Todd, P. M. A connectionist approach to algorithmic composition. Comput. Music. J. 13, 27–43 (1989).
Mozer, M. C. Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connect. Sci. 6, 247–280. https://doi.org/10.1080/09540099408915726 (1994).
Eck, D. & Schmidhuber, J. A first look at music composition using LSTM recurrent neural networks. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale 103, 48 (2002).
Waite, E. Generating long-term structure in songs and stories (2016). https://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn/.
Boulanger-Lewandowski, N., Bengio, Y. & Vincent, P. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392 (2012).
Hadjeres, G., Pachet, F. & Nielsen, F. Deepbach: a steerable model for bach chorales generation. In International Conference on Machine Learning, 1362–1371 (PMLR, 2017).
Briot, J.-P., Hadjeres, G. & Pachet, F.-D. Deep Learning Techniques for Music Generation, vol. 1 (Springer, 2020).
Ji, S., Yang, X. & Luo, J. A survey on deep learning for symbolic music generation: Representations, algorithms, evaluations, and challenges. ACM Comput. Surv. 56, 1–39 (2023).
Wang, Z. et al. Structured representation learning for polyphonic music. In International Society for Music Information Retrieval Conference, 368–375 (2020).
Shiqi Wei, G. X. Learning long-term music representations via hierarchical contextual constraints. In International Society for Music Information Retrieval Conference, 738–745 (2022).
Roberts, A., Engel, J., Raffel, C., Hawthorne, C. & Eck, D. A hierarchical latent vector model for learning long-term structure in music. In International Conference on Machine Learning, 4364–4373 (PMLR, 2018).
Trieu, N. & Keller, R. M. Jazzgan: Improvising with generative adversarial networks. In In Proc. of the 6th International Workshop on Musical Metacreation (MUME) (2018).
Dong, H.-W., Hsiao, W.-Y., Yang, L.-C. & Yang, Y.-H. Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018).
Yang, L.-C., Chou, S.-Y. & Yang, Y.-H. Midinet: A convolutional generative adversarial network for symbolic-domain music generation. In International Society for Music Information Retrieval Conference, 324–331 (2017).
Huang, Y.-S. & Yang, Y.-H. Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions. In Proceedings of the 28th ACM International Conference on Multimedia, 1180–1188 (2020).
Hernandez-Olivan, C., Hernandez-Olivan, J. & Beltrán, J. R. A survey on artificial intelligence for music generation: Agents, domains and perspectives. arXiv.org https://doi.org/10.48550/arxiv.2210.13944 (2022).
Payne, C. Musenet (2019). http://openai.com/blog/musenet.
Xu, W., McAuley, J., Berg-Kirkpatrick, T., Dubnov, S. & Dong, H.-W. Generating symbolic music from natural language prompts using an llm-enhanced dataset. arXiv preprint arXiv:2410.02084 (2024).
Wang, Y. et al. Notagen: Advancing musicality in symbolic music generation with large language model training paradigms (2025). arXiv:2502.18008.
Samek, W., Wiegand, T. & Müller, K.-R. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models (2017). arXiv:1708.08296.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behav. Brain Sci. 40, e253. https://doi.org/10.1017/S0140525X16001837 (2017).
Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nat. Commun. 14, 1597. https://doi.org/10.1038/s41467-023-37180-x (2023).
Krumhansl, C. L. Cognitive Foundations of Musical Pitch (Oxford University Press, 1990).
Temperley, D. What’s key for key? The Krumhansl–Schmuckler key-finding algorithm reconsidered. Music. Percept. 17, 65–100 (1999).
Gilboa, A. & Marlatte, H. Neurobiology of schemas and schema-mediated memory. Trends Cogn. Sci. 21, 618–631 (2017).
van Kesteren, M. T., Ruiter, D. J., Fernández, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci. 35, 211–219. https://doi.org/10.1016/j.tins.2012.02.001 (2012).
Preston, A. R. & Eichenbaum, H. Interplay of hippocampus and prefrontal cortex in memory. Curr. Biol. 23, R764–R773 (2013).
Van Kesteren, M. T., Ruiter, D. J., Fernández, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci. 35, 211–219 (2012).
Brod, G., Werkle-Bergner, M. & Shing, Y. L. The influence of prior knowledge on memory: a developmental cognitive neuroscience perspective. Front. Behav. Neurosci. 7, 139 (2013).
Tubridy, S. & Davachi, L. Medial temporal lobe contributions to episodic sequence encoding. Cereb. Cortex 21, 272–280. https://doi.org/10.1093/cercor/bhq092 (2011).
McAndrews, M. P. & Milner, B. The frontal cortex and memory for temporal order. Neuropsychologia 29, 849–859. https://doi.org/10.1016/0028-3932(91)90051-9 (1991).
