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Research to Short- and Long Term Memory Effects of Three Nerve Cell Motif Based Neuronal Networks

Year 2019, , 553 - 567, 31.08.2019
https://doi.org/10.18185/erzifbed.425620

Abstract

There are
important biological studies on memory and learning. Examination of brain cells
in solution and living tissue media and also image studies with electron
microscope revealed important findings. The obtained biological data showed
that many nerve cells in the brain formed complex connections. In this great
biological neural network structure; repeated and motif-named sections were
observed. These motifs are believed to be the building blocks of biological
neural networks and play important functional roles in the network. It is
thought that in order to understand the memory behavior of this nerve network
in the brain, it is necessary to know the behavior of the motifs. With the help
of these experimental data, the motifs are modeled by computational methods.
From studies done on models; biological memory behaviors of motifs are
examined. In this study, short- and long-term memory functions of all
three-cell neural network motifs were examined. Motives were created using
graph theory. The nerve cells forming the motifs were modeled in the soma part
of the cell using a Hodkgin-Huxley model as a single chamber. In addition, the
intercellular connection was modeled as a chemical synapse. All three-celled
motif models were developed in the Matlab software environment. The results of
the studies on the models showed that the three-cell motifs showed short-term
and long-term memory behavior. At the end of the study, short-term and
long-term memory behavior of three-cell motifs were determined.



 

References

  • Arbib, M.A., (2003). “The handbook of brain theory and neural network” Second edition.
  • Bassett, D.S. & Bullmore E., (2006). “Small-world brain networks” Neuroscientist, 512-523. Bassett, D.S. & Bullmore E., (2017). “Small-World Brain Networks Revisited” The Neuroscientist. Vol. 23(5) 499–516 © DOI: 10.1177/1073858416667720 journals.sagepub. com/ home/nro
  • Bower, J.M. & Beeman, D., (1998). “The Book of GENESIS” Second edition. Springer-Verlag, New York
  • Cornelia, I.B. & Eve, M., (2013). “From the connectome to brain function” Nature America.
  • Dayan, P. & Abbott, L.F., (2002). “Theoretical neuroscience” file:///E|/Media_folder/Books/ books.pdox.net/ Physics/Theoretical_Neuroscience/TOC.htm.
  • Dong, C.Y., Lim, J., Nam, Y. & Cho, K.H., (2009). “Systematic analysis of synchronized oscillatory neuronal networks reveals an enrichment for coupled direct and indirect feedback motifs” Bioinformatics, 25, 13, 1680–1685.
  • Feldmeyer, D., Qi, G., Emmenegger, V. & Staiger, J.F., (2018). “Inhibitory Interneurons and their Circuit Motifs in the Many Layers of the Barrel Cortex” Neuroscience 368, 132–151
  • Gal, E., London M., Globerson A., Ramaswamy S., Reimann M.,W., Muller E., Markram H., & Segev I., (2017). “Rich cell-type-specific network topology in neocortical Microcircuitry” doi:10.1038/nn.4576
  • Gerstner, W. & Kistler, W.M., (2002). “Spiking neuron models”. Cambridge University Press.
  • Gorochowski, T.E., Grierson, C.S., Bernardo M., (2018). “Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks” Sci. Adv., eaap9751
  • Heinz, K., & Stefan, H., (2009). “Motifs, algebraic connectivity and computational performance of two data-based cortical circuit templates” International Workshop on Computational Systems Biology.
  • Helmchen, F., Gilad A. & Chen, J.L., (2018). “Neocortical Dynamics During Whisker-Based Sensory Discrimination in Head-Restrained Mice” Neuroscience 368, 57–69
  • Humphries M. D., (2017). “Dynamical networks: Finding, measuring, and tracking neural population activity” Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, Cilt 1 | Sayı 4 | 2017 s.324-338, December 31, 2017
  • Izhikevich, E.M., (2007). “Dynamical systems in neuroscience” The MIT Press Cambridge, London. 16-17 Jackman S.L., Regehr W.G., (2017). “The Mechanisms and Functions of Synaptic Facilitation”. https://doi.org/10.1016/ j.neuron.2017.02.047,Volume 94, Issue 3, Pages 447-464
  • Junker, B.H. & Schreiber, F., (2008). “Analysis of biological networks”
  • Kaiser, T.F. & Peters, F.J., (2009). “Synaptic Plasticity” Nova science publishers, New York.
  • Keener J. & Sneyd, J., (2009). “Mathematical physiology”. Second Edition.
  • Keleş E. & Çepni S. (2006). “Beyin ve Öğrenme”. Journal of Turkish Science.
  • Kim, J.R., Yoon, Y. & Cho, K.H., (2008). “Coupled feedback loops form dynamic motifs of cellular networks” Biophysical Journal 94, 359–365.
  • Li, C., (2008). “Functions of neuronal network motifs” physical reviewe E 78(3 PT 2):037101
  • Milo, R., Shen, O.S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U., (2002). “Network motifs simple building blocks of complex networks” Science. 298, 824-827.
  • Navlakha S., Joseph Z.B. & Barth A.L., (2018). “Network Design and the Brain” https://doi.org/10.1016/ j.tics.2017.09.012 , Volume 22, Issue 1, Pages 64-78
  • Prill, R.J, Iglesias, P.A., & Levchenko, A., (2005). “Dynamic properties of network motifs contribute to biological network organization” Plos Biol.
  • Rodrigo, C.J., G., Jaramillo, A. & Elena S.F., (2009). “Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions” Genome Biology 10:R96 (doi:10.1186/gb-2009-10-9-r96)
  • Schachinger, D., (2003). “Simulation of extracellularly recorded activities from small nerve formations in the brain” Thesis, Wien, Mai.
  • Song, S., Sjöström, P.J., Reigl, M., Nelson, S., & Chklovskii, D.B., (2005) “Highly nonrandom features of synaptic connectivity in local cortical circuits”
  • Sporns, O. & Kotter, R., (2004). “Motifs in Brain Networks”. PLoS Biol.
  • Wang, J., Jianming, G.J., & Fei, X., (2005). “Two-parameters hopf bifurcation in the Hodgkin–Huxley model” 23, 973–980.

