Generation of Music Patterns Using Realistic Artificial Neural Networks
DOI:
https://doi.org/10.61841/tvdktd95Keywords:
Realistic Neural Network, Ensemble of Nerve Cells, Self-Organized Criticality, Musical Texts, Piecewise Scaling, Physiological Music, Dynamic Clustering.Abstract
The goal of this study is to test the hypothesis about the models of the so-called realistic artificial neural networks that are in dynamic states, demonstrating behavior close to the self-organized criticality mode, and are capable of generating musical patterns. The patterns are sequences of sounds subjectively perceived by a person as musical, which on a more formal level should mean such an internal organization of sound sequences that would allow us to speak about the presence in them, in particular, of such musical characteristics as melody, harmony, or meter. An important condition for the generation of musical patterns is the absence of intervention in the dynamics of the neural network from the outside, like the training with the teacher. We talk about the numerical sequences, which subsequently are the basis for creating the sound series. The numerical sequences are determined solely by the internal dynamics of the model. The realism of the network means the comparability of the dynamic behavior of the model with the processes occurring in the ensembles of human cortex nerve cells. The model of a modified neural network by Kropotov-Pakhomov is used in our work. The algorithm for constructing musical patterns is described. Examples of both monophonic and polyphonic musical patterns, which are the result of interneuronic interaction in a neural network, are given, and free parameters of realistic artificial neural network models are studied and described. As a result, it was found that the complex dynamics of a realistic artificial neural network can be transformed and interpreted in terms of musical patterns. We defined the areas of the neural network parameters that do not allow us to transform the dynamics of the network into musical patterns that are far from the phases corresponding to the modes of self-organized criticality. Thus, the studies conducted in the work indirectly confirmed the fact known in neurophysiology and cognitive science that the normal (non-pathological) human brain functions successfully on the “order-chaos” border, namely in a state of self-organized criticality. The materials of the article are of practical value primarily for cognitive scientists and neuroscientists, who use quantitative methods in their research. They are also relevant for musicians, as they reveal some quantitative connections between music and consciousness.
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References
[1] Kropotov, Y.D., & Pakhomov, C.V. (1981). Mathematics modeling of signal processing mechanisms by
neural populations in the brain. Report II. The influence of synaptic plasticity on the properties of a neural
network with local connections in steady mode. Human Physiology, 7(1), 152-162.
[2] Kropotov, Y.D., & Pakhomov, C.V. (1983). Mathematics modeling of signal processing mechanisms by
neural populations in the brain. Report II. The influence of synaptic plasticity on the properties of a neural
network with local connections in steady mode. Human Physiology, 9(5), 787-795.
[3] Kropotov, Y.D., & Pakhomov, C.V. (1984). Mathematics modeling of signal processing mechanisms by
neural populations in the brain. Report III. Study of the caused reactions in neural ensembles. Human
Physiology, 10(5): 813-821.
[4] Nicholls, J.G., Martin, A.R., Fuchs, P.A., Brown, D.A., Diamond, M.E., & Weisblat, D.A. (2012). From
neuron to brain (5th ed.). Sunderland, US: Sinauer Associates.
[5] Mardanov, K.A., Pismak, Y.M., & Potyagailo, Y.M. (1997). General properties of realistic neural network
dynamics. Comp. Math. Appl., 34(7/8), 675-685.
[6] Chernykh, G.A., & Pismak, Y.M. (2001). A modified Hebb principle in the Kropotov neural network
model. Vestnik of Saint Petersburg University, 4(3/20), 104-107.
[7] Chernykh, G.A., & Pismak, Y.M. (2007). Dynamics of a modified neural network model by Kropotov-Pakhomov. Neuroinformatics, 2(1): 1-43.
[8] Chernykh, G.A., & Pismak, Y.M. (2012). Piecewise scaling in a model of neural network dynamics. In:
Adam, G., Buša, J., Hnatič, M. (eds). Mathematical Modeling and Computational Science. MMCP 2011.
Lecture Notes in Computer Science, 7125: 302-307.
[9] Krasnoperova, M.A., Pismak, Y.M., & Chernykh, G.A. (2001). About the possibility of using models of
neural networks in the study of text rhythms. Formal methods in linguistic poetics. Saint-Petersburg: St.
Petersburg University Press, 9-25.
[10] Chialvo, D.R. (2010). Emergent complex neural dynamics. Nature Physics, 6, 744-750.
[11] Hesse, J., & Gross, T. (2014). Self-organized criticality as a fundamental property of neural systems.
Frontiers in systems neuroscience, 8, 166.
[12] Cocchi, L., Gollo, L.L., Zalesky, A., & Breakspear, M. (2017). Criticality in the brain: A synthesis of
neurobiology, models and cognition. Progress in Neurobiology, 158, 132-152.
[13] Haro, M., Serrà, J., Corral, A., Herrera, P. (2012). Power-Law Distribution in Encoded MFCC Frames
Speech, Music, and Environmental Sound Signals. 21st International World Wide Web Conference (WWW):
4th International Workshop on Advances in Music Information Research (AdMIRe 2012).
[14] Standard MIDI, spec 1.1.
[15] Chernykh, G.A. (2019). Realistic neural network as a source of musical patterns.
[16] Bell, S., Gabora, L. (2016). A Music-generating System Inspired by the Science of Complex Adaptive
Systems. Proceedings of the 4th International Workshop on Musical Meta-creation (MUME 2016). Palo
Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press.
[17] Bell, S., Gabora, L. (2016). A music-generating system based on network theory. Proceedings of the 7th
International Conference on Computational Creativity. Sony CSL.
[18] Bogdanov, A.A. (1901). Knowledge from a historical point of view. Saint-Petersburg: A. Leifert Tipolithography.
[19] Hebb, D.O. (1949). The organization of behavior. A neuropsychological theory. Oxford, England: Wiley.
[20] Bak, P., & Sneppen, K. (1993). Punctuated equilibrium and criticality in a simple model of evolution.
Physical Review Letters, 71(24): 4083-4086.
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