Time series analysis of the signals from the biological system is essential to understand the system’s dynamics. The birdsong data is examined using the
Hurst exponents and multifractal analysis to uncover its correlation structure. The analysis reveals that temporal correlations contribute to the
multifractal behavior observed in the data. The vocal development of songbirds is also studied, and recurrence plots of MFCC coefficients show the
gradual development of recurrent structures in syllabic sequences as vocal development progresses. In the case of human brain fMRI data during a
reading task, a complex network approach is used. The time series data is transformed into a network using the visibility algorithm, and further analysis
is conducted using simplicial analysis. The f-vector of the simplicial networks captures structural changes in the brain activity of adults and children
during reading tasks in different languages. Children exhibit higher-dimensional simplicial structures compared to adults during similar tasks.
References: i) Multifractal analysis of birdsong and its correlation structure, Rabindev Bishal, Gabriel B. Mindlin, and Neelima Gupte, Phys. Rev. E 105,
014118 (2022) (https://doi.org/10.1103/PhysRevE.105.014118) ii) A simplicial analysis of the fMRI data from human brain dynamics under
functional cognitive tasks, Rabindev Bishal, Sarika Cherodath, Nandini Chatterjee Singh, and Neelima Gupte, Front. Netw. Physiol.,Volume 2 - 2022
(https://www.frontiersin.org/articles/10.3389/fnetp. 2022.924446) iii) Recurrence study of the early vocal development of a juvenile canary song,
Rabindev Bishal, Jorge Aliende, Emiliano Marachlian, Gabriel B. Mindlin, and Neelima Gupte [Manuscript under preparation]