Andrei V. Bukh, Elena V. Rybalova, Igor A. Shepelev, Tatiyana E. Vadivasova
|
|
|
|
References
|
|
| |
| 1. |
Bregman, A. S., “Auditory Scene Analysis”, Proc. of the 7th Internat. Conf. on Pattern Recognition (Montreal, 1984), Vol. 1, 168–175 |
| 2. |
Houtsma, A. J. and Smurzynski, J., “Pitch Identification and Discrimination for Complex Tones with Many Harmonics”, J. Acoust. Soc. Am., 87:1 (1990), 304–310 |
| 3. |
Darwin, Ch. J., “Auditory Grouping”, Trends Cogn. Sci., 1:9 (1997), 327–333 |
| 4. |
de Cheveigné, A., McAdams, S., Laroche, J., and Rosenberg, M., “Identification of Concurrent Harmonic and Inharmonic Vowels: A Test of the Theory of Harmonic Cancellation and Enhancement”, J. Acoust. Soc. Am., 97:6 (1995), 3736–3748 |
| 5. |
Feng, A. S., Narins, P. M., Xu, C.-H., Lin, W.-Y., Yu, Z.-L., Qiu, Q., Xu, Z.-M., and Shen, J.-X., “Ultrasonic Communication in Frogs”, Nature, 440:7082 (2006), 333–336 |
| 6. |
Bates, M. E., Simmons, J. A., and Zorikov, T. V., “Bats Use Echo Harmonic Structure to Distinguish Their Targets from Background Clutter”, Science, 333:6042 (2011), 627–630 |
| 7. |
Deutsch, D. and Boulanger, R. C., “Octave Equivalence and the Immediate Recall of Pitch Sequences”, Music Percept., 2:1 (1984), 40–51 |
| 8. |
Borra, T., Versnel, H., Kemner, Ch., van Opstal, A. J., and van Ee, R., “Octave Effect in Auditory Attention”, Proc. Natl. Acad. Sci. USA, 110 (2013), 15225–15230 |
| 9. |
Malmberg, C. F., “The Perception of Consonance and Dissonance”, Psychol. Monogr., 25:2 (1918), 93–133 |
| 10. |
Krumhansl, C. L., “The Psychological Representation of Musical Pitch in a Tonal Context”, Cogn. Psychol., 11:3 (1979), 346–374 |
| 11. |
Glasberg, B. R. and Moore, B. C., “Derivation of Auditory Filter Shapes from Notched-Noise Data”, Hear. Res., 47:1–2 (1990), 103–138 |
| 12. |
Abeles, M. and Goldstein, M. H., “Responses of Single Units in the Primary Auditory Cortex of the Cat to Tones and to Tone Pairs”, Brain Res., 42:2 (1972), 337–352 |
| 13. |
Schwarz, D. W. and Tomlinson, R. W., “Spectral Response Patterns of Auditory Cortex Neurons to Harmonic Complex Tones in Alert Monkey (Macaca mulatta)”, J. Neurophysiol., 64:1 (1990), 282–298 |
| 14. |
Fishman, Y. I., Reser, D. H., Arezzo, J. C., and Steinschneider, M., “Pitch vs. Spectral Encoding of Harmonic Complex Tones in Primary Auditory Cortex of the Awake Monkey”, Brain Res., 786:1–2 (1998), 18–30 |
| 15. |
Kalluri, S., Depireux, D. A., and Shamma, S. A., “Perception and Cortical Neural Coding of Harmonic Fusion in Ferrets”, J. Acoust. Soc. Am., 123:5 (2008), 2701–2716 |
| 16. |
Sadagopan, S. and Wang, X., “Level Invariant Representation of Sounds by Populations of Neurons in Primary Auditory Cortex”, J. Neuroscience, 28:13 (2008), 3415–3426 |
| 17. |
Fishman, Y. I., Micheyl, Ch., and Steinschneider, M., “Neural Representation of Harmonic Complex Tones in Primary Auditory Cortex of the Awake Monkey”, J. Neuroscience, 33:25 (2013), 10312–1323 |
| 18. |
Goldstein, J. L., “An Optimum Processor Theory for the Central Formation of the Pitch of Complex Tones”, J. Acoust. Soc. Am., 54:6 (1973), 1496–1516 |
| 19. |
Cohen, M. A., Grossberg, S., and Wyse, L. L., “A Spectral Network Model of Pitch Perception”, J. Acoust. Soc. Am., 98:2 (1995), 862–879 |
| 20. |
Feng, L. and Wang, X., “Harmonic Template Neurons in Primate Auditory Cortex Underlying Complex Sound Processing”, Proc. Natl. Acad. Sci. USA, 114:5 (2017), E840–E848 |
| 21. |
Montes-Lourido, P., Kar, M., David, S. V., and Sadagopan, S., “Neuronal Selectivity to Complex Vocalization Features Emerges in the Superficial Layers of Primary Auditory Cortex”, PLoS Biol., 19:6 (2021), e3001299, 30 pp. |
| 22. |
Gütig, R., “To Spike, or When to Spike?”, Curr. Opin. Neurobiol., 25 (2014), 134–139 |
| 23. |