Tüm Üç Hücreli Biyolojik Sinir Ağı Motiflerinin Kısa- ve Uzun Dönem Bellek Davranışının İncelenmesi

Year 2019, , 553 - 567, 31.08.2019
https://doi.org/10.18185/erzifbed.425620

Abstract



Bellek ve öğrenme ile ilgili önemli biyolojik
çalışmalar yapılmaktadır. Beyin hücrelerinin, çözelti ve canlı doku
ortamlarında incelenmesi ve ayrıca elektron mikroskopla görüntü çalışmaları,
önemli bulgular ortaya koymuştur.  Elde
edilen biyolojik veriler, beyinde çok sayıda sinir hücresinin, karmaşık
bağlantılar oluşturduğunu göstermiştir. Bu büyük biyolojik sinir ağı
yapısında; tekrarlanan ve motif olarak isimlendirilen bölümler gözlenmiştir.
Bu motiflerin, biyolojik sinir ağlarının temel yapı blokları olduğuna ve ağda
önemli fonksiyonel roller oynadığına inanılmaktadır. Beyindeki bu sinir
ağının bellek davranışını anlamak için, motiflerin davranışlarını bilmek
gerektiği düşünülmektedir. Bu deneysel veriler yardımıyla, motifler hesapsal
yöntemlerle modellenmiştir. Modeller üzerinde yapılan çalışmalardan;
motiflerin biyolojik bellek davranışları incelenmektedir. Bu çalışmada tüm üç
hücreli sinir ağı motiflerinin kısa- ve uzun dönem bellek fonksiyonları
incelendi. Motifler, graph teorisi kullanılarak oluşturuldu. Motifleri
oluşturan sinir hücreleri, Hodkgin-Huxley model kullanılarak, hücrenin soma
kısmında, tek bölme şeklinde modellendi. Ayrıca hücreler arası bağlantı,
kimyasal sinaps şeklinde modellendi. Oluşturulan tüm üç hücreli motif
modelleri, Matlab yazılım ortamında geliştirildi. Modeller üzerinde yapılan
çalışmalarda elde edilen sonuçlardan, üç hücreli motiflerin kısa –ve uzun
dönem bellek davranışı gösterme durumları belirlendi. Çalışmanın sonunda, üç
hücreli motiflerden kısa –ve uzun dönem bellek davranışı sergileyenler tespit
edildi.


 


