 14(2), 1-11.
Hahnloser Richard H. R., Narula Gagan (2017), A Bayesian Account of Vocal Adaptation to Pitch-Shifted Auditory Feedback, in PLOS ONE
, 12(1), e0169795-e0169795.
Vyssotski Alexei L., Stepien Anna E., Keller Georg B., Hahnloser Richard H. R. (2016), A Neural Code That Is Isometric to Vocal Output and Correlates with Its Sensory Consequences, in PLOS Biology
, 14(10), 1-21.
Lynch Galen F., Okubo Tatsuo S., Hanuschkin Alexander, Hahnloser Richard H.R., Fee Michale S. (2016), Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex, in Neuron
, 90(4), 877-892.
Canopoli Alessandro, Zai Anja, Hahnloser Richard (2016), Lesions of a higher auditory brain area during a sensorimotor period do not impair birdsong learning , in Matters (Zürich)
Nikolov N, Hu Y, Tan X, Hahnloser RHR, Character-level Chinese-English Translation through ASCII Encoding, in WMT2018
, BrusselsAssociation for Computational Linguistics (ACL), Stroudsburg, PA, USA.
Young songbirds learn to imitate complex vocal sequences with great precision. This feat requires the ability to memorize a song template and to precisely match the constituting individual notes in the correct temporal order. What algorithms do birds use to achieve this matching? In particular, how do they detect and correct for mismatch in the pitch (fundamental frequency) of their songs? Are there parallels between pitch correction in humans and in songbirds and do these obey some general principles? To provide answers to these questions we have designed neurophysiological experiments in songbirds and psychophysics experiments in humans. We will evaluate the data obtained in relation to a probabilistic theory of pitch processing we have begun to formulate.Our primary goal is to study how young birds use an internal song template to guide the development of their song motor program. We plan to make use of a recently introduced serial tutoring (ST) paradigm in which we expose young birds to a second sensory template after they master a first template. By introducing changes between the first and the second template in terms of both fundamental frequency (pitch) and syllable sequential order we are hoping to decipher the computational rules according to which birds can tune and sequence their songs. In addition to carefully inspecting the vocal trajectories that birds take to steer their songs away from the first template towards the second, we plan to study the neural mechanisms of this song learning by performing brain lesions and by recording from neurons while birds are listening to their tutors sing and while they are singing themselves. In addition, we plan to study pitch processing in humans in a simple psychophysics experiment in which we provide pitch-shifted feedback to speaking subjects. By analyzing how subjects adapt the pitch of their voice in this paradigm we can assess similarities/dissimilarities with pitch processing in birds and we can test for agreement/disagreement with our Bayesian theory of sensorimotor integration.