Beyeler, M., Dutt, N. D., and Krichmar, J. L., “Categorization and Decision-Making in a Neurobiologically Plausible Spiking Network Using a STDP-Like Learning Rule”, Neural Netw., 48 (2013), 109–124 |
| 24. |
Kulkarni, S. R. and Rajendran, B., “Spiking Neural Networks for Handwritten Digit Recognition: Supervised Learning and Network Optimization”, Neural Netw., 103 (2018), 118–127 |
| 25. |
Tuckwell, H. C., “Synaptic Transmission in a Model for Stochastic Neural Activity”, J. Theor. Biol., 77:1 (1979), 65–81 |
| 26. |
Izhikevich, E. M., “Simple Model of Spiking Neurons”, IEEE Trans. Neural Netw., 14:6 (2003), 1569–1572 |
| 27. |
Rose, R. M. and Hindmarsh, J. L., “The Assembly of Ionic Currents in a Thalamic Neuron: 1. The Three-Dimensional Model”, Proc. R. Soc. Lond. B Biol. Sci., 237:1288 (1989), 267–288 |
| 28. |
Hodgkin, A. L. and Huxley, A. F., “A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve”, J. Physiol., 117:4 (1952), 500–544 |
| 29. |
FitzHugh, R., “Impulses and Physiological States in Theoretical Models of Nerve Membrane”, Biophys. J., 1:6 (1961), 445–466 |
| 30. |
Nagumo, J., Arimoto, S., and Yoshizawa, S., “An Active Pulse Transmission Line Simulating Nerve Axon”, Proc. IRE, 50:10 (1962), 2061–2070 |
| 31. |
Semenova, N., Zakharova, A., Anishchenko, V., and Schöll, E., “Coherence-Resonance Chimeras in a Network of Excitable Elements”, Phys. Rev. Lett., 117:1 (2016), 014102, 6 pp. |
| 32. |
Guo, S., Dai, Q., Cheng, H., Li, H., Xie, F., and Yang, J., “Spiral Wave Chimera in Two-Dimensional Nonlocally Coupled Fitzhugh – Nagumo Systems”, Chaos Solitons Fractals, 114 (2018), 394–399 |
| 33. |
Plotnikov, S. A. and Fradkov, A. L., “On Synchronization in Heterogeneous FitzHugh – Nagumo Networks”, Chaos Solitons Fractals, 121 (2019), 85–91 |
| 34. |
Rybalova, E. V., Vadivasova, T. E., Strelkova, G. I., Anishchenko, V. S., and Zakharova, A. S., “Forced Synchronization of a Multilayer Heterogeneous Network of Chaotic Maps in the Chimera State Mode”, Chaos, 29:3 (2019), 033134, 9 pp. |
| 35. |
Carletti, T. and Nakao, H., “Turing Patterns in a Network-Reduced FitzHugh – Nagumo Model”, Phys. Rev. E, 101:2 (2020), 022203, 12 pp. |
| 36. |
Hussain, I., Jafari, S., Ghosh, D., and Perc, M., “Synchronization and Chimeras in a Network of Photosensitive FitzHugh – Nagumo Neurons”, Nonlinear Dyn., 104 (2021), 2711–2721 |
| 37. |
Doruk, R. O. and Abosharb, L., “Estimating the Parameters of Fitzhugh – Nagumo Neurons from Neural Spiking Data”, Brain Sci., 9:12 (2019), Art. 364, 19 pp. |
| 38. |
Klinshov, V. V., Kovalchuk, A. V., Soloviev, I. A., Maslennikov, O. V., Franović, I., and Perc, M., “Extending Dynamic Memory of Spiking Neuron Networks”, Chaos Solitons Fractals, 182 (2024), Paper No. 114850, 10 pp. |
| 39. |
Wu, J., Chua, Y., Zhang, M., Li, H., and Tan, K. C., “A Spiking Neural Network Framework for Robust Sound Classification”, Front. Neurosci., 12 (2018), Art. 836, 17 pp. |
| 40. |
Tavanaei, A. and Maida, A. S., “Training a Hidden Markov Model with a Bayesian Spiking Neural Network”, J. Sign. Process. Syst., 90 (2018), 211–220 |
| 41. |
Wade, J. J., McDaid, L. J., Santos, J. A., and Sayers, H. M., “SWAT: A Spiking Neural Network Training Algorithm for Classification Problems”, IEEE Trans. Neural Netw., 21:11 (2010), 1817–1830 |
| 42. |
Wu, J., Xu, C., Han, X., Zhou, D., Zhang, M., Li, H., and Tan, K. C., “Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks”, IEEE Trans. Pattern Anal. Mach. Intell., 44:11 (2022), 7824–7840 |
| 43. |
Pan, Z., Zhang, M., Wu, J., Wang, J., and Li, H., “Multi-Tone Phase Coding of Interaural Time Difference for Sound Source Localization with Spiking Neural Networks”, IEEE/ACM Trans. Audio Speech Lang. Process., 29 (2021), 2656–2670 |
| 44. |
Bukh, A. V., Rybalova, E. V., Shepelev, I. A., and Vadivasova, T. E., “Classification of Musical Intervals by Spiking Neural Networks: Perfect Student in Solfége Classes”, Chaos, 34:6 (2024), Paper No. 063102, 9 pp. |