References

  • Arbib, M.A., (2003). “The handbook of brain theory and neural network” Second edition.
  • Bassett, D.S. & Bullmore E., (2006). “Small-world brain networks” Neuroscientist, 512-523. Bassett, D.S. & Bullmore E., (2017). “Small-World Brain Networks Revisited” The Neuroscientist. Vol. 23(5) 499–516 © DOI: 10.1177/1073858416667720 journals.sagepub. com/ home/nro
  • Bower, J.M. & Beeman, D., (1998). “The Book of GENESIS” Second edition. Springer-Verlag, New York
  • Cornelia, I.B. & Eve, M., (2013). “From the connectome to brain function” Nature America.
  • Dayan, P. & Abbott, L.F., (2002). “Theoretical neuroscience” file:///E|/Media_folder/Books/ books.pdox.net/ Physics/Theoretical_Neuroscience/TOC.htm.
  • Dong, C.Y., Lim, J., Nam, Y. & Cho, K.H., (2009). “Systematic analysis of synchronized oscillatory neuronal networks reveals an enrichment for coupled direct and indirect feedback motifs” Bioinformatics, 25, 13, 1680–1685.
  • Feldmeyer, D., Qi, G., Emmenegger, V. & Staiger, J.F., (2018). “Inhibitory Interneurons and their Circuit Motifs in the Many Layers of the Barrel Cortex” Neuroscience 368, 132–151
  • Gal, E., London M., Globerson A., Ramaswamy S., Reimann M.,W., Muller E., Markram H., & Segev I., (2017). “Rich cell-type-specific network topology in neocortical Microcircuitry” doi:10.1038/nn.4576
  • Gerstner, W. & Kistler, W.M., (2002). “Spiking neuron models”. Cambridge University Press.
  • Gorochowski, T.E., Grierson, C.S., Bernardo M., (2018). “Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks” Sci. Adv., eaap9751
  • Heinz, K., & Stefan, H., (2009). “Motifs, algebraic connectivity and computational performance of two data-based cortical circuit templates” International Workshop on Computational Systems Biology.
  • Helmchen, F., Gilad A. & Chen, J.L., (2018). “Neocortical Dynamics During Whisker-Based Sensory Discrimination in Head-Restrained Mice” Neuroscience 368, 57–69
  • Humphries M. D., (2017). “Dynamical networks: Finding, measuring, and tracking neural population activity” Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, Cilt 1 | Sayı 4 | 2017 s.324-338, December 31, 2017
  • Izhikevich, E.M., (2007). “Dynamical systems in neuroscience” The MIT Press Cambridge, London. 16-17 Jackman S.L., Regehr W.G., (2017). “The Mechanisms and Functions of Synaptic Facilitation”. https://doi.org/10.1016/ j.neuron.2017.02.047,Volume 94, Issue 3, Pages 447-464
  • Junker, B.H. & Schreiber, F., (2008). “Analysis of biological networks”
  • Kaiser, T.F. & Peters, F.J., (2009). “Synaptic Plasticity” Nova science publishers, New York.
  • Keener J. & Sneyd, J., (2009). “Mathematical physiology”. Second Edition.
  • Keleş E. & Çepni S. (2006). “Beyin ve Öğrenme”. Journal of Turkish Science.
  • Kim, J.R., Yoon, Y. & Cho, K.H., (2008). “Coupled feedback loops form dynamic motifs of cellular networks” Biophysical Journal 94, 359–365.
  • Li, C., (2008). “Functions of neuronal network motifs” physical reviewe E 78(3 PT 2):037101
  • Milo, R., Shen, O.S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U., (2002). “Network motifs simple building blocks of complex networks” Science. 298, 824-827.
  • Navlakha S., Joseph Z.B. & Barth A.L., (2018). “Network Design and the Brain” https://doi.org/10.1016/ j.tics.2017.09.012 , Volume 22, Issue 1, Pages 64-78
  • Prill, R.J, Iglesias, P.A., & Levchenko, A., (2005). “Dynamic properties of network motifs contribute to biological network organization” Plos Biol.
  • Rodrigo, C.J., G., Jaramillo, A. & Elena S.F., (2009). “Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions” Genome Biology 10:R96 (doi:10.1186/gb-2009-10-9-r96)
  • Schachinger, D., (2003). “Simulation of extracellularly recorded activities from small nerve formations in the brain” Thesis, Wien, Mai.
  • Song, S., Sjöström, P.J., Reigl, M., Nelson, S., & Chklovskii, D.B., (2005) “Highly nonrandom features of synaptic connectivity in local cortical circuits”
  • Sporns, O. & Kotter, R., (2004). “Motifs in Brain Networks”. PLoS Biol.
  • Wang, J., Jianming, G.J., & Fei, X., (2005). “Two-parameters hopf bifurcation in the Hodgkin–Huxley model” 23, 973–980.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ahmet Turan

Temel Kayıkçıoğlu This is me

Publication Date August 31, 2019
Published in Issue Year 2019

Cite

APA Turan, A., & Kayıkçıoğlu, T. (2019). Tüm Üç Hücreli Biyolojik Sinir Ağı Motiflerinin Kısa- ve Uzun Dönem Bellek Davranışının İncelenmesi. Erzincan University Journal of Science and Technology, 12(2), 553-567. https://doi.org/10.18185/erzifbed.425620