Öğrenme Esnasında Oluşan Dentritik Spine Yapısal Değişiminin Kısa -ve Uzun Dönem Bellek Davranışı Açısından Modellenmesi

Yıl 2018, Cilt: 8 Sayı: 1, 1 - 15, 15.06.2018

Ahmet Turan

Öz

Canlılarda öğrenme ve bellek konusunda yapılan çalışmalar, teknolojik gelişmelerle önemli hız kazanmıştır.
Beyindeki sinir hücrelerinin oluşturduğu büyük ağ ortamında yapılan görüntülemeler hücreler arası
bağlantıların öğrenme sürecinde değişimlerini göstermektedir. Özellikle sinaptik bölgede, dendritik spine
yapısal değişimleri gözlenmektedir. Genç canlı beyninde sıkça görülen bu değişimler, sinaptik uyarı süresince,
yeni dendritik spine’lerin oluşması veya var olanların büyümesi şeklindedir. Sinaptik uyarılara bağlı olarak
oluşan bu değişim, kalıcı ya da önceki durumuna geri dönme şeklinde olmaktadır. Bu durumlar çeşitli
çalışmalarda yapısal ve biyofiziksel olarak yorumlanmış ve modellenmiştir. Çalışmamızda spine yapısal
değişiminin hesapsal yöntemlerle modellenmesi için bir yöntem önerilmiştir. Modelimizde öğrenme esnasında
gerçekleşen dendritik spine yapısal değişimi: Sinaptik iletkenlik fonksiyonunun zaman sabiti parametresi ile
modellenmiştir. Bu noktada dendritik spine’da büyüme, kalıcı olma, küçülme veya yok olma durumlarını
modellemek amacıyla sinaptik iletkenlik zaman sabitine değişen değerler verildi. Dendritik spine büyümesi,
uyarı geldikçe gerçekleşmekte, zaman sabiti değerine bu olayı modellemek için artan değerler verildi. Uyarım
kesildiğinde spine yapısı kalıcı olmuş ise, zaman sabiti değeri de sabit tutuldu. Bu durumda belleği oluşturan
motifler uzun dönem bellek gibi davrandı. Spine tekrar küçülmüşse, zaman sabiti değeri de küçültüldü. Bu
durumda belleği oluşturan motifler, kısa dönem bellek gibi davrandı. Sonuç olarak bizim modelimizde
dendritik spine büyümesinin zaman sabiti değerlerinin değişimiyle modellenebileceği gösterilmiştir.

Anahtar Kelimeler

Sinir Hücresi, Dentritik spine, Sinaptik uyarı, Uzun dönem bellek, Kısa dönem bellek, Hodgkin-Huxley model

Modeling of Dendritic Spine Structural Change During Learning in Terms of Shortand Long-Term Memory Behavior

Öz

Studies on learning and memory in life have gained a significant boost in technological developments. The
images made in the large network environment created by the nerve cells of the brain show the changes in the
learning process of the intercellular connections. Structural changes of the dendritic spine are observed,
especially in the synaptic region. These changes frequently seen in the young living brain are the formation of
new dendritic spines or the growth of existing ones during the synaptic stimulation. This change, which occurs
due to synaptic stimulation, is in the form of a permanent or return to the previous state. These cases are
structured and biophysically interpreted and modeled in various studies. In our work, a method is proposed
for modeling the structural change of spine by computational methods. Structural change of dendritic spine
during learning in our model: It is modeled by the time constant parameter of the synaptic conductivity
function. At this point, the values of the synaptic conductivity time constant are given to model the growth,
persistence, shrinkage or extinction of dendritic spines. Dendritic spine growth occurs as the stimulus arrives,
increasing the time constant value to model this event. If the spine structure was permanent when the
stimulation was interrupted, the time constant value was kept constant. In this case, the motifs that make up the memory behave like long term memory. If the spine has shrunk again, the time constant value has also
shrunk. In this case, the motifs that make up the memory behave like short term memory. As a result, it has
been shown in our model that dendritic spine growth can be modeled by changing the time constants.

Anahtar Kelimeler

Nerve cell, Dentritic spine, Sinaptic stimulation, Long term memory, Short term memory, Hodgkin-Huxley model

