Decrystallization of adult birdsong by perturbation of auditory feedback.

Young birds learn to sing by using auditory feedback to compare their own vocalizations to a memorized or innate song pattern; if they are deafened as juveniles, they will not develop normal songs. The completion of song development is called crystallization. After this stage, song shows little variation in its temporal or spectral properties. However, the mechanisms underlying this stability are largely unknown. Here we present evidence that auditory feedback is actively used in adulthood to maintain the stability of song structure. We found that perturbing auditory feedback during singing in adult zebra finches caused their song to deteriorate slowly. This 'decrystallization' consisted of a marked loss of the spectral and temporal stereotypy seen in crystallized song, including stuttering, creation, deletion and distortion of song syllables. After normal feedback was restored, these deviations gradually disappeared and the original song was recovered. Thus, adult birds that do not learn new songs nevertheless retain a significant amount of plasticity in the brain.     

Functional Cluster Analysis via Orthonormalized Gaussian Basis Expansions and Its Application

We propose functional cluster analysis (FCA) for multidimensional functional data sets, utilizing orthonormalized Gaussian basis functions. An essential point in FCA is the use of orthonormal bases that yield the identity matrix for the integral of the product of any two bases. We construct orthonormalized Gaussian basis functions using Cholesky decomposition and derive a property of Cholesky decomposition with respect to Gram-Schmidt orthonormalization. The advantages of the functional clustering are that it can be applied to the data observed at different time points for each subject, and the functional structure behind the data can be captured by removing the measurement errors. Numerical experiments are conducted to investigate the effectiveness of the proposed method, as compared to conventional discrete cluster analysis. The proposed method is applied to three-dimensional (3D) protein structural data that determine the 3D arrangement of amino acids in individual protein.