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Institute of Neuroinformatics

Birdsong and Natural Language Group


We are interested in the neurobiological algorithms inherent in vocal learning, especially of birdsong. To research vocal learning experimentally, we build novel observatories for groups of songbirds and to cope with the obtained massive data sets we adopt methods of statistical inference. The inferred song learning strategies we examine in the context of reinforcement learning theory, which is a modeling framework that is well matched to the organization of the brain. Overall, the premise for our work is that animal behavior provides clues about natural intelligence, i.e., the algorithms for solving a problem.

We also explore the relevance of our biological insights for natural language processing (NLP). We want to explore possible parallels between human language and birdsong and test the relevance of evolutionary vocal learning mechanisms for the processing of human language. We direct our NLP outreach efforts to helping researchers in assimilating the scientific literature and in generating scientific arguments.

We use our mixed educational backgrounds to advance knowledge at the boundary between biology and engineering, encompassing natural behavior, the organization of the songbird brain, computational theories of vocal learning, and the creation of new methods for processing natural language.

Our research

Weiterführende Informationen

Paper accepted at IEEE ICASSP 2024:

Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser, Positive transfer of the whisper speech transformation to human and animal voice activity detection

Nov 2023, Yingqiang Gao was awarded with the DocIU Cup Award (1st Place)

The challenge on Document-based Visual Question Answering (PDF-DQA) was awarded during the first Document Intelligence and Understanding (DocIU) Workshop at CIKM'23 (The 32nd International Conference on Information and Knowledge Management, Birmingham, UK). Congratulations!

Multimodal system for recording individual-level behaviors in songbird groups

L. Rüttimann, J. Rychen, T. Tomka, H. Hörster, M. D. Rocha, R.H.R. Hahnloser. 2022. bioRxiv

Oct 2022: New paper

Yingqiang Gao, Nianlong Gu, Jessica Lam, and Richard H.R. Hahnloser. 2022. Do Discourse Indicators Reflect the Main Arguments in Scientific Papers?. In Proceedings of the 9th Workshop on Argument Mining, pages 34–50, International Conference on Computational Linguistics.

Nianlong Gu, Elliott Ash, Richard H.R. Hahnloser MemSum: Extractive Summarization of Long Documents using Multi-step Episodic Markov Decision ProcessesProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Long papers., 2022

communication network

May 2021: A system for controlling vocal communication networks.

More about May 2021: A system for controlling vocal communication networks.

J. Rychen, D.I. Rodrigues, T. Tomka, L. Rüttimann, H. Yamahachi and R.H.R. Hahnloser (2021). A system for controlling vocal communication networks. Scientific Reports, 11(1).

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2020: New funding - NCCR on Evolving Language

More about 2020: New funding - NCCR on Evolving Language

The Swiss National Centre of Competence in Research (NCCR) Evolving Language is a nationwide interdisciplinary research consortium bringing together research groups from the humanities, from language and computer science, the social sciences, and the natural sciences at an unprecedented level.