Kaynakça

• 1. 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.
• 2. 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
• 3. Bower J M & Beeman D (1998). The Book of GENESIS. Second edition. Springer-verlag, New York
• 4. Chenkov N, Sprekeler H & Kempter R (2017). Memory replay in balanced recurrent networks. PLoS Comput Biol 13(1): e1005359. https://doi.org/10.1371/journal.pcbi.
• 5. 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.
• 6. Gerstner W & Kistler W M (2002). Spiking neuron models. Cambridge University Press.
• 7. Dayan P & Abbott L F (2002). Theoretical neuroscience. file:///E|/Media_folder/Books/ books.pdox.net/ Physics/Theoretical_Neuroscience/TOC.htm.
• 8. 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.
• 9. Izhikevich E M (2007). Dynamical systems in neuroscience. The MIT Press Cambridge, London. 16-17
• 10. Keener J & Sneyd J (2009). Mathematical physiology. Second Edition. Wang J, Jianming G J & Fei X (2005). Two-parameters hopf bifurcation in the Hodgkin–Huxley model. 23, 973–980.
• 11. Schachinger D (2003). Simulation of extracellularly recorded activities from small nerve formations in the brain. Thesis, Wien, Mai.
• 12. Cornelia I B & Eve M (2013). From the connectome to brain function. Nature America.
• 13. 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.
• 14. Elodie B J, Sabrina D & Serge L (2007). Brain plasticity mechanisms and memory. A Party of Four Neuroscientist, 13 492.
• 15. Han Z, Vondriska T M, Yang L, Maclellan W R, Weissa J N ve Qu Z (2007). Signal transduction network motifs and biological memory. Journal of Theoretical Biology 246.755–761.
• 16. Heinz K, & Stefan H (2009). Motifs, algebraic connectivity and computational performance of two data-based cortical circuit templates. International Workshop on Computational Systems Biology.
• 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
• 18. Junker B.H. & Schreiber F (2008). Analysis of biological networks. Kaiser T F & Peters F J (2009). Synaptic Plasticity. Nova science publishers, New York.
• 19. Keleş E & Çepni S (2006). Beyin ve Öğrenme. Journal of Turkish Science.
• 20. Kim J R, Yoon Y & Cho K H (2008). Coupled feedback loops form dynamic motifs of cellular networks. Biophysical Journal 94, 359–365.
• 21. Li C (2008). Functions of neuronal network motifs. physical reviewe E 78(3 PT 2):037101
• 22. Mark M, Steven A S & Eric R K (2012). Synapses and memory storage. Cold Spring Harb Perspect Biol.
• 23. 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.
• 24. Mirisis A A, Alexandrescu A, Carew T J, & Kopec A M (2016). The Contribution of Spatial and Temporal Molecular Networks in the Induction of Long-term Memory and Its Underlying Synaptic Plasticity. Doi:10.3934/Neuroscience.2016.3.356
• 25. 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
• 26. Prill R J, Iglesias P A, & Levchenko A (2005). Dynamic properties of network motifs contribute to biological network organization. Plos Biol.
• 27. Spiegler A, Hansen E, Bernard C, McIntosh A R, & Jirsa V K (2016). Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain, DOI: https://doi.org/10.1523/ENEURO.0068-16.2016
• 28. Song S, Sjöström P J, Reigl M, Nelson S & Chklovskii D B (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol.
• 29. Sporns O & Kotter R (2004). Motifs in Brain Networks. PLoS Biol.
• 30. Borda J T (2004). Electroneurobiologia.
• 31. Keener J & Sneyd J (2009). Mathematical Physiology. Second Edition.
• 32. Knott G W, Holtmaat A, Wilbrecht L, Welker E & Svoboda K (2006). Spine Growth Precedes Synapse Formation in The Adult Neocortex in Vivo. Nature Neuroscience.
• 33. Arbib M A (2003). The Handbook of Brain Theory and Neural Network. Second Edition.
• 34. Roo M D, Klauser P, Garcia P M, Poglia L & Muller D (2008). Spine Dynamics and Synapse Remodeling During LTP and Memory Processes. Progress in Brain Research, 169, Elsevier B.V.
• 35. Knott G W, Holtmaat A, Wilbrecht L, Welker E & Svoboda K (2006). Spine Growth Precedes Synapse Formation in The Adult Neocortex in Vivo. Nature Neuroscience.
• 36. Holtmaat A, Wilbrecht L, Knott G W, Welker E & Karel S (2006). Experience-Dependent and Cell-Type-Specific Spine Growth in the Neocortex. 441-22
• 37. Lamprecht R & Ledoux J (2004). Structural Plasticity and Memory. Center for Neural Science, New York University
• 38. Miermans C A, Kusters R P T, Hoogenraad C C, Storm C (2017). Biophysical model of the role of actin remodeling on dendritic spine morphology. PLoS ONE 12(2): e0170113. doi:10.1371/journal.pone.0170113
• 39. Joensuua M, Lanoueb V, Hotulainena P (2018). Dendritic spine actin cytoskeleton in autism spectrum disorder. Progress in Neuropsychopharmacology & Biological Psychiatry 84 (2018) 362–381, http://dx.doi.org/10.1016/j.pnpbp.2017.08.023
• 40. Lopez P G, Marin V G, & Freire M (2010). Dendritic Spines and Development: Towards a Unifying Model of Spinogenesis - A Present Day Review of Cajal’s Histological Slides and Drawings. Hindawi Publishing Corporation Neural Plasticity. Volume 2010, Article ID 769207, 29 pages doi:10.1155/2010/769207
• 41. Frank A C, Huang S, Zhou M, Gdalyahu A, Kastellakis G, Silva T K, Lu E, Wen X, Poirazi P, Trachtenberg J T & Silva A J (2018). Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory DOI: 10.1038/s41467-017-02751-2| www.nature.com/ naturecommunications,
• 42. Petsophonsakul P, Richetin K, Andraini T, Roybon L & Rampon C (2017). Memory formation orchestrates the wiring of adult-born hippocampal neurons into brain circuits. Springer-Verlag Berlin Heidelberg, DOI 10.1007/s00429-016-1359-x
• 43. Gafarov F M (2018). Neural electrical activity and neural network growth. Neural Networks, https://doi.org/10.1016/j.neunet.2018.02.001 0893-6080
• 44. Dent E W (2017). Of microtubules and memory: implications for microtubule dynamics in dendrites and spines. Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin–Madison, DOI:10.1091/mbc.E15-11-0769, Volume 28
• 45. Bosch M, Castro J, Saneyoshi T, Matsuno H, Sur M, & Hayashi Y (2014). Structural and Molecular Remodeling of Dendritic Spine Substructures during Long-Term Potentiation. Neuron. http://dx.doi.org/10.1016/ j.neuron.2014.03.021
• 46. Ebrahimi S, Okabe S (2014). Structural dynamics of dendritic spines: Molecular composition, geometry and functional regulation. Biochimica et Biophysica Acta 1838 (2014) 2391–2398, http://dx.doi.org/10.1016/j.bbamem.2014.06.002