IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i12p1247-d298999.html
   My bibliography  Save this article

Formulation of Pruning Maps with Rhythmic Neural Firing

Author

Listed:
  • Feng-Sheng Tsai

    (Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan
    Research Center for Interneural Computing, China Medical University Hospital, Taichung 40447, Taiwan)

  • Yi-Li Shih

    (Department of Information Management, Yuan Ze University, Chung-Li 32003, Taiwan)

  • Chin-Tzong Pang

    (Department of Information Management, Yuan Ze University, Chung-Li 32003, Taiwan)

  • Sheng-Yi Hsu

    (Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan
    Research Center for Interneural Computing, China Medical University Hospital, Taichung 40447, Taiwan)

Abstract

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

Suggested Citation

  • Feng-Sheng Tsai & Yi-Li Shih & Chin-Tzong Pang & Sheng-Yi Hsu, 2019. "Formulation of Pruning Maps with Rhythmic Neural Firing," Mathematics, MDPI, vol. 7(12), pages 1-15, December.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1247-:d:298999
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/12/1247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/12/1247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Germán Sumbre & Akira Muto & Herwig Baier & Mu-ming Poo, 2008. "Entrained rhythmic activities of neuronal ensembles as perceptual memory of time interval," Nature, Nature, vol. 456(7218), pages 102-106, November.
    2. Markus Diesmann & Marc-Oliver Gewaltig & Ad Aertsen, 1999. "Stable propagation of synchronous spiking in cortical neural networks," Nature, Nature, vol. 402(6761), pages 529-533, December.
    3. Leon Glass, 2001. "Synchronization and rhythmic processes in physiology," Nature, Nature, vol. 410(6825), pages 277-284, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ricardo Bioni Liberalquino & Maurizio Monge & Stefano Galatolo & Luigi Marangio, 2018. "Chaotic Itinerancy in Random Dynamical System Related to Associative Memory Models," Mathematics, MDPI, vol. 6(3), pages 1-10, March.
    2. Robert G. Sacco, 2019. "The Predictability of Synchronicity Experience: Results from a Survey of Jungian Analysts," International Journal of Psychological Studies, Canadian Center of Science and Education, vol. 11(3), pages 1-46, September.
    3. Alexey V. Rusakov & Dmitry A. Tikhonov & Nailya I. Nurieva & Alexander B. Medvinsky, 2021. "Emergence of Self-Organized Dynamical Domains in a Ring of Coupled Population Oscillators," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
    4. Meo, Marcos M. & Iaconis, Francisco R. & Del Punta, Jessica A. & Delrieux, Claudio A. & Gasaneo, Gustavo, 2024. "Multifractal information on reading eye tracking data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    5. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    6. Andrey Molyakov, 2019. "Mathematical Modeling of Neurodynamic Systems- Solving DIS-Tasks Using Massive-Multithread Supercomputers," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 21(5), pages 16159-16162, October.
    7. Reis, A.S. & Brugnago, E.L. & Viana, R.L. & Batista, A.M. & Iarosz, K.C. & Ferrari, F.A.S. & Caldas, I.L., 2023. "The role of the fitness model in the suppression of neuronal synchronous behavior with three-stage switching control in clustered networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    8. Emiliano Torre & Carlos Canova & Michael Denker & George Gerstein & Moritz Helias & Sonja Grün, 2016. "ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-34, July.
    9. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
    10. Gois, Sandra R.F.S.M. & Savi, Marcelo A., 2009. "An analysis of heart rhythm dynamics using a three-coupled oscillator model," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2553-2565.
    11. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar, 2016. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 197-203.
    12. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar & Mrowinski, Maciej J. & Fronczak, Piotr & Fronczak, Agata, 2017. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals. II. An ARCH econometric-like modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 462-474.
    13. Piassi, V.S.M. & Colli, E. & Tufaile, A. & Sartorelli, J.C., 2009. "Arnold family in acoustically forced air bubble formation," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1041-1049.
    14. Yao, Chenggui & Ma, Jun & He, Zhiwei & Qian, Yu & Liu, Liping, 2019. "Transmission and detection of biharmonic envelope signal in a feed-forward multilayer neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 797-806.
    15. Christopher K Rhea & Tobin A Silver & S Lee Hong & Joong Hyun Ryu & Breanna E Studenka & Charmayne M L Hughes & Jeffrey M Haddad, 2011. "Noise and Complexity in Human Postural Control: Interpreting the Different Estimations of Entropy," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-9, March.
    16. Cazelles, Bernard & Chavez, Mario & Courbage, Maurice, 2012. "Editorial," Chaos, Solitons & Fractals, Elsevier, vol. 45(5), pages 1-1.
    17. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.
    18. Gabriel Koch Ocker & Krešimir Josić & Eric Shea-Brown & Michael A Buice, 2017. "Linking structure and activity in nonlinear spiking networks," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-47, June.
    19. Thounaojam, Umeshkanta Singh & Manchanda, Kaustubh, 2023. "Continuous and explosive synchronization of phase oscillators on star network: Effect of degree-frequency correlations and time-delays," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    20. Wu, Yan & Wu, Liqing & Zhu, Yuan & Yi, Ming & Lu, Lulu, 2024. "Enhancing weak signal propagation by intra- and inter-layer global couplings in a feedforward network," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1247-:d:298999. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.