Meier, B. et al. Implicit task sequence learning in patients with Parkinson’s disease, frontal lesions and amnesia: The critical role of fronto–striatal loops. Neuropsychologia 51, 3014–3024. https://doi.org/10.1016/j.neuropsychologia.2013.10.009 (2013).
Jenkins, L. J. & Ranganath, C. Prefrontal and medial temporal lobe activity at encoding predicts temporal context memory. J. Neurosci. 30, 15558–15565. https://doi.org/10.1523/JNEUROSCI.1337-10.2010 (2010).
Vuust, P., Heggli, O. A., Friston, K. J. & Kringelbach, M. L. Music in the brain. Nat. Rev. Neurosci. 23, 287–305 (2022).
Purves, D. et al. The auditory cortex. In Neuroscience, 2nd edn (Sinauer Associates, 2001).
Sankaran, N., Leonard, M. K., Theunissen, F. & Chang, E. F. Encoding of melody in the human auditory cortex. Sci. Adv. 10, eadk0010 (2024).
McDermott, J. H. & Oxenham, A. J. Music perception, pitch, and the auditory system. Curr. Opin. Neurobiol. 18, 452–463 (2008).
Merchant, H., Harrington, D. L. & Meck, W. H. Neural basis of the perception and estimation of time. Annu. Rev. Neurosci. 36, 313–336 (2013).
Gupta, D. S. Processing of sub-and supra-second intervals in the primate brain results from the calibration of neuronal oscillators via sensory, motor, and feedback processes. Front. Psychol. 5, 816 (2014).
MacDonald, C. J., Lepage, K. Q., Eden, U. T. & Eichenbaum, H. Hippocampal, “time cells’’ bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).
Liang, Q., Zeng, Y. & Xu, B. Temporal-sequential learning with a brain-inspired spiking neural network and its application to musical memory. Front. Comput. Neurosci. 14, 51. https://doi.org/10.3389/fncom.2020.00051 (2020).
Liang, Q. & Zeng, Y. Stylistic composition of melodies based on a brain-inspired spiking neural network. Front. Syst. Neurosci. 15, 639484. https://doi.org/10.3389/fnsys.2021.639484 (2021).
Zeng, Y. et al. Braincog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation. Patterns 4, 100789. https://doi.org/10.1016/j.patter.2023.100789 (2023).
Izhikevich, E. M. Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569. https://doi.org/10.1109/TNN.2003.820440 (2003).
Spitzer, N. C. Electrical activity in early neuronal development. Nature 444, 707–712 (2006).
Hua, J. Y. & Smith, S. J. Neural activity and the dynamics of central nervous system development. Nat. Neurosci. 7, 327–332. https://doi.org/10.1038/nn1215 (2004).
Zheng, J. Q. & ming Poo, M. Calcium signaling in neuronal motility. Annu. Rev. Cell Dev. Biol. 23, 375–404. https://doi.org/10.1146/annurev.cellbio.23.090506.123221 (2007).
Bi, G.-Q. & Poo, M.-M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998 (1998).
Hannon, E. E. & Trainor, L. J. Music acquisition: effects of enculturation and formal training on development. Trends Cogn. Sci. 11, 466–472 (2007).
Herholz, S. C. & Zatorre, R. J. Musical training as a framework for brain plasticity: Behavior, function, and structure. Neuron 76, 486–502 (2012).
Dubovsky, I., Yevseev, S. V., Sposobin, I. V. & Sokolov, V. V. Textbook of Harmony (Parts I & II) (People’s Music Publishing House, 1991).
Huang, H. Exercises and Answers for Harmony Textbook, vol. I (People’s Music Publishing House, 2011).
Cuthbert, M. S. & Ariza, C. music21: A toolkit for computer-aided musicology and symbolic music data. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010) (2010).
Yang, L.-C. & Lerch, A. On the evaluation of generative models in music. Neural Comput. Appl. 32, 4773–4784 (2020).
Dong, H.-W., Chen, K., McAuley, J. & Berg-Kirkpatrick, T. Muspy: A toolkit for symbolic music generation. arXiv preprint arXiv:2008.01951 (2020).
Hernandez-Olivan, C. & Beltran, J. R. Musicaiz: A python library for symbolic music generation, analysis and visualization. SoftwareX 22, 101365 (2023).
Temperley, D. Music and Probability (MIT Press, 2007).
Temperley, D. A probabilistic model of melody perception. Cogn. Sci. 32, 418–444. https://doi.org/10.1080/03640210701863332 (2008).
Krumhansl, C. et al. Cross-cultural music cognition: cognitive methodology applied to north sami yoiks. Cognition 76, 13–58 (2000).
You, S. et al. Physics of life reviews. 53, 131–133. https://doi.org/10.1016/j.plrev.2025.03.002 (2025).
Koelsch, S., Vuust, P. & Friston, K. Predictive processes and the peculiar case of music. Trends Cogn. Sci. 23, 63–77. https://doi.org/10.1038/s41583-022-00578-5 (2019).